kelan nyo isubmit yung assignment no. 7 and 8 nyo nasa slides yun ng stats. isubmit nyo sa akin sa lunes during electromagnetism kasi kukulangin yung class participation nyo sa stats.
Math 6 - Solving Problems Involving Algebraic Expressions and Equations.pptxmenchreo
The document provides examples and steps for solving algebraic expressions and equations. It includes examples of simplifying expressions, evaluating expressions given variable values, solving equations using transposition methods, determining if a given variable value makes an equation true, and combining like terms. The examples cover topics like evaluating expressions, solving one-step equations, determining variable values that make statements true, and combining algebraic expressions.
Garfield wakes up and realizes his toy Pooky is missing. Odie left Garfield clues to solve math problems in order to find Pooky's location. Garfield solves problems involving slope, including finding the slope from points on a line and graph, and using the slope-intercept form of a line. After solving all the clues, Garfield retrieves Pooky. Mr. De Guzman then teaches the students about slope and how it describes the steepness of a line.
This document discusses analyzing and summarizing relationships between two quantitative variables (bivariate data) using scatterplots. It covers key topics like correlation, linear regression lines, residuals, outliers and influential points. Scatterplots display the relationship between two variables and can show positive or negative linear associations or no relationship. Correlation coefficients measure the strength and direction of linear relationships, while regression lines predict variable relationships. Residual plots assess linearity and outliers.
Worksheet 9 problems involving the maturity value (f) and present value (p)Rwin Soliman
This document provides 3 problems involving calculations of maturity value (F) and present value (P) for financial instruments:
1) Calculate the interest and maturity value for a P12,000 principal, 3.5% interest rate over 3.5 years.
2) Calculate the principal and maturity value given a 6% interest rate, 9 month term, and P650 interest paid.
3) Calculate the maturity value and interest earnings for a P32,600 deposit earning 4.5% interest over 3 years and 7 months.
The document describes various methods for constructing and interpreting frequency distributions and graphs, including:
1) Constructing a frequency distribution involves deciding on class intervals, calculating class widths and limits, tallying data points, and counting frequencies.
2) Additional metrics like midpoints, relative frequencies, and cumulative frequencies can provide more information about the distribution.
3) Graphs like histograms, frequency polygons, relative frequency histograms, and cumulative frequency graphs visually represent the distribution using bars or lines.
The document discusses operations and composition of functions. It explains that to find the sum of two functions f and g, you add them together and combine like terms. To find the difference, you subtract the second function from the first and distribute negatives. To find the product, you multiply corresponding terms of f and g. For the quotient f/g, you divide the first function by the second. The domain of sums, differences and products is where x is in the domains of both f and g, while the domain of the quotient excludes values where the denominator would be 0. Composition means substituting one function into another, written as f(g) or g(f).
The lesson plan details teaching measures of central tendency of ungrouped data to 7th grade students. The objectives are for students to calculate the mean, median, and mode of data sets with at least 90% proficiency. Examples are provided to demonstrate finding the mean by adding all values and dividing by the number of items, and the median by arranging data from least to greatest and taking the middle value or average of the two middle values for even numbers of items. Students will practice these skills by working through examples of finding the mean, median, and mode of various data sets.
This ppt covers the topic of Abstract Algebra in M.Sc. Mathematics 1 semester topic of Algebraic closed field is Introduction , field & sub field , finite extension field , algebraic element.
Point estimate for a population proportion pMuel Clamor
This document provides information about point estimates for population proportions:
1) A point estimate predicts a parameter with a single number, while an interval estimate provides a range of numbers that could be the true parameter value.
2) The point estimator for a population proportion p is the sample proportion p, which is calculated as the number of successes divided by the sample size n.
3) Two examples are given to demonstrate calculating the point estimate of a population proportion p from sample data on the number of successes.
learning competency 4a. writes expressions with rational exponents as radicalsrina valencia
This document provides instructions on how to write expressions with rational exponents as radicals. It explains that if m/n is a rational number and a is a positive real number, then a^m/n = (nth root of a)^m provided that (nth root of a)^m is a real number. This form of nth root of a^m is called the principal nth root. The document contains examples of rewriting expressions with rational exponents in radical form. It also includes practice problems for students to complete.
This document presents information on multivariate analysis of variance (MANOVA). It discusses when MANOVA is appropriate to use and its advantages over univariate ANOVA. Specifically, it notes that MANOVA considers multiple dependent variables simultaneously and is more powerful than conducting separate univariate tests. The document provides an example of a two-factor mixed MANOVA design investigating the effects of sex and chocolate type on ratings of chocolate taste, crunchiness, and flavor.
Mode of Grouped Data - Math 7 (4th Quarter)Carlo Luna
This document discusses the concept of mode in grouped data. It provides examples of calculating the mode of different data sets. The mode is the value that occurs most frequently in a data set. For grouped data, the mode is calculated using a formula that considers the lower boundary of the modal class, the frequency of the modal class, and the frequencies of neighboring classes. Worked examples demonstrate applying this formula to frequency distributions to determine the modal value.
The document provides steps for dividing out common factors and factoring rational expressions:
1) Identify common factors in the numerator and denominator and divide them out.
2) Factor the numerator and denominator by identifying common factors in each expression.
3) Divide the factored numerator and denominator, simplifying the expression.
This document defines and provides formulas and examples for calculating quartiles and deciles from both ungrouped and grouped data. Quartiles and deciles are statistical measures used to divide a data set into four and ten equal parts, respectively. The document explains that quartiles are calculated as Q1, Q2, Q3 to divide the data into the lower 25%, middle 50%, and upper 25%. Deciles are calculated as D1-D9 to divide the data into ten equal parts. Modified formulas are provided to calculate quartiles and deciles from grouped frequency distribution data. Examples are included to demonstrate calculating these measures.
This document summarizes various statistical measures used to describe and analyze numerical data, including measures of central tendency (mean, median, mode), measures of variation (range, interquartile range, variance, standard deviation, coefficient of variation), and ways to describe the shape of distributions (symmetric vs. skewed using box-and-whisker plots). It provides definitions and formulas for calculating these common statistical concepts.
This document provides information about relations and functions. It defines relations and functions, compares their properties, and gives examples to illustrate the key characteristics and differences between relations and functions. Specifically, it explains that a function is a relation where each input is mapped to exactly one output, while a relation can map the same input to multiple outputs. It provides examples to demonstrate how to determine if a relation represents a function by examining the inputs only. The document also introduces the vertical line test as a way to identify functions from graphs. Finally, it gives examples of real-world applications of functions and their domains and ranges.
- The sample mean is the best estimate of the population mean and can be used to construct confidence intervals to estimate the true population mean.
- There are two situations when estimating a population mean: when the population standard deviation (σ) is known, and when σ is unknown.
- When σ is known, a z-test is used. When σ is unknown, a t-test is used since the sample standard deviation is used to estimate the population standard deviation.
- An angle is formed by two rays or sides with a common endpoint called the vertex. Angles can be named using the vertex and endpoints, just the vertex if unique, or a number.
- Angles are classified as acute (<90°), right (90°), or obtuse (>90° and <180°). A straight angle is 180°.
- Congruent angles have the same measure. Their measures can be indicated with arc marks or the lowercase "m" notation.
In the previous lesson we discussed a measure of location known as the measure of central tendency. There are other measures of location which are useful in describing the distribution of the data set. These measures of location include the maximum, minimum, percentiles, deciles and quartiles. How to compute and interpret these measures are also discussed in this lesson.
Statistics involves collecting, describing, and analyzing data. There are two main areas: descriptive statistics which describes sample data, and inferential statistics which draws conclusions about populations from samples. A population is the entire set being studied, while a sample is a subset of the population. Variables are characteristics being measured, and can be either qualitative (categorical) or quantitative (numerical). Data is collected through experiments or surveys using sampling methods to obtain a representative sample from the population. There is usually variability in data that statistics aims to measure and characterize.
This document outlines the core curriculum for general mathematics for 11th grade students in the Philippines. It covers several key areas of mathematics including functions, rational functions, inverse functions, exponential and logarithmic functions, basic business mathematics concepts like interest and annuities, basic concepts in stocks, bonds and loans, and logic. For each topic, it lists the relevant content and performance standards and provides over 50 specific learning competencies students are expected to master by the end of the course.
Statistics involves collecting, organizing, analyzing, and interpreting data. Descriptive statistics describe characteristics of a data set through measures like central tendency and variability. Inferential statistics draw conclusions about a population based on a sample. Key terms include population, sample, parameter, statistic, data types, levels of measurement, and sampling techniques like simple random sampling. Common data gathering methods are interviews, questionnaires, and registration records. Data can be presented textually, in tables, or graphically through charts, graphs, and maps.
the rectangular coordinate system and midpoint formulas, linear equations in two variable, slope of a line, equation of a line, applications of linear equations and graphing
This document contains a semi-detailed lesson plan for a Grade 8 math class. The lesson plan aims to teach students about the arithmetic mean of ungrouped data. Students will gather data about daily allowances from their classmates. They will then compute the mean of this ungrouped data. Additionally, students will collect height and weight measurements to calculate other means. The lesson plan evaluates students' performance on recent tests in science, math, and English, and assigns community-based homework involving interviews about occupations and salaries.
Grade 10_Math-Lesson 2-3 Graphs of Polynomial Functions .pptxErlenaMirador1
The document discusses how to graph polynomial functions by determining:
1) The end behavior using the leading coefficient test
2) The maximum number of turning points from the degree of the polynomial
3) The x-intercepts by finding the zeros of the polynomial
4) The y-intercept by evaluating the polynomial at x=0
It provides examples of using these steps to graph various polynomial functions of degrees 1-5.
This document provides a teaching guide for a Statistics and Probability course for senior high school students. It begins with an introduction that discusses the importance of statistics and data analysis. It then describes the structure and contents of the teaching guide, which is designed to help teachers facilitate student learning and understanding. The guide is aligned with both DepEd's functional skills and CHED's college readiness standards to prepare students for future education and work. It also provides context about statistics as a discipline and how data analysis has grown in importance.
This document provides an introduction to random variables. It defines random variables as functions that assign real numbers to outcomes of an experiment. Random variables can be either discrete or continuous depending on whether their possible values are countable or uncountable. The document also defines probability mass functions (pmf) which describe the probabilities of discrete random variables taking on particular values. Expectation is introduced as a way to summarize random variables using a single number by taking a weighted average of all possible outcomes.
This ppt covers the topic of Abstract Algebra in M.Sc. Mathematics 1 semester topic of Algebraic closed field is Introduction , field & sub field , finite extension field , algebraic element.
Point estimate for a population proportion pMuel Clamor
This document provides information about point estimates for population proportions:
1) A point estimate predicts a parameter with a single number, while an interval estimate provides a range of numbers that could be the true parameter value.
2) The point estimator for a population proportion p is the sample proportion p, which is calculated as the number of successes divided by the sample size n.
3) Two examples are given to demonstrate calculating the point estimate of a population proportion p from sample data on the number of successes.
learning competency 4a. writes expressions with rational exponents as radicalsrina valencia
This document provides instructions on how to write expressions with rational exponents as radicals. It explains that if m/n is a rational number and a is a positive real number, then a^m/n = (nth root of a)^m provided that (nth root of a)^m is a real number. This form of nth root of a^m is called the principal nth root. The document contains examples of rewriting expressions with rational exponents in radical form. It also includes practice problems for students to complete.
This document presents information on multivariate analysis of variance (MANOVA). It discusses when MANOVA is appropriate to use and its advantages over univariate ANOVA. Specifically, it notes that MANOVA considers multiple dependent variables simultaneously and is more powerful than conducting separate univariate tests. The document provides an example of a two-factor mixed MANOVA design investigating the effects of sex and chocolate type on ratings of chocolate taste, crunchiness, and flavor.
Mode of Grouped Data - Math 7 (4th Quarter)Carlo Luna
This document discusses the concept of mode in grouped data. It provides examples of calculating the mode of different data sets. The mode is the value that occurs most frequently in a data set. For grouped data, the mode is calculated using a formula that considers the lower boundary of the modal class, the frequency of the modal class, and the frequencies of neighboring classes. Worked examples demonstrate applying this formula to frequency distributions to determine the modal value.
The document provides steps for dividing out common factors and factoring rational expressions:
1) Identify common factors in the numerator and denominator and divide them out.
2) Factor the numerator and denominator by identifying common factors in each expression.
3) Divide the factored numerator and denominator, simplifying the expression.
This document defines and provides formulas and examples for calculating quartiles and deciles from both ungrouped and grouped data. Quartiles and deciles are statistical measures used to divide a data set into four and ten equal parts, respectively. The document explains that quartiles are calculated as Q1, Q2, Q3 to divide the data into the lower 25%, middle 50%, and upper 25%. Deciles are calculated as D1-D9 to divide the data into ten equal parts. Modified formulas are provided to calculate quartiles and deciles from grouped frequency distribution data. Examples are included to demonstrate calculating these measures.
This document summarizes various statistical measures used to describe and analyze numerical data, including measures of central tendency (mean, median, mode), measures of variation (range, interquartile range, variance, standard deviation, coefficient of variation), and ways to describe the shape of distributions (symmetric vs. skewed using box-and-whisker plots). It provides definitions and formulas for calculating these common statistical concepts.
This document provides information about relations and functions. It defines relations and functions, compares their properties, and gives examples to illustrate the key characteristics and differences between relations and functions. Specifically, it explains that a function is a relation where each input is mapped to exactly one output, while a relation can map the same input to multiple outputs. It provides examples to demonstrate how to determine if a relation represents a function by examining the inputs only. The document also introduces the vertical line test as a way to identify functions from graphs. Finally, it gives examples of real-world applications of functions and their domains and ranges.
- The sample mean is the best estimate of the population mean and can be used to construct confidence intervals to estimate the true population mean.
- There are two situations when estimating a population mean: when the population standard deviation (σ) is known, and when σ is unknown.
- When σ is known, a z-test is used. When σ is unknown, a t-test is used since the sample standard deviation is used to estimate the population standard deviation.
- An angle is formed by two rays or sides with a common endpoint called the vertex. Angles can be named using the vertex and endpoints, just the vertex if unique, or a number.
- Angles are classified as acute (<90°), right (90°), or obtuse (>90° and <180°). A straight angle is 180°.
- Congruent angles have the same measure. Their measures can be indicated with arc marks or the lowercase "m" notation.
In the previous lesson we discussed a measure of location known as the measure of central tendency. There are other measures of location which are useful in describing the distribution of the data set. These measures of location include the maximum, minimum, percentiles, deciles and quartiles. How to compute and interpret these measures are also discussed in this lesson.
Statistics involves collecting, describing, and analyzing data. There are two main areas: descriptive statistics which describes sample data, and inferential statistics which draws conclusions about populations from samples. A population is the entire set being studied, while a sample is a subset of the population. Variables are characteristics being measured, and can be either qualitative (categorical) or quantitative (numerical). Data is collected through experiments or surveys using sampling methods to obtain a representative sample from the population. There is usually variability in data that statistics aims to measure and characterize.
This document outlines the core curriculum for general mathematics for 11th grade students in the Philippines. It covers several key areas of mathematics including functions, rational functions, inverse functions, exponential and logarithmic functions, basic business mathematics concepts like interest and annuities, basic concepts in stocks, bonds and loans, and logic. For each topic, it lists the relevant content and performance standards and provides over 50 specific learning competencies students are expected to master by the end of the course.
Statistics involves collecting, organizing, analyzing, and interpreting data. Descriptive statistics describe characteristics of a data set through measures like central tendency and variability. Inferential statistics draw conclusions about a population based on a sample. Key terms include population, sample, parameter, statistic, data types, levels of measurement, and sampling techniques like simple random sampling. Common data gathering methods are interviews, questionnaires, and registration records. Data can be presented textually, in tables, or graphically through charts, graphs, and maps.
the rectangular coordinate system and midpoint formulas, linear equations in two variable, slope of a line, equation of a line, applications of linear equations and graphing
This document contains a semi-detailed lesson plan for a Grade 8 math class. The lesson plan aims to teach students about the arithmetic mean of ungrouped data. Students will gather data about daily allowances from their classmates. They will then compute the mean of this ungrouped data. Additionally, students will collect height and weight measurements to calculate other means. The lesson plan evaluates students' performance on recent tests in science, math, and English, and assigns community-based homework involving interviews about occupations and salaries.
Grade 10_Math-Lesson 2-3 Graphs of Polynomial Functions .pptxErlenaMirador1
The document discusses how to graph polynomial functions by determining:
1) The end behavior using the leading coefficient test
2) The maximum number of turning points from the degree of the polynomial
3) The x-intercepts by finding the zeros of the polynomial
4) The y-intercept by evaluating the polynomial at x=0
It provides examples of using these steps to graph various polynomial functions of degrees 1-5.
This document provides a teaching guide for a Statistics and Probability course for senior high school students. It begins with an introduction that discusses the importance of statistics and data analysis. It then describes the structure and contents of the teaching guide, which is designed to help teachers facilitate student learning and understanding. The guide is aligned with both DepEd's functional skills and CHED's college readiness standards to prepare students for future education and work. It also provides context about statistics as a discipline and how data analysis has grown in importance.
This document provides an introduction to random variables. It defines random variables as functions that assign real numbers to outcomes of an experiment. Random variables can be either discrete or continuous depending on whether their possible values are countable or uncountable. The document also defines probability mass functions (pmf) which describe the probabilities of discrete random variables taking on particular values. Expectation is introduced as a way to summarize random variables using a single number by taking a weighted average of all possible outcomes.
The document shows the steps to calculate the mean of a probability distribution. A table lists the possible values (X) of a random variable, their respective probabilities (P(x)), and the product of each x and P(x). These products are summed to obtain 1.7, which is equal to the mean (μ) of the probability distribution.
This document discusses several discrete probability distributions:
1. Binomial distribution - For experiments with a fixed number of trials, two possible outcomes, and constant probability of success. The probability of x successes is given by the binomial formula.
2. Geometric distribution - For experiments repeated until the first success. The probability of the first success on the xth trial is p(1-p)^(x-1).
3. Poisson distribution - For counting the number of rare, independent events occurring in an interval. The probability of x events is (e^-μ μ^x)/x!, where μ is the mean number of events.
This document defines key concepts related to random variables including:
- A random variable is a numerical measure of outcomes from a random phenomenon.
- Probability distributions describe the probabilities associated with random variables.
- Expected value refers to the mean or weighted average of a probability distribution.
- As the number of trials increases, the actual mean approaches the true mean due to the Law of Large Numbers.
- Binomial and geometric distributions model situations with success/failure outcomes and independence between trials.
This document provides a teaching guide for a Statistics and Probability course for senior high school students. It begins with an introduction that discusses the importance of statistics and data analysis. It then outlines the structure and goals of the teaching guide, which includes sections on introduction, instruction, practice, enrichment, and evaluation. The guide is meant to help teachers facilitate student understanding, mastery of concepts, and a sense of ownership over their learning. It also discusses aligning the guide with DepEd and CHED standards to prepare students for college. The preface provides additional context on statistics as a discipline and its growing importance.
This document provides an outline for a Probability and Statistics course. It covers topics such as introduction to statistics, tabular and graphical representation of data, measures of central tendency and variation, probability, discrete and continuous distributions, and hypothesis testing. Descriptive statistics are used to summarize and describe data, while inferential statistics allow predictions and inferences about a larger data set based on a sample. Variables can be classified based on their scale of measurement as nominal, ordinal, interval, or ratio. Graphical representations include pie charts, histograms, bar graphs, and frequency polygons. Measures of central tendency include the mean, median, and mode.
This document provides an outline for a course on probability and statistics. It begins with an introduction to statistics, including definitions and general uses. It then discusses topics that will be covered, such as measures of central tendency, probability, discrete and continuous distributions, and hypothesis testing. References for textbooks are also provided. The document differentiates between descriptive and inferential statistics. It defines key statistical concepts such as population, sample, variable, and the different variable types. It also covers the different scales of measurement for variables. An assignment is included asking students to list statisticians' contributions, give a real-life application example, and define independent and dependent variables.
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This document provides an overview of key concepts in statistics including:
- Descriptive statistics which summarize and describe data, and inferential statistics which make predictions based on samples.
- The difference between a population, which is all subjects being studied, and a sample, which is a subset selected from the population.
- Types of variables like qualitative, quantitative, discrete, continuous and different measurement scales.
- Various data collection methods like random, systematic, stratified and cluster sampling.
- The two main types of statistical studies - observational which observes phenomena, and experimental which manipulates variables.
- Common statistical terms like independent and dependent variables and misuses of statistics to mislead.
This document discusses descriptive and inferential statistics used in nursing research. It defines key statistical concepts like levels of measurement, measures of central tendency, descriptive versus inferential statistics, and commonly used statistical tests. Nominal, ordinal, interval and ratio are the four levels of measurement, with ratio allowing the most data manipulation. Descriptive statistics describe sample data while inferential statistics allow estimating population parameters and testing hypotheses. Common descriptive statistics include mean, median and mode, while common inferential tests are t-tests, ANOVA, chi-square and correlation. Type I errors incorrectly reject the null hypothesis.
1. The document discusses quantitative research methods, including comparing groups, examining relationships between variables, different types of data and levels of measurement, sampling techniques, and common statistical tools.
2. Key statistical tools covered include t-tests, ANOVA, correlation analysis, chi-square tests, and non-parametric equivalents for comparing groups and examining relationships.
3. The purpose of quantitative research is to systematically investigate phenomena through collecting and analyzing numerical data.
Statistics is the science of collecting, organizing, summarizing, analyzing, and interpreting data. It involves gathering data through various methods, presenting data in tables, analyzing the data by applying statistical principles to identify patterns and relationships, and interpreting the results. Key concepts in statistics include measures of central tendency (mean, median, mode), measures of dispersion (range, standard deviation), frequency distributions, and different types of variables (continuous, discrete, nominal, ordinal, interval, ratio). Statistics is used for precise description of data, predicting behavior, and testing hypotheses.
This document provides an introduction and definitions related to key concepts in statistics. It discusses what statistics is as the science of collecting, organizing, and interpreting data. It defines important statistical terms like data, variables, statistics, and parameters. It also outlines the two main branches of statistics as descriptive statistics, which focuses on summarizing and presenting data, and inferential statistics, which analyzes samples to make inferences about populations. Finally, it discusses common sources of data like published sources, experiments, and surveys.
This document discusses descriptive and inferential statistics. Descriptive statistics involves collecting, organizing and summarizing data, while inferential statistics involves generalizing from samples to populations and making predictions. Examples of descriptive statistics include mean sales and discount rates, while examples of inferential statistics include predicting life satisfaction and the relationship between awareness and resiliency. The document also covers variables, levels of measurement, and types of data.
This document provides an introduction to descriptive statistics. It defines key statistical concepts such as population, sample, variables, and measurements. It explains different types of variables, including qualitative and quantitative variables. It also describes different levels of measurement for variables, including nominal, ordinal, interval, and ratio scales. The document then discusses topics such as sampling methods, data collection techniques, and ways to organize and present data, including through tables, graphs, and textual descriptions.
The document contains an outline of the table of contents for a textbook on general statistics. It covers topics such as preliminary concepts, data collection and presentation, measures of central tendency, measures of dispersion and skewness, and permutations and combinations. Sample chapters discuss introduction to statistics, variables and data, methods of presenting data through tables, graphs and diagrams, computing the mean, median and mode, and other statistical measures.
This document provides an introduction to statistics, including defining what statistics is, the different types of variables and scales of measurement, and why statistics is important in dentistry. It discusses how statistics can be used for research, understanding medical literature, and informing clinical decision making. Descriptive statistics are used to summarize and describe data, while inferential statistics allow generalizing beyond the sample data to the overall population. Nominal, ordinal, interval, and ratio scales of measurement are explained along with examples. The importance of understanding the scale of measurement is that it determines which statistical tests can appropriately be used for analysis.
This document provides an introduction to statistics, including defining what statistics is, the different types of variables and scales of measurement, and why statistics is important in dentistry. It discusses how statistics can be used for research, understanding medical literature, and informing clinical decision making. Descriptive statistics are used to summarize and describe data, while inferential statistics allow generalizing beyond the sample data to the overall population. Nominal, ordinal, interval, and ratio scales of measurement are explained along with examples. The importance of understanding the scale of measurement is that it determines which statistical tests can appropriately be used for analysis.
This document provides an introduction to statistics, including defining what statistics is, the different types of variables and scales of measurement, and why statistics is important in dentistry. It discusses how statistics can be used for research, understanding medical literature, and informing clinical decision making. Descriptive statistics are used to summarize and describe data, while inferential statistics allow generalizing beyond the sample data to the overall population. Nominal scales name categories, ordinal scales rank order items, interval scales have equal intervals but an arbitrary zero point, and ratio scales have a true zero point where the absence of a trait can be measured.
This document summarizes key concepts from an introduction to statistics textbook. It covers types of data (quantitative, qualitative, levels of measurement), sampling (population, sample, randomization), experimental design (observational studies, experiments, controlling variables), and potential misuses of statistics (bad samples, misleading graphs, distorted percentages). The goal is to illustrate how common sense is needed to properly interpret data and statistics.
This document provides an outline for a course on electromagnetism, electricity, and digital electronics. The course covers topics such as the theory of electrons and electricity, resistors, Ohm's law, circuits, magnetism, diodes, logic gates, combinational and sequential circuits. References for the course include textbooks on digital design, electronic devices, engineering circuit analysis, and introductions to electric circuits and digital circuits. The document also provides details on some of the topics, including the theory of electrons, insulators/conductors/semiconductors, direct and alternating current, voltage, current, resistance, and Ohm's law.
This document provides an outline for a course on electromagnetism, electricity, and digital electronics. It covers topics such as the theory of electrons and atoms, resistors, circuits, magnetism, diodes, logic gates, and combinational and sequential circuits. References provided include textbooks on digital design, electronic devices, engineering circuit analysis, and introductions to electric circuits and digital circuits. The document also includes sections on electron theory, atomic structure, conductors and insulators, sources of electricity, alternating and direct current, voltage, current and resistance, and Ohm's law.
This document provides an outline for a course on probability and statistics. It begins with an introduction to statistics, including definitions and general uses. It then covers various topics that will be taught, such as measures of central tendency, probability, discrete and continuous distributions, and hypothesis testing. References for textbooks are also provided. The document contains sample assignments and examples to illustrate concepts like scales of measurement, data collection methods, and graphical representations of data. It provides instructions for calculating measures of central tendency and examples of frequency distributions and their related graphs.
Electricity and magnetism are closely related phenomena. Electricity is the flow of electric charge, usually electrons, while magnetism is produced by moving electric charges such as electric currents. When an electric current passes through a wire, it produces a magnetic field around the wire. This allows electromagnets to be created by wrapping wire around an iron core and passing a current through it. The interaction between electric currents and magnetic fields also allows electric motors and generators to operate by electromagnetic induction.
This document provides an outline for a course on probability and statistics. It includes an introduction to key statistical concepts like measures of central tendency, dispersion, correlation, probability distributions, and hypothesis testing. Assignments are provided to help students apply these statistical methods to real-world examples from various fields like business, engineering, and the biological sciences. References for further reading on topics in statistics and probability are also listed.
This document provides an outline for a course on probability and statistics. It begins with an introduction to key concepts like measures of central tendency, dispersion, correlation, and probability distributions. It then lists common probability distributions and hypothesis testing. The document provides examples of how statistics is used in various fields. It also defines key statistical concepts like population and sample, variables, and different scales of measurement. Finally, it discusses data collection methods and ways to represent data through tables and graphs.
This document provides an outline for a course on electromagnetism, electricity, and digital electronics. The course covers topics such as the theory of electrons and electricity, resistors, Ohm's law, electric circuits, theory of magnetism, diodes, logic gates, and combinational and sequential circuits. It lists textbooks that will be used as references. The document also provides detailed explanations of concepts in atomic structure, electricity, circuits, electromagnetism, and electronics.
This document outlines the topics that will be covered in a physics course, including units and measurements, vectors, motion, forces, energy, momentum, heat transfer, and properties of matter. It also lists the requirements of a midterm physicist report and endterm group exhibit assessing physics applications. Course policies address proper classroom attitude and restricting electronic gadgets.
The document contains definitions of force, friction, inertia, and gravity. It then presents a series of true/false questions that test understanding of these core concepts of motion and forces. The questions provide examples of how these scientific principles apply in everyday situations like mowing the lawn, mixing a cake, or riding a roller coaster.
The document discusses concepts related to motion including distance vs displacement, speed vs velocity, acceleration, kinematics formulas, and graphing position, velocity, and acceleration over time. It provides examples and explanations of these physics concepts as well as tips for graphing motion.
This document provides an outline for a course on electromagnetism, electricity, and digital electronics. It covers topics such as the theory of electrons and electricity, resistors, Ohm's law, electric circuits, theory of magnetism, diodes, logic gates, and combinational and sequential circuits. It lists textbooks that will be used and provides examples and exercises to help teach the concepts.
This document provides an outline for a course on probability and statistics. It begins with an introduction to statistics, including definitions and general uses. It then covers topics like measures of central tendency, probability, discrete and continuous distributions, and hypothesis testing. References for textbooks on statistics and counterexamples in probability are also provided. Assignments ask students to list contributors to statistics, apply statistics in real life, define independent and dependent variables, and understand scales of measurement. Methods of data collection, tabular and graphical representation of data, and measures of central tendency and location are also discussed.
This document provides an outline for a course on probability and statistics. It begins with an introduction to key concepts like measures of central tendency, dispersion, correlation, and probability distributions. It then lists common probability distributions and the textbook and references used. Later sections define important statistical terms like population, sample, variable types, data collection methods, and ways of presenting data through tables and graphs. It provides examples of how statistics is used and ends with examples of different variable scales.
This document provides an outline for a course on probability and statistics. It begins with an introduction to key concepts like measures of central tendency, dispersion, correlation, and probability distributions. It then lists common probability distributions and the textbook and references used. Later sections define important statistical terms like population, sample, variable types, data collection methods, and ways of presenting data through tables and graphs. It provides examples of each variable scale and ends with assignments for students.
This document provides an outline for a course on electromagnetism, electricity, and digital electronics. It covers topics such as the theory of electrons and electricity, resistors, Ohm's law, electric circuits, the theory of magnetism, diodes, logic gates, and flip-flops. It lists several textbooks that will be used as references. It then delves into some of the topics in more detail, including the structure of atoms, types of insulators and conductors, direct and alternating current, voltage, current, resistance, and Ohm's law. It also discusses magnetism, electromagnetism, and provides examples of devices that use magnets.
This document provides information on numerical concepts in physics such as:
1) Determining the number of significant figures in measurements and calculations.
2) Converting numbers between normal and scientific notation.
3) Using unit analysis to convert between different units of measurement like centimeters to meters.
It defines key terms like significant figures and scientific notation. It also provides examples of operations using significant figures and scientific notation, as well as guidelines for unit conversions between the metric system and other units.
Physics is the basic science that studies matter and energy. It is a systematically organized body of knowledge based on observable facts about nature. Physics includes several main branches that study different aspects of matter and energy, such as mechanics, thermodynamics, waves, electricity and magnetism, and modern physics. The scientific method is used in physics to make hypotheses, conduct experiments, and develop theories to build understanding of natural phenomena.
2. COURSE OUTLINE Introduction to Statistics Tabular and Graphical representation of Data Measures of Central Tendencies, Locations and Variations Measure of Dispersion and Correlation Probability and Combinatorics Discrete and Continuous Distributions Hypothesis Testing
3. Text and References Statistics: a simplified approach by Punsalan and Uriarte, 1998, Rex Texbook Probability and Statistics by Johnson, 2008, Wiley Counterexamples in Probability and Statistics by Romano and Siegel, 1986, Chapman and Hall
4. Introduction to Statistics Definition In its plural sense, statistics is a set of numerical data e.g. Vital statistics, monthly sales, exchange rates, etc. In its singular sense, statistics is a branch of science that deals with the collection, presentation, analysis and interpretation of data.
5. General uses of Statistics Aids in decision making by providing comparison of data, explains action that has taken place, justify a claim or assertion, predicts future outcome and estimates un known quantities Summarizes data for public use
6. Examples on the role of Statistics In Biological and medical sciences, it helps researchers discover relationship worthy of further attention. Ex. A doctor can use statistics to determine to what extent is an increase in blood pressure dependent upon age - In social sciences, it guides researchers and helps them support theories and models that cannot stand on rationale alone. Ex. Empirical studies are using statistics to obtain socio-economic profile of the middle class to form new socio-political theories.
7. Con’t In business, a company can use statistics to forecast sales, design products, and produce goods more efficiently. Ex. A pharmaceutical company can apply statistical procedures to find out if the new formula is indeed more effective than the one being used. In Engineering, it can be used to test properties of various materials, Ex. A quality controller can use statistics to estimate the average lifetime of the products produced by their current equipment.
8. Fields of Statistics Statistical Methods of Applied Statistics: Descriptive-comprise those methods concerned with the collection, description, and analysis of a set of data without drawing conclusions or inferences about a larger set. Inferential-comprise those methods concerned with making predictions or inferences about a larger set of data using only the information gathered from a subset of this larger set.
9. con’t b. Statistical theory of mathematical statistics- deals with the development and exposition of theories that serve as a basis of statistical methods
10. Descriptive VS Inferential DESCRIPTIVE A bowler wants to find his bowling average for the past 12 months A housewife wants to determine the average weekly amount she spent on groceries in the past 3 months A politician wants to know the exact number of votes he receives in the last election INFERENTIAL A bowler wants to estimate his chance of winning a game based on his current season averages and the average of his opponents. A housewife would like to predict based on last year’s grocery bills, the average weekly amount she will spend on groceries for this year. A politician would like to estimate based on opinion polls, his chance for winning in the upcoming election.
11. Population as Differrentiated from Sample The word population refers to groups or aggregates of people, animals, objects, materials, happenings or things of any form, this means that there are populations of students, teachers, supervisors, principals, laboratory animals, trees, manufactured articles, birds and many others. If your interest is on few members of the population to represent their characteristics or traits, these members constitute a sample. The measures of the population are called parameters, while those of the sample are called estimates or statistics.
12. The Variable It refers to a characteristic or property whereby the members of the group or set vary or differ from one another. However, a constant refers to a property whereby the members of the group do not differ one another. Variables can be according to functional relationship which is classified as independent and dependent. If you treat variable y as a function of variable z, then z is your independent variable and y is your dependent variable. This means that the value of y, say academic achievement depends on the value of z.
13. Con’t Variables according to continuity of values. 1. Continuous variable – these are variables whose levels can take continuous values. Examples are height, weight, length and width. 2. Discrete variables – these are variables whose values or levels can not take the form of a decimal. An example is the size of a particular family.
14. Con’t Variables according to scale of measurements: 1. Nominal – this refers to a property of the members of a group defined by an operation which allows making of statements only of equality or difference. For example, individuals can be classified according to thier sex or skin color. Color is an example of nominal variable.
15. Con’t 2. Ordinal – it is defined by an operation whereby members of a particular group are ranked. In this operation, we can state that one member is greater or less that the others in a criterion rather than saying that he/it is only equal or different from the others such as what is meant by the nominal variable. 3. Interval – this refers to a property defined by an operation which permits making statement of equality of intervals rather than just statement of sameness of difference and greater than or less than. An interval variable does not have a “true” zero point.; althought for convenience, a zero point may be assigned.
16. Con’t 4. Ratio – is defined by the operation which permits making statements of equality of ratios in addition to statements of sameness or difference, greater than or less than and equality or inequality of differences. This means that one level or value may be thought of or said as double, triple or five times another and so on.
17. Assignment no. 1 Make a list of at least 5 mathematician or scientist that contributes in the field of statistics. State their contributions With your knowledge of statistics, give a real life situation how statistics is applied. Expand your answer. When can a variable be considered independent and dependent? Give an example for your answer.
18. Con’t IV. Enumerate some uses of statistics. Do you think that any science will develop without test of the hypothesis? Why?
19. Examples of Scales of Measurement 1.Nominal Level Ex. Sex: M-Male F-Female Marital Status: 1-single 2- married 3- widowed 4- separated 2. Ordinal Level Ex. Teaching Ratings: 1-poor 2-fair 3- good 4- excellent
20. Con’t 3. Interval Level Ex. IQ, temperature 4. Ratio Level Ex. Age, no. of correct answers in exam
21. Data Collection Methods Survey Method – questions are asked to obtain information, either through self administered questionnaire or personal interview. Observation Method – makes possible the recording of behavior but only at the time of occurrence (ex. Traffic count, reactions to a particular stimulus)
22. Con’t 3. Experimental method – a method designed for collecting data under controlled conditions. An experiment is an operation where there is actual human interference with the conditions that can affect the variable under study. 4. Use of existing studies – that is census, health statistics, weather reports. 5. Registration method – that is car registration, student registration, hospital admission and ticket sales.
23. Tabular Representation Frequency Distribution is defined as the arrangement of the gathered data by categories plus their corresponding frequencies and class marks or midpoint. It has a class frequency containing the number of observations belonging to a class interval. Its class interval contain a grouping defined by the limits called the lower and the upper limit. Between these limits are called class boundaries.
24. Frequency of a Nominal Data Male and Female College students Major in Chemistry SEX FREQUENCY MALE 23 FEMALE 107 TOTAL 130
25. Frequency of Ordinal Data Ex. Frequency distribution of Employee Perception on the Behavior of their Administrators Perception Frequency Strongly favorable 10 favorable 11 Slightly favorable 12 Slightly unfavorable 14 Unfavorable 22 Strongly unfavorable 31 total 100
26. Frequency Distribution Table Definition: Raw data – is the set of data in its original form Array – an arrangement of observations according to their magnitude, wither in increasing or decreasing order. Advantages: easier to detect the smallest and largest value and easy to find the measures of position
27. Grouped Frequency of Interval Data Given the following raw scores in Algebra Examination, 56 42 28 56 41 56 55 59 50 55 57 38 62 52 66 65 33 34 37 47 42 68 62 54 68 48 56 39 77 80 62 71 57 52 60 70
28. Con’t Compute the range: R = H – L and the number of classes by K = 1 + 3.322log n where n = number of observations. Divide the range by 10 to 15 to determine the acceptable size of the interval. Hint: most frequency distribution have odd numbers as the size of the interval. The advantage is that the midpoints of the intervals will be whole number. Organize the class interval. See to it that the lowest interval begins with a number that is multiple of the interval size.
29. Con’t 4. Tally each score to the category of class interval it belongs to. 5. Count the tally columns and summarizes it under column (f). Then add the frequency which is the total number of the cases (N). 6. Determine the class boundaries. UCB and LCB.(upper and lower class boundary) 7. Compute the midpoint for each class interval and put it in the column (M). M = (LS + HS) / 2
30. Con’t 8. Compute the cumulative distribution for less than and greater than and put them in column cf< and cf>. (you can now interpret the data). cf = cumulative frequency 9. Compute the relative frequency distribution. This can be obtained by RF% = CF/TF x 100% CF = CLASS FREQUENCY TF = TOTAL FREQUENCY
31. Graphical Representation The data can be graphically presented according to their scale or level of measurements. 1. Pie chart or circle graph. The pie chart at the right is the enrollment from elementary to master’s degree of a certain university. The total population is 4350 students
32. Con’t 2. Histogram or bar graph- this graphical representation can be used in nominal, ordinal or interval. For nominal bar graph, the bars are far apart rather than connected since the categories are not continuous. For ordinal and interval data, the bars should be joined to emphasize the degree of differences
33. Given the bar graph of how students rate their library. A-strongly favorable, 90 B-favorable, 48 C-slightly favorable, 88 D-slightly unfavorable, 48 E-unfavorable, 15 F-strongly unfavorable, 25
34. The Histogram of Person’s Age with Frequency of Travel age freq RF 19-20 20 39.2% 21-22 21 41.2% 23-24 4 7.8% 25-26 4 7.8% 27-28 2 3.9% total 51 100%
35. Exercises From the previous grouped data on algebra scores, Draw its histogram using the frequency in the y axis and midpoints in the x axis. Draw the line graph or frequency polygon using frequency in the y axis and midpoints in the x axis. Draw the less than and greater than ogives of the data. Ogives is a cumulation of frequencies by class intervals. Let the y axis be the CF> and x axis be LCB while y axis be CF< and x axis be UCB
36. Con’t d. Plot the relative frequency using the y axis as the relative frequency in percent value while in the x axis the midpoints.
41. Con’t Construct the class interval, frequency table, class midpoint(use a whole number midpoint), less than and greater than cumulative frequency, upper and lower boundary and relative frequency. Plot the histogram, frequency polygon, and ogives
42. Con’t 3. Draw the pie chart and bar graph of the plans of computer science students with respect to attending a seminar. Compute for the Relative frequency of each. A-will not attend=45 B-probably will not attend=30 C-probably will attend=40 D-will attend=25
43. Measures of Centrality and Location Mean for Ungrouped Data X’ = Σ X / N where X’ = the mean Σ X = the sum of all scores/data N = the total number of cases Mean for Grouped Data X’ = Σ fM / N where X’ = the mean M = the midpoint fM = the product of the frequency and each midpoint N = total number of cases
44. Con’t Ex. Find the mean of 10, 20, 25,30, 30, 35, 40 and 50. Given the grades of 50 students in a statistics class Class interval f 10-14 4 15-19 3 20-24 12 25-29 10 30-34 6 35-39 6 40-44 6 45-49 3
45. Con’t The weighted mean. The weighted arithmetic mean of given groups of data is the average of the means of all groups WX’ = Σ Xw / N where WX’ = the weighted mean w = the weight of X Σ Xw = the sum of the weight of X’s N = Σ w = the sum of the weight of X
46. Con’t Ex. Find the weighted mean of four groups of means below: Group, i 1 2 3 4 X i 60 50 70 75 W i 10 20 40 50
47. Con’t Median for Ungrouped Data The median of ungrouped data is the centermost scores in a distribution. Mdn = (X N/2 + X (N + 2)/2 ) / 2 if N is even Mdn = X (1+N)/2 if N is odd Ex. Find the median of the following sets of score: Score A: 12, 15, 19, 21, 6, 4, 2 Score B: 18, 22, 31, 12, 3, 9, 11, 8
48. Con’t Median for Grouped Data Procedure: Compute the cumulative frequency less than. Find N/2 Locate the class interval in which the middle class falls, and determine the exact limit of this interval. Apply the formula Mdn = L + [(N/2 – F)i]/fm where L = exact lower limit interval containing the median class F = The sum of all frequencies preceeding L. fm = Frequency of interval containing the median class i = class interval N = total number of cases
49. Con’t Ex. Find the median of the given frequency table. class interval f cf< 25-29 3 3 30-34 5 8 35-39 10 18 40-44 15 33 45-49 15 48 50-54 15 63 55-59 21 84 60-64 8 92 65-69 6 98 70-74 2 100
50. Con’t Mode of Ungrouped Data It is defined as the data value or specific score which has the highest frequency. Find the mode of the following data. Data A : 10, 11, 13, 15, 17, 20 Data B: 2, 3, 4, 4, 5, 7, 8, 10 Data C: 3.5, 4.8, 5.5, 6.2, 6.2, 6.2, 7.3, 7.3, 7.3, 8.8
51. Mode of Grouped Data For grouped data, the mode is defined as the midpoint of the interval containing the largest number of cases. Mdo = L + [d 1 /(d 1 + d 2 )]i where L = exact lower limit interval containing the modal class. d 1 = the difference of the modal class and the frequency of the interval preceding the modal class d 2 = the difference of the modal class and the frequency of the interval after the modal class.
52. Ex. Find the mode of the given frequency table. class interval f cf< 25-29 3 3 30-34 5 8 35-39 10 18 40-44 15 33 45-49 15 48 50-54 15 63 55-59 21 84 60-64 8 92 65-69 6 98 70-74 2 100
53. Exercises Determine the mean, median and mode of the age of 15 students in a certain class. 15, 18, 17, 16, 19, 18, 23 , 24, 18, 16, 17, 20, 21, 19 2. To qualify for scholarship, a student should have garnered an average score of 2.25. determine if the a certain student is qualified for a scholarship.
54. Subject no. of units grade A 1 2.0 B 2 3.0 C 3 1.5 D 3 1.25 E 5 2.0
55. Find the mean, median and mode of the given grouped data. Classes f 11-22 2 23-34 8 35-46 11 47-58 19 59-70 14 71-82 5 83-94 1
56. Quartiles refer to the values that divide the distribution into four equal parts. There are 3 quartiles represented by Q 1 , Q 2 and Q 3 . The value Q 1 refers to the value in the distribution that falls on the first one fourth of the distribution arranged in magnitude. In the case of Q 2 or the second quartile, this value corresponds to the median. In the case of third quartile or Q 3 , this value corresponds to three fourths of the distribution.
57.
58. For grouped data, the computing formula of the kth quartile where k = 1,2,3,4,… is given by Q k = L + [(kn/4 - F)/fm]Ii Where L = lower class boundary of the kth quartile class F = cumulative frequency before the kth quartile class fm = frequency before the kth quartile i = size of the class interval
59. Exercises Compute the value of the first and third quartile of the given data class interval f cf< 25-29 3 3 30-34 5 8 35-39 10 18 40-44 15 33 45-49 15 48 50-54 15 63 55-59 21 84 60-64 8 92 65-69 6 98 70-74 2 100
60. Decile: If the given data is divided into ten equal parts, then we have nine points of division known as deciles. It is denoted by D 1 , D 2 , D 3 , D 4 …and D 9 D k = L + [(kn/10 – F)/fm] I Where k = 1,2,3,4 …9
61. Exercises Compute the value of the third, fifth and seventh decile of the given data class interval f cf< 25-29 3 3 30-34 5 8 35-39 10 18 40-44 15 33 45-49 15 48 50-54 15 63 55-59 21 84 60-64 8 92 65-69 6 98 70-74 2 100
62. Percentile- refer to those values that divide a distribution into one hundred equal parts. There are 99 percentiles represented by P 1 , P 2 , P 3 , P 4 , P 5 , …and P 99 . when we say 55 th percentile we are referring to that value at or below 55/100 th of the data. P k = L + [(kn/100 – F)/fm]i Where k = 1,2,3,4,5,…99
63. Exercises Compute the value of the 30 th , 55 th , 68 th and 88 th percentile of the given data class interval f cf< 25-29 3 3 30-34 5 8 35-39 10 18 40-44 15 33 45-49 15 48 50-54 15 63 55-59 21 84 60-64 8 92 65-69 6 98 70-74 2 100
64. Assignment no. 3 The rate per hour in pesos of 12 employees of a certain company were taken and are shown below. 44.75, 44.75, 38.15, 39.25, 18.00, 15.75, 44.75, 39.25, 18.50, 65.25, 71.25, 77.50 Find the mean, median and mode. If the value 15.75 was incorrectly written as 45.75, what measure of central tendency will be affected? Support your answer.
65. II. The final grades of a student in six subjects were tabulated below. Subj units final grade Algebra 3 60 Religion 2 90 English 3 75 Pilipino 3 86 PE 1 98 History 3 70 Determine the weighted mean If the subjects were of equal number of units, what would be his average?
66. III. The ages of qualified voters in a certain barangay were taken and are shown below Class Interval Frequency 18-23 20 24-29 25 30-35 40 36-41 52 42-47 30 48-53 21 54-59 12 60-65 6 66-71 4 72-77 1
67. Find the mean, median and mode Find the 1 st and 3 rd quantile Find the 4 th and 6 th decile Find the 25 th and 75 th percentile
68. Measure of Variation The range is considered to be the simplest form of measure of variation. It is the difference between the highest and the lowest value in the distribution. R = H – L For grouped data, the3 difference between the highest upper class boundary and the lowest lower class boundary. Example: find the range of the given grouped data in slide no. 59
69. Semi-inter Quartile Range This value is obtained by getting one half of the difference between the third and the first quartile. Q = (Q 3 – Q 1 )/2 Example: Find the semin-interquartile range of the previous example in slide no. 59
70. Average Deviation The average deviation refers to the arithmetic mean of the absolute deviations of the values from the mean of the distribution. This measure is sometimes known as the mean absolute deviation. AD = Σ│ x – x’ │ / n Where x = the individual values x’ = mean of the distribution
71. Steps in solving for AD Arrange the values in column according to magnitude Compute for the value of the mean x’ Determine the deviations (x – x’) Convert the deviations in step 3 into positive deviations. Use the absolute value sign. Get the sum of the absolute deviations in step 4 Divide the sum in step 5 by n.
72. Example: Consider the following values: 16, 13, 9, 6, 15, 7, 11, 12 Find the average deviation.
73. For grouped data: AD = Σ f│x – x’│ / n Where f = frequency of each class x = midpoint of each class x’ = mean of the distribution n = total number of frequency
74. Example: Find the average deviation of the given data Classes f 11-22 2 23-34 8 35-46 11 47-58 19 59-70 14 71-82 5 83-94 1
75. Variance For ungrouped data s 2 = Σ (x – x’) 2 / n Example: Find the variance of 16, 13, 9, 6, 15, 7, 11, 12
76. For grouped data s 2 = Σ f(x – x’) 2 / n Where f = frequency of each class x = midpoint of each class interval x’ = mean of the distribution n = total number of frequency
77. Example: Find the variance of the given data Classes f 11-22 2 23-34 8 35-46 11 47-58 19 59-70 14 71-82 5 83-94 1
78. Coefficient of variation If you wish to compare the variability between different sets of scores or data, coefficient of variation would be very useful measure for interval scale data CV = s/x Where s = standard deviation x = the mean
79. Example: In a particular university, a researcher wishes to compare the variation in scores of the urban students with that of the scores of the rural students in their college entrance test. It is know that the urban student’s mean score is 384 with a standard deviation of 101; while among the rural students, the mean is 174, with a standard deviation of 53, which group shows more variation in scores?
80. Standard Deviation s = √s 2 For ungrouped data s = √ Σ (x – x’) 2 / n For grouped data s = √ Σ f(x – x’) 2 / n
81. Find the standard deviation of the previous examples for ungrouped and grouped data. Find the standard deviation of the given data Classes f 11-22 2 23-34 8 35-46 11 47-58 19 59-70 14 71-82 5 83-94 1
83. Measure of variation for nominal data VR = 1 – fm/N Where VR = the variation ratio fm = modal class frequency N = counting of observation
84. Example: With the data given by a clinical psychologist on the type of therapy used, compute the variation ratios. Type of therapy no. of patients YR 1980 YR 1985 Logotherapy 20 8 Reality Therapy 60 105 Rational Therapy 42 6 Transactional analysis 39 9 Family therapy 52 5 Others 41 8
85. Assignment no. 4 I. Compute for the semi-interquartile range, absolute deviation, variance and standard deviation test III of assignment no. 3. II. Compute for the semi-interquartile range, absolute deviation, variance and standard deviation of test I of assignment no. 3.
86. SIMPLE LINEAR REGRESSION AND MEASURES OF CORRELATION In this topic, you will learn how to predict the value of one dependent variable from the corresponding given value of the independent variable.
87. The scatter diagram: In solving problems that concern estimation and forecasting, a scatter diagram can be used as a graphical approach. This technique consist of joining the points corresponding to the paired scores of dependent and independent variables which are commonly represented by X and Y on the X-Y coordinate system.
88. Example: The working experience and income of 8 employees are given below Employee years of income experience (in Thousands) X Y A 2 8 B 8 10 C 4 11 D 11 15 E 5 9 F 13 17 G 4 8 H 15 14
89. Using the Least Squares Linear Regression Equation: Y = a + bX Where b = [n Σ xy – Σ x Σ y] / [n Σ x 2 – ( Σ x) 2 ] a = y’ – bx’ Obtain the equation of the given data and estimate the income of an employee if the number of years experience is 20 years.
90. Standard Error of Estimate Se = √ [ Σ Y i 2 – a(Y i ) – b(X i Y i )] / n-2 The standard error of estimate is interpreted as the standard deviation. We will find that the same value of X will always fall between the upper and lower 3Se limits.
91. Measures of Correlation The degree of relationship between variables is expressed into: Perfect correlation (positive or negative) Some degree of correlation (positive or negative) No correlation
92. For a perfect correlation, it is either positive or negative represented by +1 and -1. correlation coefficients, positive or negative, is represented by +0.01 to +0.99 and -0.01 to -0.99. The no correlation is represented by 0.
93. 0 to +0.25 very small positive correlation +0.26 to +0.50 moderately small positive correlation +0.51 to +0.75 high positive correlation +0.76 to +0.99 very high positive correlation +1.00 perfect positive correlation ---------------------------------------------------------- 0 to -0.25 very small negative correlation -0.26 to -0.50 moderately small positive correlation -0.51 to -0.75 high negative correlation -0.76 to -0.99 very high negative correlation -1.00 perfect negative correlation
94. Anybody who wants to interpret the results of the coefficient of correlation should be guided by the following reminders: The relationship of two variables does no necessarily mean that one is the cause of the effect of the other variable. It does not imply cause-effect relationship. When the computed Pearson r is high, it does not necessarily mean that one factor is strongly dependent on the other. On the other hand, when the computed Pearson r is small it does not necessarily mean that one factor has no dependence on the other. If there is a reason to believe that the two variables are related and the computed Pearson r is high, these two variables are really meant as associated. On the other hand, if the variables correlated are low, other factors might be responsible for such small association. Lastly, the meaning of correlation coefficient just simply informs us that when two variables change there may be a strong or weak relationship taking place.
95. The formula for finding the Pearson r is [n Σ XY – Σ X Σ Y] r = ------------------------------ √ [n Σ X 2 – ( Σ X) 2 ] [n Σ Y 2 – ( Σ Y) 2 ]
96. Example: Given two sets of scores. Find the Pearson r and interpret the result. X Y 18 10 16 14 14 14 13 12 12 10 10 8 10 5 8 6 6 12 3 0
97. Correlation between Ordinal Data This is the Spearman Rank-Order Correlation Coefficient (Spearman Rho). For cases of 30 or less, Spearman ρ is the most widely used of the rank correlation method. 6 Σ D 2 ρ = 1 - ----------- n(n 2 – 1) Where D = (RX – RY)
98. Example: Individual Test X Test Y 1 18 24 2 17 28 3 14 30 4 13 26 5 12 22 6 10 18 7 8 15 8 8 12
99. Gamma Rank Order An alternative to the rank order correlation is the Goodman’s and Kruskal’s Gamma (G). The value of one variable can be estimated or predicted from the other variable when you have the knowledge of their values. The gamma can also be used when ties are found in the ranking of the data.
100. N S - N 1 G = ----------------- N S + N 1 Where N S = the number of pairs ordered in the parallel direction N 1 = the number of pairs ordered in the opposite direction
101. Given a segment of the Filipino Electorate according to religion and political party LAKAS LP NP Total Catholic 50 25 20 INC 34 72 21 Born Again 22 12 10 Total
102. Correlation between Nominal Data The Guttman’s Coefficient of predictability is the proportionate reduction in error measure which shows the index of how much an error is reduced in predicting values of one variable from the value of another. Σ FBR - MBC λ c = ------------------ N – MBC Where FBR = the biggest cell frequencies in the ith row MBC = the biggest column totals N = total observations
103. Σ FBC - MBR λ r = ------------------- N – MBR Where FBC = the biggest cell frequencies in the column MBR = the biggest of the row totals N = total number of observations Compute for the λ c and λ r for the segment of Filipino electorate and political parties.
104. Assignment no. 5 Given the average yearly cost and sales of company A for a period of 8 years. Find the pearson r and interpret the results. Year Cost Sales per P10,000 per P10,000 15 38 30 53.3 16 60 39 72 20 40 36 47.5 45 82 10 21.5
105. Given the grades of 10 students in statistics determine the spearman rho and interpret the result Student Q1 Q2 A 62 57 B 90 88 C 75 90 D 60 67 E 58 60 F 89 79 G 91 78 H 90 62 I 94 86 J 50 55
106. 3. Compute for the gamma shown and interpret the result Socio-economic status EDUCATIONAL STATUS TOTAL UPPER MIDDLE LOWER TOTAL UPPER 24 19 5 MIDDLE 12 54 29 LOWER 9 26 25 TOTAL
108. Counting Techniques Consider the numbers 1,2,3 and 4. suppose you want to determine the total 2 digit numbers that can be formed if these are combined. First, let us assume that no digit is to be repeated. 12 21 31 41 23 32 42 24 34 43 Notice that we were able to used all the possibilities. In this example, we have 12 possible 2 digit numbers.
109. Now, what if the digits can be repeated? 12 13 14 22 23 24 23 33 34 42 43 44 Hence, we have 16 possible outcomes. In the first activity, we can do it in n 1 ways and after it has been done, the second activity can be done in n 2 ways, then the total number of ways in which the two activities can be done is equal to n 1 n 2 .
110. Example: How many two digit numbers can be formed from the numbers 1,2,3 and 4 if Repetition is not allowed? Repetition is allowed? 2. How many three digit numbers can be formed from the digits 1,2,3,4 and 5 if any of the digits can be repeated? 3. The club members are going to elect their officers. If there are 5 candidates for president, 5 candidates for vice president and 3 for secretary, then how many ways can the officers be elected?
111. An office executive plans to buy as laptop in which there are 5 brands available. Each of the brands has 3 models and each model has 5 colors to chose from. In how many ways can the executive choose? Consider the numbers 2,3 5 and 7. if repetition is not allowed, how many three digit numbers can be formed such that They are all odd? They are all even? They are greater that 500?
112. 6. A pizza place offers 3 choices of salad, 20 kinds of pizza and 4 different deserts. How many different 3 course meals can one order? 7. The executive of a certain company is consist of 5 males and 2 females. How many ways can the presidents and secretary be chosen if The president must be female and the secretary must be male? The president and the secretary are of opposite sex? The president and the secretary should be male?
113. Permutation The term permutation refers to the arrangement of objects with reference to order. P(n,r) = n! / (n – r)! Evaluate: P(10,6) P(5,5) P(4,3) + P(4,4)
114. Examples: In how many ways can a president, a vice president, a secretary and a treasurer be elected from a class with 40 students? In how many ways can 7 individuals be seated in a row of 7 chairs? In how many ways can 9 individuals be seated in a row of 9 chairs if two individuals wanted to be seated side by side?
115. 4. Suppose 5 different math books and 7 different physics books shall be arranged in a shelf. In how many ways can such books be arranged if the books of the same subject be placed side by side? Determine the possible permutations of the word MISSISSIPPI. Find the total 8 digit numbers that can be formed using all the digits in the following numerals 55777115
116. In how many ways can 6 persons be seated around a table with 6 chairs if two individuals wanted to be seated side by side? In a local election, there are 7 people running for 3 positions. In how many ways can this be done?
117. Combination A combination is an arrangement of objects not in particular order. nCr = C(n,r) = n! / r!(n-r)! Evaluate: 8 C 4 5( 5 C 4 – 5 C 2 ) 7 C 5 / ( 7 C 6 – 7 C 2 )
118. A class is consist of 12 boys and 10 girls. In how many ways can the class elect the president, vice president, secretary and a treasurer? In how many ways can the class elect 4 members of a certain committee? In how many ways can a student answer 6 out of ten questions? In how many ways can a student answer 6 out of 10 questions if he is required to answer 2 of the first 5 questions?
119. In how many ways can 3 balls be drawn from a box containing 8 red and 6 green balls? A box contain 8 red and 6 green balls. In how many ways can 3 balls be drawn such that They are all green? 2 is red and 1 is green? 1 is red and 2 is green?
120. A shipment of 40 computers are unloaded from the van and tested. 6 of them are defective. In how many ways can we select a set of 5 computers and get at least one defective? Five letters a,b,c,d,e are to be chosen. In how many ways could you choose None of them At least two of them At most three of them
121. Assignment no. 6 How many possible outcomes are there if A die is rolled? A pair of dice is rolled? 2. In how many ways can 5 math teachers be assigned to 4 available subjects if each of the 5 teachers have equal chance of being assigned to any of the 4 subjects?
122. 3. Consider the numbers 1,2,3,5,and 6. how many 3 digit numbers can be formed from these numbers if Repetition is not allowed and 0 should not be in the first digit? Repetition is allowed and 0 should not be in the first digit? 4. A college has 3 entrance gates and 2 exit gates. In how many ways can a student enter then leave the building?
123. In how many ways can 9 passengers be seated in a bus if there are only 5 seats available? In how many ways can 4 boys and 4 girls be seated in a row of 8 chairs if They can sit anywhere? The boys and girls are to be seated alternately? 7. In how many ways can ten participants in a race placed first, second and third?
124. Determine the number of distinct permutations of each of the following: STATISTICS ADRENALIN 44044999404 A class consist of 12 boys and 10 girls. In how many ways can a committee of five be formed if All members are boys? 2 are boys and 3 are girls?
125. 10. In how many ways can a student answer an exam if out of the 6 problem, he is required to answer only 4?
126. Probability In the study of probability, we shall consider activities for which the outcomes cannot be predicted with certainty. These activities, called experiment , could always result in a single outcome. Although the single outcome can not be predicted before the performance of the experiment, the set of all possible outcomes can be determined. This set of all possible outcomes is referred to as sample space . Each individual element or outcome in a sample space is known as a sample point .
127. Definition of terms: Random experiment- any process of generating a set of data or observations that can be repeated under basically the same conditions, which lead to well defined outcomes. Sample space – set of all possible outcomes of an experiment, usually denoted by S. Sample point- an element of the sample space or outcomes.
128. event- any subset of the sample space usually denoted by capital letters. Null space- a subset of the sample space that contains no elements and denoted by the symbol Ø. Simple event – an event which contains only one element of the sample space. Compound event – an event that can be expressed as the union of the simple events, thus containing more than one sample points. Mutually exclusive events- two events A and B are mutually exclusive if A∩B have no elements in common.
129. The probability of a event A denoted by P(A) is the sum of the probabilities of mutually exclusive outcomes that constitute the event. It must satisfy the following properties: 0 ≤ P(A) ≤ 1
130. Example: 1. Consider the activity of rolling a die. This activity has 6 possible outcomes, that is 1,2,3,4,5 and 6. thus, S = {1,2,3,4,5,6} Any numbers 1 to 6 is a sample point of S. we can say that there are 6 sample points. If we let A be the event of getting an even number and B an event of getting a perfect square, then A = {2,4,6} and B = {1,4} Note that the elements of A are elements of the sample space S. the number of sample points in a sample space S, events A and B are usually written as n(S) = 6, n(A) = 3 and n(B) = 2.
131. If a pair of dice is rolled, then determine the number of sample points of the following: Sample space Event of getting a sum of 5. Event of getting a sum of at most 4. A box contains 6 red and 4 green balls. If three balls are drawn from the box, then determine the number of sample points of the following: The sample space The event of getting all green balls The event of getting 1 red and 2 green balls.
132. Probability is the chance that an event will happen. The probability of an event A denoted by P(A) refers to the number between 0 and 1 including the values of 0 and 1. This number can be expressed as a fraction, as a decimal or as a percent. When we assign a probability of 0 to event A, it means that it is impossible for event A to occur. When event A is assigned a probability of 1, then we say that event A will really occur.
133. P(A) + P(A)’ = 1 The probability of occurrence plus the probability of non-occurrence is always equal to 1. Example: A student in a statistics class was able to compute the probability of passing the subject to be equal to 0.55. Based on this information, what is the probability that he is not going to pass the subject?
134. Three approaches of probability: Subjective probability- it is determine by the use of intuition, personal beliefs and other indirect information. A posteriori or probability of relative frequency (empirical probability) – it is determined by repeating the experiment a large number of times using the following rule: no. of times event A occurred P(A) = --------------------------------------------------- no. of times experiment was repeated
135. Example: Records show that 120 out of 500 students who entered in a CS/IT programs leave the school due to financial problems. What is the probability that a freshman entering this college will leave the school due to financial problem?
136. 2. Last year, the efficiency rating of the employees of a certain company were taken and presented in a frequency distribution below: Efficiency rating no. of employees 60-65 12 66-71 10 72-77 31 78-83 29 84-89 8 Based on the data, what can we say about the proportion of employees for this year who shall have an efficiency rating from 72-77 and 84-89?
137. A Priori or classical probability – it is determined even before the experiment is performed using the following rule: n(A) P(A) = -------- n(S) Where n(A) = no. of sample points in event A n(S) = no. of sample points in sample space S.
138. If a coin is tossed , what is the probability of getting a head? If two coins are tossed, what is the probability of getting both heads? If a die is rolled, what is the probability of getting an odd number? An even number? A perfect square? If a pair of dice is rolled, what is the probability of getting a sum of 6? A sum of 13?
139. 5. The probability that a college student without a flu shot will get the flu is 0.42.what is the probability that a college student without the flu shot will not get the flu? 6. A box contains 7 red and 6 green balls. If 2 balls are drawn from the box, what is the probability of getting both green? 1 red and 1 green?
140. Addition Rule: In practice, the probability of two or more events are usually considered. If we let A and B be events then these two events can be combined to form another event. The event that at least one of the events A or B will happen is denoted by AUB. The event that both events A and B will occur is denoted by A∩B. The probability of AUB denoted by P(AUB) is given by P(AUB) = P(A) + P(B) – P(A∩B)
141. Two events A and B are said to be mutually exclusive if they can not occur both at the same time. This implies that the occurrence of event A excludes the occurrence of event B and vice versa. Therefore, P(A∩B) has no sample point which is equal to 0. The previous equation will be P(AUB) = P(A) + P(B)
142. Consider rolling a die and the events of getting an odd number, an even number and a perfect square. Determine the probability of getting An odd or an even number. An even number or a perfect square. (this implies that the two events can occur both at the same time. Therefore the two events are non-mutually exclusive events)
143. 2. A card is drawn from an ordinary deck of 52 playing cards. Find the probability of getting An ace or a queen A queen or a face card A black card or a queen
144. 3. You are going to rolled a pair of dice. Find the probability of getting the sum that is even or the sum that is multiple of 3. 4. A student goes to the library and checks out that 40% are work of fiction, 30% are non fiction and 20% are either fiction or non-fiction. What is the probability that the student check out a work of fiction, non-fiction or both?
145. 5 . The probability that Anita will buy machine A is 7/11 and the probability that she will buy machine B is 5/11. If the probability of buying either machine A and B is 9/11, what is the probability of buying the two machine?
146. 6. A community swim team has 150 members. Seventy-five of the members are advanced swimmers. Forty-seven of the members are intermediate swimmers. The remainder are novice swimmers. Forty of the advanced swimmers practice 4 times a week. Thirty of the intermediate swimmers practice 4 times a week. Ten of the novice swimmers practice 4 times a week. Suppose one member of the swim team is randomly chosen. Answer the questions (Verify the answers):
147. What is the probability that the member is a novice swimmer? What is the probability that a member practice 4 times a week? What is the probability that the member is an advanced swimmer and practice 4 times a week? What is the probability that a member is an advance swimmer and an intermediate swimmer? Are they mutually exclusive?
148. SEATWORK 1. A BOX CONTAINS 7 RED, 3 GREEN AND 2 YELLOW BALLS. IF ONE BALL IS DRAWN FROM THE BOX, THEN WHAT IS THE PROBABILITY OF GETTING A RED? A NON-RED? A NON-GREEN? 2. SUPPOSE THAT WE ROLL A DICE, WHAT IS THE PROBABILITY OF GETTING A SUM OF 6 OR 8? 3. SUPPOSE WE PICK ONE CARD FROM A DECK OF CARDS, WHAT IS THE PROBABILITY OF GETTING A KING OR A SPADE? A KING OR NUMBER 8? 4. KLAUS IS TRYING TO CHOOSE WHERE TO GO ON VACATION. HIS CHOICES ARE A=BAGIUO AND B=TAGAYTAY. HE CAN ONLY AFFORD ONE VACATION. THE PROBABILITY OF CHOOSING A IS 0.36 AND THE PROBABILITY OF CHOOSING B IS 0.44. WHAT IS THE PROBABILITY THAT HE CHOOSES TO GO EITHER A OR B? WHAT IS THE PROBABILITY THAT HE WILL NOT CHOOSE ANY OF THE TWO DISTINATION?
149. Conditional Probability and Multiplication Rule It is the probability that a second event will occur if the first event already happened. Symbolically, conditional probability is written as P(A/B) and is read as the probability of event A given that B has occurred. The computing formula for the conditional probability of A given B is given by P(A/B) = P(A ∩ B)/P(B), provided P(B) is not equal to zero.
150. Let P(A) = 0.55 P(B) = 0.35 P(A ∩B) = 0.20 Find P(A/B) and P(B/A) 2 . A die is rolled. If the result is an even number, what is the probability that it is a perfect square? 3. A card is drawn from a deck of 52 cards. Given that the card drawn is a face card, then what is the probability of getting a king? A spade? A red card?
151. 4 . A vendor has 35 balloons on strings. 20 balloons are yellow, 8 are red and 7 are green. A balloon was selected at random and sold. Given that the balloon selected and sold is yellow, what is the probability that the next balloon selected and sold at random is also yellow? 5. Given that 25 microwaves are on display in a certain store but 2 of them are defective. A customer wishes to buy 2 microwaves and pick them up without replacement. Find the probability that the two are defective.
152. 6. Should women participate in combat? yes no Male 32 18 Female 8 42 Find the probability that the respondent answered YES given that the respondent was a female. Find the probability that the respondent was a male given that the respondent answered NO.
153. 7. A box contains 3 red and 8 black balls. If two balls are drawn in succession without replacement, what is the probability that Both are red? The first ball is red and the second ball is black? 8. A box contains 3 red and 8 black balls. If 2 balls are drawn at random with replacement, what is the probability that both are red?
154. Assignment no. 7 1.. A BOX CONTAINS 7 RED, 3 GREEN AND 2 YELLOW BALLS. IF ONE BALL IS DRAWN FROM THE BOX, THEN WHAT IS THE PROBABILITY OF GETTING A RED? A NON-RED? A NON-GREEN? 2. SUPPOSE THAT WE ROLL A DICE, WHAT IS THE PROBABILITY OF GETTING A SUM OF 6 OR 8? 3. SUPPOSE WE PICK ONE CARD FROM A DECK OF CARDS, WHAT IS THE PROBABILITY OF GETTING A KING OR A SPADE? A KING OR NUMBER 8? 4. KLAUS IS TRYING TO CHOOSE WHERE TO GO ON VACATION. HIS CHOICES ARE A=BAGIUO AND B=TAGAYTAY. HE CAN ONLY AFFORD ONE VACATION. THE PROBABILITY OF CHOOSING A IS 0.36 AND THE PROBABILITY OF CHOOSING B IS 0.44. WHAT IS THE PROBABILITY THAT HE CHOOSES TO GO EITHER A OR B? WHAT IS THE PROBABILITY THAT HE WILL NOT CHOOSE ANY OF THE TWO DISTINATION?
155. 5. The probability that it is Friday and that a student is absent is 0.03. Since there are 5 school days in a week, the probability that it is Friday is 0.2. What is the probability that a student is absent given that today is Friday?
156. Normal Distribution The normal probability curve is one of the most commonly used theoretical distributions in statistical inference. The mathematical equation of the normal curve was developed by De Moivre in 1773. the distribution is sometimes called the Gaussian distribution in honor of Gauss, who also derived the equation in the 19 th century.
157. Con’t A large population investigated in education and the behavioral sciences has characteristics that follow a normal distribution. If we are to study, for instance, the scholastic mental capacity of a school population N= 1500, we may find that majority of the student population will yield average scores, a small portion will yield above and below average scores and a few students will yield extremely high and low scores.
158. Con’t The characteristics of the Normal Curve is 1. The curve is symmetrical and bell shaped. It has its highest point at the center. The lines at both sides fall off toward the opposite directions at exactly equal distance from the center. Therefore if the curve is folded at the middle, the two sides are perfectly of the same size and shape.
159. Con’t 2. The number of cases, N, is infinite. This is the reason why the curve is asymptotic to the baseline which means that the curve at both sides does not touch the baseline or the axis, and that the curve may extend infinitely to both directions. 3. The three measures of central tendency, mean, median and mode coincide at one point at the center of the distribution.
160. Con’t 4. The height of the curve indicate the frequency of cases, expressed as probability, proportion or percentage. Hence, the total area under the normal curve is 1.0 in terms of probability or proportion and 100% in terms of percentage. Thus one half of the area is 50% 5. The basic unit of measurement is expressed in sigma units ( σ ) or standard deviations along the baseline. It is also called Z-scores.
161. Con’t 6. Two parameters are used to describe the curve. One is the parameter mean( μ or x’) which is equal to zero and the other is the standard deviation( σ ) which is equal to 1. 7. Standard deviations or A scores departing away from the mean ( μ or x’) towards the right of the curve is in positive while scores departing from the mean is in negative values.
163. From the previous curve We can say that, At least 68% of the values in the given set of data fall within plus or minus 1 standard deviation from the mean. In symbols, the interval is given by (x’ – 1 σ ) – (x’ + 1 σ ). At least 95% of the value in the given set of data fall within plus or minus 2 standard deviation from the mean. In symbol, the interval is (x’ – 2 σ ) – (x’ + 2 σ ) and so on.
164. To illustrate the significance of the empirical rule, consider the NCEE scores of students in a certain college whose mean score x’ or μ = 65 and the standard deviation σ or SD = 6 approximately, 68% of the students in that college have NCEE scores between 80 plus or minus 10, that is 65 – (1)(6) – 65 + (1)(6) 59 - 71
165. The Standard Score The standard score Z represents a normal distribution with mean x’ = 0 and SD = 1. such transformation can be obtained by using the formula below. Z = (x – x’) / SD
166. Normal Curve Areas The total area under the normal curve is equal to 1. since a normally distributed set of data is symmetric, then the total area from Z = 0 to the right is equal to 0.5. the area from Z = 0 to the left is also equal to 0.5. Example: Find the area under the curve from 0 <Z<1.25 -1.25<Z<0
167. Normal Probability Distribution Find the probability value of P(Z >1.45) P(Z<-0.4) P(-0.4<Z<1.45) P(1.15<Z<2.33) P(Z<1.28) P(Z>-1.04)
168. Con’t 7. The examination results of a large group of students in statistics are normally distributed with a mean of 40 and a standard deviation of 4. If a student is chosen at random, what is the probability that his score is Below 30? Above 55? Below 42? Between 35 to 45? Between 33 to 50?
169. Con’t The efficiency rating of 400 faculty members of a certain university were taken and resulted in a mean rating of 78 with a standard deviation of 6.75. assuming that the set of data are normally distributed, how many of the faculty members have an efficiency rating of Greater than 78? Less that 78? Greater than 85? Between 75-90?
170. Assignment no. 8 Find the area under the following condition Between the -2.02 and 1.01 To the right of 1.62 To the left of 0.56 Between 0.65 and 1.18 Between -2.09 and -0.78 II. In a reading ability test, with a sample of 120 cases, the mean score is 50 and the standard deviation is 5.5.
171. Con’t a. What percentage of the cases falls between the mean and a score of 55? b. What is the probability that a score picked at random will lie above the score of 55? c. What is the probability that a score will lie below 40? d. How many cases fall between 55 to 60? e. How many cases fall between 40 to 49?