This presentation includes an introduction to statistics, introduction to sampling methods, collection of data, classification and tabulation, frequency distribution, graphs and measures of central tendency.
This document discusses linear functions and how to represent them using equations, graphs, and tables of values. It defines a linear function as one that can be written in the form f(x) = mx + b, where m is the slope and b is the y-intercept. Examples are provided to illustrate determining the slope and y-intercept from an equation and representing a linear function using an equation, table of values, or graph. It is explained that a linear function will produce a straight line on a graph and have constant differences in x- and y-coordinates in its table of values.
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The document discusses the nature and standards of the teaching profession. It states that teaching facilitates learning and uses knowledge and skills to meet students' educational needs. Teaching emphasizes developing values, social skills, and self-concept in students. To be a profession, teaching requires specialized knowledge, benefits society, involves cooperation through organizations, and demands continuous learning. Teachers are professionals who are responsible for instruction, assessing student learning, and maintaining a safe environment for learning. The document also outlines social and professional standards for teachers, including being role models, having good relationships, and demonstrating commitment to teaching.
Sulfonylureas are oral hypoglycemic drugs that enhance insulin secretion from the pancreas. They work by blocking ATP-sensitive potassium channels in pancreatic beta cells, which leads to insulin release. Common side effects include hypoglycemia and weight gain. Examples include glibenclamide, glipizide, and glimepiride. Choice of sulfonylurea depends on factors like duration of action, renal function, and patient age. They are generally effective treatments for type 2 diabetes but require caution in elderly patients or those with kidney/liver problems.
Introduction to Statistics - Basic concepts
- How to be a good doctor - A step in Health promotion
- By Ibrahim A. Abdelhaleem - Zagazig Medical Research Society (ZMRS)
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.
The document discusses quartiles, which divide a data set into four equal parts. The first quartile contains the smallest 25% of values, the second quartile contains values between the 25th and 50th percentiles, the third quartile contains values between the 50th and 75th percentiles, and the fourth quartile contains the largest 25% of values. Formulas are provided for calculating the lower quartile (Q1), median (Q2), and upper quartile (Q3). The quartile deviation is defined as half the distance between Q3 and Q1, while the interquartile range is the full distance between Q3 and Q1. Examples are given to illustrate quartile calculations.
Basic statistics is the science of collecting, organizing, summarizing, and interpreting data. It allows researchers to gain insights from data through graphical or numerical summaries, regardless of the amount of data. Descriptive statistics can be used to describe single variables through frequencies, percentages, means, and standard deviations. Inferential statistics make inferences about phenomena through hypothesis testing, correlations, and predicting relationships between variables.
This document discusses hypothesis testing, including:
1) The objectives are to formulate statistical hypotheses, discuss types of errors, establish decision rules, and choose appropriate tests.
2) Key symbols and concepts are defined, such as the null and alternative hypotheses, Type I and Type II errors, test statistics like z and t, means, variances, sample sizes, and significance levels.
3) The two types of errors in hypothesis testing are discussed. Hypothesis tests can result in correct decisions or two types of errors when the null hypothesis is true or false.
4) Steps in hypothesis testing are outlined, including formulating hypotheses, specifying a significance level, choosing a test statistic, establishing a
This document provides an introduction to statistics. It discusses why statistics is important and required for many programs. Reasons include the prevalence of numerical data in daily life, the use of statistical techniques to make decisions that affect people, and the need to understand how data is used to make informed decisions. The document also defines key statistical concepts such as population, parameter, sample, statistic, descriptive statistics, inferential statistics, variables, and different types of variables.
Lecture on Introduction to Descriptive Statistics - Part 1 and Part 2. These slides were presented during a lecture at the Colombo Institute of Research and Psychology.
This presentation covers statistics, its importance, its applications, branches of statistics, basic concepts used in statistics, data sampling, types of sampling,types of data and collection of data.
This document discusses measures of central tendency, including the mean, median, and mode. It provides examples of calculating each measure using sample data sets. The mean is the average value calculated by summing all values and dividing by the number of data points. The median is the middle value when data is ordered from lowest to highest. The mode is the most frequently occurring value. Examples are given to demonstrate calculating the mean, median, and mode from sets of numeric data.
Statistics is the methodology used to interpret and draw conclusions from collected data. It provides methods for designing research studies, summarizing and exploring data, and making predictions about phenomena represented by the data. A population is the set of all individuals of interest, while a sample is a subset of individuals from the population used for measurements. Parameters describe characteristics of the entire population, while statistics describe characteristics of a sample and can be used to infer parameters. Basic descriptive statistics used to summarize samples include the mean, standard deviation, and variance, which measure central tendency, spread, and how far data points are from the mean, respectively. The goal of statistical data analysis is to gain understanding from data through defined steps.
Introduction to Statistics - Basic Statistical Termssheisirenebkm
Statistics is the study of collecting, organizing, and interpreting numerical data. It has two main branches: descriptive statistics, which summarizes and describes data, and inferential statistics, which is used to analyze samples and make generalizations about populations. The key concepts in statistics include populations, samples, parameters, statistics, qualitative and quantitative data, discrete and continuous variables.
This document discusses measures of variability, which refer to how spread out a set of data is. Variability is measured using the standard deviation and variance. The standard deviation measures how far data points are from the mean, while the variance is the average of the squared deviations from the mean. To calculate the standard deviation, you take the square root of the variance. This provides a measure of variability that is on the same scale as the original data. The standard deviation and variance are widely used statistical measures for quantifying the spread of a data set.
Distinguish between Parameter and Statistic.
Calculate sample variance and sample standard deviation.
Visit the website for more services: https://cristinamontenegro92.wixsite.com/onevs
The document defines a sampling distribution of sample means as a distribution of means from random samples of a population. The mean of sample means equals the population mean, and the standard deviation of sample means is smaller than the population standard deviation, equaling it divided by the square root of the sample size. As sample size increases, the distribution of sample means approaches a normal distribution according to the Central Limit Theorem.
This document discusses variance and standard deviation. It defines variance as the average squared deviation from the mean of a data set. Standard deviation measures how spread out numbers are from the mean and is calculated by taking the square root of the variance. The document provides step-by-step instructions for calculating both variance and standard deviation, including examples using test score data.
This document discusses six measures of variation used to determine how values are distributed in a data set: range, quartile deviation, mean deviation, variance, standard deviation, and coefficient of variation. It provides definitions and examples of calculating each measure. The range is defined as the difference between the highest and lowest values. Quartile deviation uses the interquartile range (Q3-Q1). Mean deviation is the average of the absolute deviations from the mean. Variance and standard deviation measure how spread out values are from the mean, with variance using sums of squares and standard deviation taking the square root of variance.
This document discusses statistics and their uses in various fields such as business, health, learning, research, social sciences, and natural resources. It provides examples of how statistics are used in starting businesses, manufacturing, marketing, and engineering. Statistics help decision-makers reduce ambiguity and assess risks. They are used to interpret data and make informed decisions. However, statistics also have limitations as they only show averages and may not apply to individuals.
Chapter 6 simple regression and correlationRione Drevale
There is a significant positive correlation between amount of feed intake and live weight of broilers. The correlation coefficient (r) between feed intake and live weight is 0.726, which is statistically significant with p<0.017. On average, broilers gain approximately 0.5 kg of live weight for every 1 kg of feed consumed.
1a difference between inferential and descriptive statistics (explanation)Ken Plummer
The document discusses descriptive and inferential statistics. Descriptive statistics describe the features of a data set using numerical measures like the range, mode, and mean. Inferential statistics draw conclusions about a larger population based on analyzing a sample, allowing inferences to be made about the population. The example shows a teacher using descriptive statistics to answer a parent's questions about their child's spelling test scores and the class data. The parent then asks inferential questions comparing the class to other groups, allowing the teacher to infer how the sample class compares more broadly.
Quick reminder ordinal or scaled or nominal porportionalKen Plummer
This is learning module for a decision point within a decision model for statistics as part of a teaching methodology called Decision-Based Learning developed at Brigham Young University in Provo, Utah, United States
Introduction to Statistics - Basic concepts
- How to be a good doctor - A step in Health promotion
- By Ibrahim A. Abdelhaleem - Zagazig Medical Research Society (ZMRS)
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.
The document discusses quartiles, which divide a data set into four equal parts. The first quartile contains the smallest 25% of values, the second quartile contains values between the 25th and 50th percentiles, the third quartile contains values between the 50th and 75th percentiles, and the fourth quartile contains the largest 25% of values. Formulas are provided for calculating the lower quartile (Q1), median (Q2), and upper quartile (Q3). The quartile deviation is defined as half the distance between Q3 and Q1, while the interquartile range is the full distance between Q3 and Q1. Examples are given to illustrate quartile calculations.
Basic statistics is the science of collecting, organizing, summarizing, and interpreting data. It allows researchers to gain insights from data through graphical or numerical summaries, regardless of the amount of data. Descriptive statistics can be used to describe single variables through frequencies, percentages, means, and standard deviations. Inferential statistics make inferences about phenomena through hypothesis testing, correlations, and predicting relationships between variables.
This document discusses hypothesis testing, including:
1) The objectives are to formulate statistical hypotheses, discuss types of errors, establish decision rules, and choose appropriate tests.
2) Key symbols and concepts are defined, such as the null and alternative hypotheses, Type I and Type II errors, test statistics like z and t, means, variances, sample sizes, and significance levels.
3) The two types of errors in hypothesis testing are discussed. Hypothesis tests can result in correct decisions or two types of errors when the null hypothesis is true or false.
4) Steps in hypothesis testing are outlined, including formulating hypotheses, specifying a significance level, choosing a test statistic, establishing a
This document provides an introduction to statistics. It discusses why statistics is important and required for many programs. Reasons include the prevalence of numerical data in daily life, the use of statistical techniques to make decisions that affect people, and the need to understand how data is used to make informed decisions. The document also defines key statistical concepts such as population, parameter, sample, statistic, descriptive statistics, inferential statistics, variables, and different types of variables.
Lecture on Introduction to Descriptive Statistics - Part 1 and Part 2. These slides were presented during a lecture at the Colombo Institute of Research and Psychology.
This presentation covers statistics, its importance, its applications, branches of statistics, basic concepts used in statistics, data sampling, types of sampling,types of data and collection of data.
This document discusses measures of central tendency, including the mean, median, and mode. It provides examples of calculating each measure using sample data sets. The mean is the average value calculated by summing all values and dividing by the number of data points. The median is the middle value when data is ordered from lowest to highest. The mode is the most frequently occurring value. Examples are given to demonstrate calculating the mean, median, and mode from sets of numeric data.
Statistics is the methodology used to interpret and draw conclusions from collected data. It provides methods for designing research studies, summarizing and exploring data, and making predictions about phenomena represented by the data. A population is the set of all individuals of interest, while a sample is a subset of individuals from the population used for measurements. Parameters describe characteristics of the entire population, while statistics describe characteristics of a sample and can be used to infer parameters. Basic descriptive statistics used to summarize samples include the mean, standard deviation, and variance, which measure central tendency, spread, and how far data points are from the mean, respectively. The goal of statistical data analysis is to gain understanding from data through defined steps.
Introduction to Statistics - Basic Statistical Termssheisirenebkm
Statistics is the study of collecting, organizing, and interpreting numerical data. It has two main branches: descriptive statistics, which summarizes and describes data, and inferential statistics, which is used to analyze samples and make generalizations about populations. The key concepts in statistics include populations, samples, parameters, statistics, qualitative and quantitative data, discrete and continuous variables.
This document discusses measures of variability, which refer to how spread out a set of data is. Variability is measured using the standard deviation and variance. The standard deviation measures how far data points are from the mean, while the variance is the average of the squared deviations from the mean. To calculate the standard deviation, you take the square root of the variance. This provides a measure of variability that is on the same scale as the original data. The standard deviation and variance are widely used statistical measures for quantifying the spread of a data set.
Distinguish between Parameter and Statistic.
Calculate sample variance and sample standard deviation.
Visit the website for more services: https://cristinamontenegro92.wixsite.com/onevs
The document defines a sampling distribution of sample means as a distribution of means from random samples of a population. The mean of sample means equals the population mean, and the standard deviation of sample means is smaller than the population standard deviation, equaling it divided by the square root of the sample size. As sample size increases, the distribution of sample means approaches a normal distribution according to the Central Limit Theorem.
This document discusses variance and standard deviation. It defines variance as the average squared deviation from the mean of a data set. Standard deviation measures how spread out numbers are from the mean and is calculated by taking the square root of the variance. The document provides step-by-step instructions for calculating both variance and standard deviation, including examples using test score data.
This document discusses six measures of variation used to determine how values are distributed in a data set: range, quartile deviation, mean deviation, variance, standard deviation, and coefficient of variation. It provides definitions and examples of calculating each measure. The range is defined as the difference between the highest and lowest values. Quartile deviation uses the interquartile range (Q3-Q1). Mean deviation is the average of the absolute deviations from the mean. Variance and standard deviation measure how spread out values are from the mean, with variance using sums of squares and standard deviation taking the square root of variance.
This document discusses statistics and their uses in various fields such as business, health, learning, research, social sciences, and natural resources. It provides examples of how statistics are used in starting businesses, manufacturing, marketing, and engineering. Statistics help decision-makers reduce ambiguity and assess risks. They are used to interpret data and make informed decisions. However, statistics also have limitations as they only show averages and may not apply to individuals.
Chapter 6 simple regression and correlationRione Drevale
There is a significant positive correlation between amount of feed intake and live weight of broilers. The correlation coefficient (r) between feed intake and live weight is 0.726, which is statistically significant with p<0.017. On average, broilers gain approximately 0.5 kg of live weight for every 1 kg of feed consumed.
1a difference between inferential and descriptive statistics (explanation)Ken Plummer
The document discusses descriptive and inferential statistics. Descriptive statistics describe the features of a data set using numerical measures like the range, mode, and mean. Inferential statistics draw conclusions about a larger population based on analyzing a sample, allowing inferences to be made about the population. The example shows a teacher using descriptive statistics to answer a parent's questions about their child's spelling test scores and the class data. The parent then asks inferential questions comparing the class to other groups, allowing the teacher to infer how the sample class compares more broadly.
Quick reminder ordinal or scaled or nominal porportionalKen Plummer
This is learning module for a decision point within a decision model for statistics as part of a teaching methodology called Decision-Based Learning developed at Brigham Young University in Provo, Utah, United States
This document provides an overview of key concepts in descriptive statistics and intelligence testing including:
1. It describes four scales of measurement: nominal, ordinal, ratio, and equal-interval. It also discusses distributions, measures of central tendency, and measures of dispersion.
2. It discusses norms-referenced and criterion-referenced assessment. It also covers reliability, validity, and factors that can affect accurate assessment such as accommodations for students with disabilities.
3. It provides an overview of intelligence tests and behaviors they sample. It notes the dilemmas in assessing intelligence and describes some commonly used individual intelligence tests.
This document discusses the four scales of measurement used in statistics: nominal, ordinal, interval, and ratio. Nominal scales simply categorize variables without order, like gender or favorite color. Ordinal scales maintain unique identities and a rank order, but not necessarily equal distances, like the results of a horse race. Interval scales preserve equal distances between units in addition to identity and order, as in the Fahrenheit temperature scale. Ratio scales satisfy all properties by also having a true zero point, such as weight scales.
Descriptive statistics are used to analyze and summarize data. There are two types of descriptive measures: measures of central tendency that describe a typical response like the mode, median, and mean; and measures of variability that reveal the typical difference between values like the range and standard deviation. Statistical analysis can be descriptive to summarize data, inferential to make conclusions about a population, differences to compare groups, associative to determine relationships, or predictive to forecast events. Data coding and a code book are used to identify codes for questionnaire responses.
This document provides a literature review on workplace harassment of health workers. It defines different types of workplace harassment including verbal, physical, and sexual harassment. It discusses how harassment can occur between coworkers, managers/supervisors, and customers. The document also summarizes several studies that found high rates of harassment experienced by nurses, doctors, and other healthcare workers. Specifically, it was found that nurses experienced more verbal mistreatment, intimidation and physical violence compared to other health professionals. The document discusses the negative impacts of harassment, including physical and psychological health effects like anxiety, depression, and post-traumatic stress. In conclusion, it emphasizes that sexual harassment violates dignity and can harm victims both psychologically and physically.
Khalil Sattar founded K&NS in 1964 with a vision of improving nutrition in Pakistan by starting a small broiler farm. This small beginning grew into a large poultry and food company that now produces various chicken products. K&NS markets eggs, day-old chicks, poultry feed, processed chicken, and ready-to-cook products. It sells through its own stores and major retailers. While K&NS has been successful in introducing halal products, it faces challenges in capturing new markets and competing on price against other chicken companies.
This document provides information about standard deviation and how to calculate it using highway fatality data from 1999-2001 as an example. It defines standard deviation and the steps to take, which are to find the mean, calculate the deviation of each value from the mean, square the deviations, sum the squared deviations, divide the sum by the number of values, and take the square root of the result. Applying these steps to the fatality data, the mean is calculated to be 41,890.67 and the standard deviation is calculated to be 43,980.2.
This document discusses descriptive and inferential statistics. Descriptive statistics describe what is occurring in an entire population, using words like "all" or "everyone". Inferential statistics draw conclusions about a larger population based on a sample, since observing the entire population is often not feasible. The document provides examples to illustrate the difference, such as determining average test scores for all students versus using a sample of scores to estimate averages for an entire state.
Quickreminder nature of the data (relationship)Ken Plummer
This document provides guidance on which statistical tests to use when analyzing different variable types. It recommends using the phi coefficient for dichotomous by dichotomous variables, point-biserial for dichotomous by scaled variables, Spearman's rho for ordinal by any other variable or scaled by scaled with one variable skewed and less than 30 subjects, and Kendall's tau for ordinal with ties by any other variable or scaled by scaled with one variable skewed and less than 30 subjects with ties.
This document discusses various statistical techniques used for inferential statistics, including parametric and non-parametric techniques. Parametric techniques make assumptions about the population and can determine relationships, while non-parametric techniques make few assumptions and are useful for nominal and ordinal data. Commonly used parametric tests are t-tests, ANOVA, MANOVA, and correlation analysis. Non-parametric tests mentioned include Chi-square, Wilcoxon, and Friedman tests. Examples are provided to illustrate the appropriate uses of each technique.
The document discusses basic descriptive quantitative data analysis techniques such as tables, graphs, and summary statistics. It covers topics like frequency distributions, contingency tables, bar graphs, pie charts, and measures of central tendency and variation. The objectives are to learn how to perform these analyses in Excel and how they are useful for understanding complex quantitative data and communicating findings to others. Employers value these types of quantitative and data visualization skills.
Is the Data Scaled, Ordinal, or Nominal Proportional?Ken Plummer
The document discusses different types of data used in statistical analysis: scaled, ordinal, and nominal data. Scaled data represents quantities where the intervals between values are equal, such as temperature or test scores. Ordinal data uses numbers to represent relative rankings, like placing in an event, but the intervals are not equal. The document uses examples to illustrate the properties of scaled and ordinal data and explains how to determine if a given data set is scaled or ordinal.
This presentation discusses parametric and non-parametric methods for analyzing relationships between variables. Parametric methods can be used when sample data is normally distributed and scaled, representing population parameters. They involve examining relationships between variables like death anxiety and religiosity through statistical tests. Non-parametric methods do not require normal distribution or scaling and can be used as an alternative.
Null hypothesis for single linear regressionKen Plummer
The document discusses the null hypothesis for a single linear regression analysis. It explains that the null hypothesis states that there is no effect or relationship between the independent and dependent variables. As an example, if investigating the relationship between hours of sleep and ACT scores, the null hypothesis would be: "There will be no significant prediction of ACT scores by hours of sleep." The document provides a template for writing the null hypothesis in terms of the specific independent and dependent variables being analyzed.
This document provides guidance on reporting the results of a single sample t-test in APA format. It includes templates for describing the test and population in the introduction and reporting the mean, standard deviation, t-value and significance in the results. An example is given of a hypothetical single sample t-test comparing IQ scores of people who eat broccoli regularly to the general population.
Quick reminder diff-rel-ind-gd of fit (spanish in four slides) (2)Ken Plummer
El documento explica cuatro conceptos estadísticos: diferencia, relación, independencia y calidad de ajuste. La diferencia se refiere a comparar estadísticas entre grupos, la relación examina cómo cambian dos variables juntas, la independencia investiga si una variable depende de otra, y la calidad de ajuste compara resultados reales con expectativas.
The document discusses different scales of measurement used in research. There are four main scales: nominal, ordinal, interval, and ratio. Nominal scales use numbers to replace categories or names and assume no quantitative relationship between numbers. Ordinal scales represent relative quantities of attributes but intervals between numbers are not equal. Interval and ratio scales both assume equal intervals but ratio scales have a true zero point.
This document discusses inferential statistics, which uses sample data to make inferences about populations. It explains that inferential statistics is based on probability and aims to determine if observed differences between groups are dependable or due to chance. The key purposes of inferential statistics are estimating population parameters from samples and testing hypotheses. It discusses important concepts like sampling distributions, confidence intervals, null hypotheses, levels of significance, type I and type II errors, and choosing appropriate statistical tests.
This document provides an introduction to inferential statistics, including key terms like test statistic, critical value, degrees of freedom, p-value, and significance. It explains that inferential statistics allow inferences to be made about populations based on samples through probability and significance testing. Different levels of measurement are discussed, including nominal, ordinal, and interval data. Common inferential tests like the Mann-Whitney U, Chi-squared, and Wilcoxon T tests are mentioned. The process of conducting inferential tests is outlined, from collecting and analyzing data to comparing test statistics to critical values to determine significance. Type 1 and Type 2 errors in significance testing are also defined.
This document provides information on quantitative research methods. It defines quantitative research as collecting numerical data that is analyzed using statistical methods to explain phenomena. The key steps in quantitative research are observing something to explain, collecting numerical data, and analyzing the data using statistics. Common types of quantitative research covered are surveys, correlational research, causal-comparative research, and experimental research. Strengths and weaknesses as well as importance across different fields are discussed. Variables in quantitative research are also defined, including independent, dependent, discrete, and continuous variables.
Practical Research 2 - Quantitative Research (Nature of Inquiry & Research)Cristy Ann Subala
Definition of Quantitative Research
Characteristics of Quantitative Research
Four Basic Kinds of Quantitative Research
Strengths and Weaknesses of Quantitative Research
Importance of Quantitative Research across Fields
Types of variables
This document provides an overview of educational research tools and methods. It discusses different types of sampling techniques including random sampling, stratified sampling, cluster sampling, and systematic sampling. It also covers various data collection instruments such as interviews, questionnaires, tests, scales, and observations. Key aspects of research validity and reliability are defined. The document concludes with an outline of the stages involved in developing an educational research proposal.
This document discusses key concepts in statistics including:
- Descriptive statistics which describes data characteristics and inferential statistics which draws conclusions about populations based on samples.
- The steps in conducting research including data collection, organization, analysis, and interpretation.
- Common statistical terms like population, sample, parameter, variable, and the difference between descriptive and inferential statistics.
Data Analysis and Statistics
Key terms
• Statistics
• Data
• Data Collection
• Descriptive Statistics
• Inferential Statistics
• Discrete Data
• Continuous Data
• Frequency Distribution
This document provides an introduction to statistics lesson 1. It defines statistics as a branch of mathematics that deals with collecting, organizing, presenting, analyzing, and interpreting data. It discusses the origins of the word "statistics" and provides examples of its importance in areas like weather forecasting, predicting disease, and political campaigns. The document also covers topics like the difference between population and sample, parameters and statistics, and types of statistical questions.
The document discusses planning a research study, including identifying the target population and sample, deciding on the appropriate level and size of sampling, and selecting appropriate data collection methods. Some key points covered are:
1) Researchers must identify the target population and determine whether to study individuals, organizations, or a combination. They must also decide how many people or organizations to include in the sample.
2) Researchers select a sampling method depending on rigor needed, population characteristics, and participant availability. Common methods include simple random sampling, stratified sampling, cluster sampling, and convenience/snowball sampling.
3) Researchers identify what data needs to be collected to measure the study variables. Common methods are tests, questionnaires, interviews, observations
Statistics can be used to analyze data, make predictions, and draw conclusions. It has a variety of applications including predicting disease occurrence, weather forecasting, medical studies, quality testing, and analyzing stock markets. There are two main branches of statistics - descriptive statistics which summarizes and presents data, and inferential statistics which analyzes samples to make conclusions about populations. Key terms include population, sample, parameter, statistic, variable, data, qualitative vs. quantitative data, discrete vs. continuous data, and the different levels of measurement. Important figures in the history of statistics mentioned are William Petty, Carl Friedrich Gauss, Ronald Fisher, and James Lind.
These introductory statistics slides will give you a basic understanding of statistics, types of statistics, variable and its types, the levels of measurements, data collection techniques, and types of sampling.
Statistics can be misused in several ways:
1) By using statistics to sell products or get attention through evoking fear or shock with selective or biased statistics.
2) By detaching statistics from their proper context or comparisons to make claims that are misleading.
3) By suggesting causal relationships through statistics that do not account for all variables or factors. Care must be taken to properly collect and interpret statistical data to avoid misleading conclusions.
This presentation is about Basic Statistics-related to types of Data-Qualitative and Quantitative, and its Examples in everyday life- By: Dr. Farhana Shaheen
Statistics is the study of collecting, organizing, analyzing, and interpreting numerical data. It has two main branches: descriptive statistics, which describes characteristics of a data set, and inferential statistics, which draws conclusions about a population based on a sample. Key concepts in statistics include populations, samples, parameters, statistics, variables, and data types.
This document discusses counting techniques including permutations and combinations. It provides examples of using the fundamental counting principle to calculate the number of possible outcomes in situations like selecting shirts from various sizes and colors or generating license plates from letters and numbers. The key concepts of permutations, which involve ordered arrangements, and combinations, which involve unordered selections, are explained along with related formulas. Practice problems apply these techniques to scenarios such as seating arrangements, word rearrangements, and group selections.
Here are the steps to solve this problem using stratified random sampling:
1. Divide the population into strata based on the barangays.
2. Calculate the sample size for each stratum proportionately based on the total sample size (1000 residents) and population size of each stratum.
3. Randomly select the calculated sample size from each stratum.
Barangay Population Proportion of sample Sample size
Mapayapa 2,000 0.2 200
Malinis 1,000 0.1 100
Mahangin 1,500 0.15 150
Mabunga 2,500 0.25 250
There are 9! = 362,880 ways to seat 9 people around a round table, as the fundamental counting principle states that with 9 seats and 9 people, each person can be placed in each seat in 9 * 8 * 7 ... * 1 = 9! ways.
Here are the modes for the three examples:
1. The mode is 3. This value occurs most frequently among the number of errors committed by the typists.
2. The mode is 82. This value occurs most frequently among the number of fruits yielded by the mango trees.
3. The mode is 12 and 15. These values occur most frequently among the students' quiz scores.
This document discusses measures of central tendency - mean, median, and mode - for grouped data. It provides examples of calculating the mean, median, and mode using frequency distributions with class intervals and frequencies. The mean is calculated using the class midpoint formula or coded formula. The median is found by determining the median class based on the cumulative frequency. The mode is the class with the highest frequency.
This document discusses different methods for presenting data graphically. It begins by listing the objectives of the lesson and identifying textual, tabular, and graphical methods. Examples of various graphs like bar charts, histograms, frequency polygons, pie charts, and ogives are then shown and explained using sample data on examination scores. The document concludes by assigning activities for students to practice constructing these different graphs from sample data sets.
This document discusses different methods for presenting data graphically. It defines bar charts, histograms, frequency polygons, pie charts, and ogives. Examples of each type of graph are provided using sample data on examination scores of 60 students. Bar charts and histograms use class marks and frequencies to show the distribution. Frequency polygons connect the points on a line graph. Pie charts show the proportion of each class. Ogives use class boundaries and cumulative frequencies to indicate less than and greater than distributions. Students are assigned an activity to practice constructing these various graphs using their own collected data.
1. The document discusses different methods for presenting data, including textual, tabular, and graphical methods.
2. It provides examples of how to prepare a stem-and-leaf plot, construct a frequency distribution table, and define key terms related to grouped and ungrouped data presentation.
3. The objectives are to describe how to prepare a stem-and-leaf plot, describe data textually, construct a frequency distribution table, create graphs, and interpret graphs and tables.
1. The document discusses different techniques for gathering data, including direct interviews, questionnaires, registration, and experiments.
2. It also provides Slovin's formula for determining sample size based on population size and margin of error.
3. Several examples are given to illustrate determining sample size using Slovin's formula for different population sizes and margins of error.
This document discusses how graphs and statistics can be misleading. It provides examples of graphs that exaggerate differences through misleading scales or that imply correlations without sufficient evidence. Readers are advised to consider whether the data is presented accurately, whether the graph is trying to influence them, and whether conclusions follow when margins of error are large. Graphs should use regular intervals and present information clearly without biased implications.
This document provides an overview of levels of measurement in statistics and applications of statistics. It discusses the four levels of measurement - nominal, ordinal, interval, and ratio - and provides examples. It also outlines several real-world fields where statistics is used, such as health, manufacturing, and environmental protection. Finally, it notes that statistics can be abused and lists some examples of misleading statistical claims.
This document discusses several graphs and statistics that can be misleading. It provides examples of graphs that use scales to exaggerate differences, imply causation, or lack necessary context. Readers are advised to consider whether the data is presented accurately, whether the graph is trying to influence them, and whether margins of error are too large.
This document provides an overview of levels of measurement in statistics and applications of statistics. It discusses the four levels of measurement - nominal, ordinal, interval, and ratio - and provides examples. It also outlines several real-world fields that utilize statistics such as manufacturing, public health, environmental protection, and lawmaking. Specifically, it notes how statistics helps determine economic indicators, improve products, control diseases, protect wildlife, and inform policies around issues like pollution and traffic safety. Finally, it cautions that statistics can also be misleading if not interpreted carefully.
This document provides an introduction to summation notation and how to use a scientific calculator to evaluate expressions involving summation. It contains examples of using summation to find the total and average of data sets. The document also explains how to enter two-variable data into a calculator and obtain statistics like the total, average, and standard deviation of each variable as well as their correlation. Exercises at the end provide practice calculating various statistics for two data sets using summation notation and a calculator.
Statistics involves collecting, organizing, presenting, analyzing, and interpreting data to make decisions. Descriptive statistics describes characteristics and properties of a group through gathering, organizing, presenting, and describing data. Inferential statistics draws inferences about a large group based on a sample through inductive reasoning and hypothesis testing. The examples provided illustrate common uses of descriptive and inferential statistics.
2. Statistics is a group of methods that are used to
collect, organize, present, analyze, and interpret
data to make decisions.
Collection refers to the gathering of information or
data.
Organization or presentation involves summarizing
data or information in textual, graphical, or tabular
forms.
Analysis involves describing the data by using
statistical methods and procedures.
Interpretation refers to the process of making
conclusions based on the analyzed data.
4. Descriptive Statistics
- is a statistical procedure concerned with
describing the characteristics and properties of a
group of persons, places, or things.
- Involves gathering, organizing, presenting, and
describing data.
For example, we may describe a collection of
persons by stating how many are poor and how
many are rich, how many are literate and how
many are illiterate, how many fall into various
categories of age, height, civil status, IQ, and
many more.
5. 1. How many students are interested to take
Statistics online?
2. What are the highest and lowest scores
obtained by STENEX applicants this year?
3. What are the characteristics of the most
likable teacher according to students?
4. What proportion of SRSTHS students likes
Mathematics?
6. Inferential Statistics
is a statistical procedure that is used to draw
inferences or information about the properties or
characteristics by a large group of people, places, or
things on the basis of the information obtained from a
small portion of a large group.
also called inductive reasoning or inductive
statistics.
Example:
Suppose we want to know the most favorite brand of
toothpaste of a certain barangay and we do not have
enough time and money to interview all the residents
of that barangay, we may just ask selected residents.
With the data obtained from the interviews, we shall
draw or make conclusion as to the barangay’s favorite
brand of toothpaste.
7. 1. Is there a significant difference in the
academic performance of male and female
sophomore students in Statistics?
2. Is there a significant difference between
the proportions of students who prefer
Coke than Pepsi?
3. Is there a significant relationship between
amount of time studied and grades
received?
4. Is there a significant difference between
the Biology scores of 30 students before
and after taking Memory Plus for 15 days?
8. Descriptive Inferential
Sampling Distribution
Definition of Terms
Sampling Techniques Hypothesis Testing
• Z – test
Presentation of data • T – test
• F – test
• Test on Proportion
Summation • Chi-square test
Calculator Exercises
Correlation and Regression
Summary Measures of
Data
Normal Distribution
9. Tell whether the following situations will make use
of descriptive statistics or inferential statistics.
1. A teacher computes the average grade of her
students and then determines the top ten
students.
2. A manager or a business firm predicts future
sales of the company based on the present
sales.
3. A psychologist investigates if there is a
significant relationship between mental age
and chronological age.
4. A researcher studies the effectiveness of a new
fertilizer to increasing food production.
5. A janitor counts the number of various
furniture inside the school.
10. 6. A sports journalist determines the most
popular basketball player for this year.
7. A school administrator forecast future
expansion of a school.
8. A market vendor investigates the most
popular brand of vinegar.
9. An engineer calculates the average height
of the buildings along Taft Avenue.
10. A dermatologist tests the relative
effectiveness of a new brand of medicine in
curing pimples and other skin diseases.
12. In this survey conducted by Pulse Asia:
1. Who were surveyed by Pulse Asia?
2. Is there anyone among you who
was a respondent in this research?
3. Why do you think Pulse Asia was
able to conclude the 69% favor RH
bill?
13. A population consists of all elements –
individuals, items, or objects – whose
characteristics are being studied. The
population being studied is called the target
population.
A portion of the population selected for
study is referred to as a sample.
14. Population – total number of SRSTHS students
during SY 2010-2011: 877 students
Sample – Second year students of SRSTHS
during SY 2010-2011: 228 students
Give your own examples!
15. Elements or Members of a sample or population is a
specific subject or object(for example, a person, firm,
item, state or country)
Example: YOU as a member of the SRSTHS
population.
Variable is a characteristic or property of a population
or sample which makes the members different from
each other.
Example: in II-Pasteur, gender is a variable
Constant is a property or characteristic of a
population or sample, which makes the members of
the group similar to each other.
Example: if a class is composed of all boys, gender
is constant.
Data (singular form is datum)are numbers or
measurements that are collected as a result from
observation, interview, questionnaire,
experimentation, test and so forth.
16. Parameter is any numerical or nominal
characteristic of a population. It is a value or
measurement obtained from a population. It is
usually referred to as the true or actual value.
Example: The researcher uses the whole
population of SRSTHS to get the average
allowance of SRSTHS students.
Statistic is an estimate of a parameter. It is any
value or measurement obtained from a sample.
Example: The researcher uses the sample
(n=200) to get the average allowance of SRSTHS
students.
17. Qualitative data are data which can assume
values that manifest the concept of
attributes. These are sometimes called
categorical data.
Example: gender, nationality
Quantitative data are data which are
numerical in nature. These are data
obtained from counting or measuring.
Example: Height, test scores
19. Discrete Variables – is one that can assume a
finite number of values. In other words, it
can assume specific values only. The values
of a discrete variable are obtained through
the process of counting.
Example: the number of chairs in a room
Continuous Variables – A variable that can
assume any numerical value over a certain
interval or interval. The values of a
continuous variable are obtained through
measuring.
Example: The height of Kuya Ronil.
20. Dependent Variable is a variable which is
affected or influenced by another variable.
Independent Variable is one which affects or
influences the dependent variable.
Example:
In a research problem entitled,
“The Effect of Technology-based Instruction
on the Students’ Mathematics Achievement”.
The independent variable here is the
technology-based instruction, while the
dependent variable is the academic
achievement of students.
21. A. Classify the following as quantitative or
qualitative data
1. Color of the eye
2. Number of typewriters in a room
3. Civil status
4. Address
5. Telephone numbers
6. Age of teachers
7. Rank of students
8. Speed of a car
9. Birth rates
10. Score in mathematics examination
22. B. Identify each of the following as continuous or
discrete.
1. Weight of a body
2. Length of a rod
3. Number of chairs in the room
4. Dimensions of a table
5. Number of possible outcomes in throwing a die
6. Number of hairs on your head
7. Amount of sales in a business firm
8. All rational numbers
9. Speed of light
10. Area of a land
11. Lifetime of television tubes and batteries
12. Life span of a person
13. Number of passengers in a plane.
23. A. Google search or cut out newspaper clippings on a
research article on any topic. It should contain the results
of any survey conducted locally(preferred) or abroad.
Guidelines:
1. Clip the whole article if taken from a magazine or
newspaper. If it comes from the Internet, download the
whole article. If it is more than two pages, summarize
it.
2. Indicate the name of magazine/newspaper, date of
publication, title of article and author. Highlight the
population/sample/margin of error used in the article.
3. Identify statements which belong to: (a)descriptive
statistics (b) inferential statistics
4. Find out the population/sample used in the survey.
5. Enumerate the data gathered and classify whether they
are: a) qualitative b) quantitative