This document discusses descriptive statistics and analysis. It provides definitions of key terms like data, variable, statistic, and parameter. It also describes common measures of central tendency like mean, median and mode. Additionally, it covers measures of variability such as range, variance and standard deviation. Various graphical and numerical methods for summarizing and presenting sample data are presented, including tables, charts and distributions.
This document provides an overview of descriptive statistics techniques for summarizing categorical and quantitative data. It discusses frequency distributions, measures of central tendency (mean, median, mode), measures of variability (range, variance, standard deviation), and methods for visualizing data through charts, graphs, and other displays. The goal of descriptive statistics is to organize and describe the characteristics of data through counts, averages, and other summaries.
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.
1) The document discusses commonly used statistical tests in research such as descriptive statistics, inferential statistics, hypothesis testing, and tests like t-tests, ANOVA, chi-square tests, and normal distributions.
2) It provides examples of how to determine sample sizes needed for adequate power in hypothesis testing and how to perform t-tests to analyze sample means.
3) Key statistical concepts covered include parameters, statistics, measurement scales, type I and II errors, and interpreting results of hypothesis tests.
This document discusses various statistical methods used to organize and interpret data. It describes descriptive statistics, which summarize and simplify data through measures of central tendency like mean, median, and mode, and measures of variability like range and standard deviation. Frequency distributions are presented through tables, graphs, and other visual displays to organize raw data into meaningful categories.
This document discusses research hypotheses. It defines a hypothesis as a tentative statement about the relationship between two or more variables. Hypotheses are important as they help translate research problems into predicted outcomes and guide methodology. Good hypotheses are clear, testable, and relevant to the research. Hypotheses can be simple, complex, associative, causal, directional, or non-directional. They may be generated from theoretical frameworks, previous studies, literature or experiences. The null hypothesis states there is no relationship between variables while the research hypothesis predicts a relationship.
Atomic absorption spectroscopy is an analytical technique that measures the concentration of elements by detecting the amount of light absorbed by atoms in the gaseous state at specific wavelengths. It works by vaporizing and atomizing samples using a flame or graphite furnace, then measuring the absorption of light from a hollow cathode lamp at characteristic wavelengths. The instrument consists of a light source, atomizer, monochromator, detector, and readout system. Calibration curves of concentration versus absorption are used to determine unknown concentrations in samples. Potential interferences can affect the analysis and must be minimized. Atomic absorption spectroscopy has various applications in fields like metallurgy, pharmaceutical analysis, and biochemical analysis.
1. The document discusses descriptive statistics, which is the study of how to collect, organize, analyze, and interpret numerical data.
2. Descriptive statistics can be used to describe data through measures of central tendency like the mean, median, and mode as well as measures of variability like the range.
3. These statistical techniques help summarize and communicate patterns in data in a concise manner.
This document provides an overview of statistics concepts including descriptive and inferential statistics. Descriptive statistics are used to summarize and describe data through measures of central tendency (mean, median, mode), dispersion (range, standard deviation), and frequency/percentage. Inferential statistics allow inferences to be made about a population based on a sample through hypothesis testing and other statistical techniques. The document discusses preparing data in Excel and using formulas and functions to calculate descriptive statistics. It also introduces the concepts of normal distribution, kurtosis, and skewness in describing data distributions.
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)
Descriptive statistics is used to describe and summarize key characteristics of a data set. Commonly used measures include central tendency, such as the mean, median, and mode, and measures of dispersion like range, interquartile range, standard deviation, and variance. The mean is the average value calculated by summing all values and dividing by the number of values. The median is the middle value when data is arranged in order. The mode is the most frequently occurring value. Measures of dispersion describe how spread out the data is, such as the difference between highest and lowest values (range) or how close values are to the average (standard deviation).
Descriptive statistics, central tendency, measures of variability, measures of dispersion, skewness, kurtosis, range, standard deviation, mean, median, mode, variance, normal distribution
This document defines data and different types of data presentation. It discusses quantitative and qualitative data, and different scales for qualitative data. The document also covers different ways to present data scientifically, including through tables, graphs, charts and diagrams. Key types of visual presentation covered are bar charts, histograms, pie charts and line diagrams. Presentation should aim to clearly convey information in a concise and systematic manner.
This presentation is about Basic Statistics-related to types of Data-Qualitative and Quantitative, and its Examples in everyday life- By: Dr. Farhana Shaheen
This document discusses measures of central tendency and variability in descriptive statistics. It defines and provides formulas for calculating the mean, median, and mode as measures of central tendency. The mean is the most useful measure and is calculated by summing all values and dividing by the total number of observations. Variability refers to how spread out or clustered the data values are and is measured by calculations like the range, variance, and standard deviation. The standard deviation is specifically defined as the average deviation of the data from the mean and is considered the best single measure of variability.
The document provides an overview of inferential statistics. It defines inferential statistics as making generalizations about a larger population based on a sample. Key topics covered include hypothesis testing, types of hypotheses, significance tests, critical values, p-values, confidence intervals, z-tests, t-tests, ANOVA, chi-square tests, correlation, and linear regression. The document aims to explain these statistical concepts and techniques at a high level.
measurement and scaling is an important tool of research. by following the right and suitable scale will provide an appropriate result of research.this slide show will additionally provide the statistical testing for research measurement and scale.
Statistical Data Analysis | Data Analysis | Statistics Services | Data Collec...Stats Statswork
The present article helps the USA, the UK and the Australian students pursuing their business and marketing postgraduate degree to identify right topic in the area of marketing in business. These topics are researched in-depth at the University of Columbia, brandies, Coventry, Idaho, and many more. Stats work offers UK Dissertation stats work Topics Services in business. When you Order stats work Dissertation Services at Tutors India, we promise you the following – Plagiarism free, Always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts.
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This document discusses sampling and sampling distributions. It begins by explaining why sampling is preferable to a census in terms of time, cost and practicality. It then defines the sampling frame as the listing of items that make up the population. Different types of samples are described, including probability and non-probability samples. Probability samples include simple random, systematic, stratified, and cluster samples. Key aspects of each type are defined. The document also discusses sampling distributions and how the distribution of sample statistics such as means and proportions can be approximated as normal even if the population is not normal, due to the central limit theorem. It provides examples of how to calculate probabilities and intervals for sampling distributions.
This document provides an introduction and overview of biostatistics. It defines key biostatistics terms like population, sample, parameter, statistic, quantitative vs. qualitative data, levels of measurement, descriptive vs. inferential biostatistics, and common statistical notations. It also discusses sources of health information and how computerized health management information systems are used to collect, analyze and report data.
- Univariate analysis refers to analyzing one variable at a time using statistical measures like proportions, percentages, means, medians, and modes to describe data.
- These measures provide a "snapshot" of a variable through tools like frequency tables and charts to understand patterns and the distribution of cases.
- Measures of central tendency like the mean, median and mode indicate typical or average values, while measures of dispersion like the standard deviation and range indicate how spread out or varied the data are around central values.
This document discusses descriptive statistics and how they are used to summarize and describe data. Descriptive statistics allow researchers to analyze patterns in data but cannot be used to draw conclusions beyond the sample. Key aspects covered include measures of central tendency like mean, median, and mode to describe the central position in a data set. Measures of dispersion like range and standard deviation are also discussed to quantify how spread out the data values are. Frequency distributions are described as a way to summarize the frequencies of individual data values or ranges.
Descriptive statistics are methods of describing the characteristics of a data set. It includes calculating things such as the average of the data, its spread and the shape it produces.
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.
Variables describe attributes that can vary between entities. They can be qualitative (categorical) or quantitative (numeric). Common types of variables include continuous, discrete, ordinal, and nominal variables. Data can be presented graphically through bar charts, pie charts, histograms, box plots, and scatter plots to better understand patterns and trends. Key measures used to summarize data include measures of central tendency (mean, median, mode) and measures of variability (range, standard deviation, interquartile range).
This document provides an overview of data processing and analysis techniques. It discusses editing, coding, classification, and tabulation as part of data processing. For data analysis, it describes descriptive statistics such as univariate, bivariate, and multivariate analysis. It also discusses inferential statistics and various correlation, regression, time series analysis techniques to determine relationships between variables and test hypotheses.
Descriptive statistics are used to summarize large datasets and communicate findings. There are measures of central tendency like mean, median, and mode to describe typical values. Measures of dispersion like range and standard deviation quantify how spread out the data is. Skewness measures describe the symmetry of distributions. Together these statistics condense complex data into clear high-level insights.
1. The document discusses descriptive statistics, which is the study of how to collect, organize, analyze, and interpret numerical data.
2. Descriptive statistics can be used to describe data through measures of central tendency like the mean, median, and mode as well as measures of variability like the range.
3. These statistical techniques help summarize and communicate patterns in data in a concise manner.
This document provides an overview of statistics concepts including descriptive and inferential statistics. Descriptive statistics are used to summarize and describe data through measures of central tendency (mean, median, mode), dispersion (range, standard deviation), and frequency/percentage. Inferential statistics allow inferences to be made about a population based on a sample through hypothesis testing and other statistical techniques. The document discusses preparing data in Excel and using formulas and functions to calculate descriptive statistics. It also introduces the concepts of normal distribution, kurtosis, and skewness in describing data distributions.
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)
Descriptive statistics is used to describe and summarize key characteristics of a data set. Commonly used measures include central tendency, such as the mean, median, and mode, and measures of dispersion like range, interquartile range, standard deviation, and variance. The mean is the average value calculated by summing all values and dividing by the number of values. The median is the middle value when data is arranged in order. The mode is the most frequently occurring value. Measures of dispersion describe how spread out the data is, such as the difference between highest and lowest values (range) or how close values are to the average (standard deviation).
Descriptive statistics, central tendency, measures of variability, measures of dispersion, skewness, kurtosis, range, standard deviation, mean, median, mode, variance, normal distribution
This document defines data and different types of data presentation. It discusses quantitative and qualitative data, and different scales for qualitative data. The document also covers different ways to present data scientifically, including through tables, graphs, charts and diagrams. Key types of visual presentation covered are bar charts, histograms, pie charts and line diagrams. Presentation should aim to clearly convey information in a concise and systematic manner.
This presentation is about Basic Statistics-related to types of Data-Qualitative and Quantitative, and its Examples in everyday life- By: Dr. Farhana Shaheen
This document discusses measures of central tendency and variability in descriptive statistics. It defines and provides formulas for calculating the mean, median, and mode as measures of central tendency. The mean is the most useful measure and is calculated by summing all values and dividing by the total number of observations. Variability refers to how spread out or clustered the data values are and is measured by calculations like the range, variance, and standard deviation. The standard deviation is specifically defined as the average deviation of the data from the mean and is considered the best single measure of variability.
The document provides an overview of inferential statistics. It defines inferential statistics as making generalizations about a larger population based on a sample. Key topics covered include hypothesis testing, types of hypotheses, significance tests, critical values, p-values, confidence intervals, z-tests, t-tests, ANOVA, chi-square tests, correlation, and linear regression. The document aims to explain these statistical concepts and techniques at a high level.
measurement and scaling is an important tool of research. by following the right and suitable scale will provide an appropriate result of research.this slide show will additionally provide the statistical testing for research measurement and scale.
Statistical Data Analysis | Data Analysis | Statistics Services | Data Collec...Stats Statswork
The present article helps the USA, the UK and the Australian students pursuing their business and marketing postgraduate degree to identify right topic in the area of marketing in business. These topics are researched in-depth at the University of Columbia, brandies, Coventry, Idaho, and many more. Stats work offers UK Dissertation stats work Topics Services in business. When you Order stats work Dissertation Services at Tutors India, we promise you the following – Plagiarism free, Always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts.
Contact Us:
Website: www.statswork.com
Email: info@statswork.com
UnitedKingdom: +44-1143520021
India: +91-4448137070
WhatsApp: +91-8754446690
This document discusses sampling and sampling distributions. It begins by explaining why sampling is preferable to a census in terms of time, cost and practicality. It then defines the sampling frame as the listing of items that make up the population. Different types of samples are described, including probability and non-probability samples. Probability samples include simple random, systematic, stratified, and cluster samples. Key aspects of each type are defined. The document also discusses sampling distributions and how the distribution of sample statistics such as means and proportions can be approximated as normal even if the population is not normal, due to the central limit theorem. It provides examples of how to calculate probabilities and intervals for sampling distributions.
This document provides an introduction and overview of biostatistics. It defines key biostatistics terms like population, sample, parameter, statistic, quantitative vs. qualitative data, levels of measurement, descriptive vs. inferential biostatistics, and common statistical notations. It also discusses sources of health information and how computerized health management information systems are used to collect, analyze and report data.
- Univariate analysis refers to analyzing one variable at a time using statistical measures like proportions, percentages, means, medians, and modes to describe data.
- These measures provide a "snapshot" of a variable through tools like frequency tables and charts to understand patterns and the distribution of cases.
- Measures of central tendency like the mean, median and mode indicate typical or average values, while measures of dispersion like the standard deviation and range indicate how spread out or varied the data are around central values.
This document discusses descriptive statistics and how they are used to summarize and describe data. Descriptive statistics allow researchers to analyze patterns in data but cannot be used to draw conclusions beyond the sample. Key aspects covered include measures of central tendency like mean, median, and mode to describe the central position in a data set. Measures of dispersion like range and standard deviation are also discussed to quantify how spread out the data values are. Frequency distributions are described as a way to summarize the frequencies of individual data values or ranges.
Descriptive statistics are methods of describing the characteristics of a data set. It includes calculating things such as the average of the data, its spread and the shape it produces.
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.
Variables describe attributes that can vary between entities. They can be qualitative (categorical) or quantitative (numeric). Common types of variables include continuous, discrete, ordinal, and nominal variables. Data can be presented graphically through bar charts, pie charts, histograms, box plots, and scatter plots to better understand patterns and trends. Key measures used to summarize data include measures of central tendency (mean, median, mode) and measures of variability (range, standard deviation, interquartile range).
This document provides an overview of data processing and analysis techniques. It discusses editing, coding, classification, and tabulation as part of data processing. For data analysis, it describes descriptive statistics such as univariate, bivariate, and multivariate analysis. It also discusses inferential statistics and various correlation, regression, time series analysis techniques to determine relationships between variables and test hypotheses.
Descriptive statistics are used to summarize large datasets and communicate findings. There are measures of central tendency like mean, median, and mode to describe typical values. Measures of dispersion like range and standard deviation quantify how spread out the data is. Skewness measures describe the symmetry of distributions. Together these statistics condense complex data into clear high-level insights.
This document provides an overview of key concepts in statistics including:
- Statistics involves collecting and analyzing quantitative data and summarizing results numerically. It is used across many fields including business, economics, and science.
- Common statistical measures include the mean, median, mode, range, variance, and standard deviation which quantify central tendency and dispersion of data.
- Time series analysis examines data measured over time to identify trends, seasonal variations, cycles, and irregular fluctuations. Proper sampling and avoiding bias are important in statistical analysis.
This document provides an introduction to statistics. It defines statistics as the science of data that involves collecting, classifying, summarizing, organizing, and interpreting numerical information. It outlines key terms such as data, population, sample, parameter, and statistic. It describes different types of variables like independent and dependent variables. It discusses descriptive statistics, inferential statistics, and predictive modeling. Finally, it explains important concepts like measures of central tendency, measures of variation, and statistical distributions like the normal distribution.
This document provides an overview of biostatistics. It defines biostatistics as the branch of statistics dealing with biological and medical data, especially relating to humans. Some key points covered include:
- Descriptive statistics are used to describe data through methods like graphs and quantitative measures. Inferential statistics are used to characterize populations based on sample results.
- Biostatistics applies statistical techniques to collect, analyze, and interpret data from biological studies and health/medical research. It is used for tasks like evaluating vaccine effectiveness and informing public health priorities.
- Common analyses in biostatistics include measures of central tendency like the mean, median, and mode to summarize data, and measures of dispersion to quantify variation. Frequency distributions are
This document provides an overview of quantitative research design and methods. It discusses quantitative research as aiming to discover how many people think, act or feel in a specific way using large sample sizes and standardized questions. The summary then describes quantitative research designs as descriptive (measuring subjects once) or experimental (measuring subjects before and after treatment). It also summarizes key aspects of quantitative data analysis including descriptive statistics, inferential statistics, and some common parametric and non-parametric statistical tests.
A frequency distribution summarizes data by organizing it into intervals and counting the frequency of observations within each interval. It presents the data distribution in a table or chart. To create one, you first collect data, identify the range of values, create intervals, count frequencies within each interval, and construct a table or chart showing the intervals and frequencies. Frequency distributions are useful for understanding central tendency, dispersion, patterns and making comparisons. They have many applications across fields like descriptive statistics, data analysis, business, economics, manufacturing, healthcare and education.
The material is consolidated from different sources on the basic concepts of Statistics which could be used for the Visualization an Prediction requirements of Analytics.
I deeply acknowledge the sources which helped me consolidate the material for my students.
Please acknowledge my work and I hope you like it. This is not boring like other ppts you see, I have tried my best to make it extremely informative with lots of pictures and images, I am sure if you choose this as your presentation for statistics topic in your office or school, you are surely going to appreciated by all including your teachers, friends, your interviewer or your manager.
Practical Research 2 Chapter 3: Common Statistical ToolsDaianMoreno1
This document provides an overview of common statistical tools including:
- The arithmetic mean, which is the sum of a list of numbers divided by the total number of items. It provides an overall trend of data.
- Frequency distributions which show how many evaluations fall into various categories using tables, histograms or pie charts.
- Bar graphs which present categorical and numeric variables in class intervals through bars to show patterns.
- Standard deviation which measures how spread out numbers are from the mean. A low standard deviation means numbers align with the mean.
- T-tests and Pearson's correlation coefficient which measure relationships between variables, and the chi-square test which compares expected to observed categorical variable frequencies.
Measure of central tendency grouped data.pptxSandeAlotaBoco
A measure of central tendency describes the middle or center of a data set and includes the mean, median, and mode. The mean is the average value calculated by dividing the sum of all values by the total number of values. The median is the middle number when values are arranged from lowest to highest. The mode is the value that occurs most frequently in the data set.
This document provides an overview of key concepts in psychological statistics. It defines statistics as procedures for organizing, summarizing, and interpreting information using facts and figures. It discusses populations and samples, variables and data, parameters and statistics, descriptive and inferential statistics, sampling error, and experimental and nonexperimental methods. It also covers scales of measurement, frequency distributions, measures of central tendency and variability, and the importance of measurement in research.
pulse ppt.pptx Types of pulse , characteristics of pulse , Alteration of pulsesushreesangita003
what is pulse ?
Purpose
physiology and Regulation of pulse
Characteristics of pulse
factors affecting pulse
Sites of pulse
Alteration of pulse
for BSC Nursing 1st semester
for Gnm Nursing 1st year
Students .
vitalsign
Title: A Quick and Illustrated Guide to APA Style Referencing (7th Edition)
This visual and beginner-friendly guide simplifies the APA referencing style (7th edition) for academic writing. Designed especially for commerce students and research beginners, it includes:
✅ Real examples from original research papers
✅ Color-coded diagrams for clarity
✅ Key rules for in-text citation and reference list formatting
✅ Free citation tools like Mendeley & Zotero explained
Whether you're writing a college assignment, dissertation, or academic article, this guide will help you cite your sources correctly, confidently, and consistent.
Created by: Prof. Ishika Ghosh,
Faculty.
📩 For queries or feedback: ishikaghosh9@gmail.com
In this concise presentation, Dr. G.S. Virdi (Former Chief Scientist, CSIR-CEERI, Pilani) introduces the Junction Field-Effect Transistor (JFET)—a cornerstone of modern analog electronics. You’ll discover:
Why JFETs? Learn how their high input impedance and low noise solve the drawbacks of bipolar transistors.
JFET vs. MOSFET: Understand the core differences between JFET and MOSFET devices.
Internal Structure: See how source, drain, gate, and the depletion region form a controllable semiconductor channel.
Real-World Applications: Explore where JFETs power amplifiers, sensors, and precision circuits.
Perfect for electronics students, hobbyists, and practicing engineers looking for a clear, practical guide to JFET technology.
How to Manage Upselling in Odoo 18 SalesCeline George
In this slide, we’ll discuss on how to manage upselling in Odoo 18 Sales module. Upselling in Odoo is a powerful sales technique that allows you to increase the average order value by suggesting additional or more premium products or services to your customers.
How to Manage Purchase Alternatives in Odoo 18Celine George
Managing purchase alternatives is crucial for ensuring a smooth and cost-effective procurement process. Odoo 18 provides robust tools to handle alternative vendors and products, enabling businesses to maintain flexibility and mitigate supply chain disruptions.
Computer crime and Legal issues Computer crime and Legal issuesAbhijit Bodhe
• Computer crime and Legal issues: Intellectual property.
• privacy issues.
• Criminal Justice system for forensic.
• audit/investigative.
• situations and digital crime procedure/standards for extraction,
preservation, and deposition of legal evidence in a court of law.
How to Clean Your Contacts Using the Deduplication Menu in Odoo 18Celine George
In this slide, we’ll discuss on how to clean your contacts using the Deduplication Menu in Odoo 18. Maintaining a clean and organized contact database is essential for effective business operations.
Ajanta Paintings: Study as a Source of HistoryVirag Sontakke
This Presentation is prepared for Graduate Students. A presentation that provides basic information about the topic. Students should seek further information from the recommended books and articles. This presentation is only for students and purely for academic purposes. I took/copied the pictures/maps included in the presentation are from the internet. The presenter is thankful to them and herewith courtesy is given to all. This presentation is only for academic purposes.
How to Configure Public Holidays & Mandatory Days in Odoo 18Celine George
In this slide, we’ll explore the steps to set up and manage Public Holidays and Mandatory Days in Odoo 18 effectively. Managing Public Holidays and Mandatory Days is essential for maintaining an organized and compliant work schedule in any organization.
How to Configure Scheduled Actions in odoo 18Celine George
Scheduled actions in Odoo 18 automate tasks by running specific operations at set intervals. These background processes help streamline workflows, such as updating data, sending reminders, or performing routine tasks, ensuring smooth and efficient system operations.
What is the Philosophy of Statistics? (and how I was drawn to it)jemille6
What is the Philosophy of Statistics? (and how I was drawn to it)
Deborah G Mayo
At Dept of Philosophy, Virginia Tech
April 30, 2025
ABSTRACT: I give an introductory discussion of two key philosophical controversies in statistics in relation to today’s "replication crisis" in science: the role of probability, and the nature of evidence, in error-prone inference. I begin with a simple principle: We don’t have evidence for a claim C if little, if anything, has been done that would have found C false (or specifically flawed), even if it is. Along the way, I’ll sprinkle in some autobiographical reflections.
2. Terms of
Statistics
Refers to methods and
techniques used for describing,
organizing, analyzing, and
interpreting numerical data.
Statistics is often categorized
into descriptive and
inferential statistics.
3. Types of statistics:
• Descriptive (which summarize some characteristic of a sample)
• Measures of central tendency
• Measures of distribution
• Measures of skewness
• Inferential (which test for significant differences between groups
and/or significant relationships among variables within the sample
• t-ratio, chi-square, beta-value
4. Univariate
Analysis
• Univariate analysis involves the
examination across cases of one
variable at a time. There are three
major characteristics of a single
variable that we tend to look at:
• the distribution
• the central tendency
• the dispersion
In most situations, we would describe
all three of these characteristics for
each of the variables in our study.
5. 100,000 fifth-grade
students take an
English achievement
test
Researcher randomly
samples 1,000 students
scores
Used to describe
the sample
Based on descriptive
statistics to estimate
scores of the entire
population o 100,000
students
6. Descriptive
Statistics
Thus, descriptive statistics are
used to classify, organize, and
summarize numerical data about a
particular group of observations.
There is no attempt to generalize
these statistics, which describe
only one group, to other samples
or population.
7. Descriptive
Statistics:
Methods of describing the
characteristics of a data set.
Useful because they allow you
to make sense of the data.
Helps exploring and making
conclusions about the data in
order to make rational decisions.
Includes calculating things such
as the average of the data, its
spread and the shape it
produces.
8. Continue
In other words, descriptive statistics are used to
summarize, organize, and reduce large
numbers of observations.
Descriptive statistics portray and focus on what
is with respect to the sample data, for example:
What percentage of students want to go to
college?
11. • A frequency distribution is a table that shows classes or intervals of
data with a count of the number in each class. The frequency f of a
class is the number of data points in the class.
Frequencies
Upper Class
Limits
12. Cont.
• Graphs
• Pie or Bar Chart or Histogram
• Stem and Leaf Plot
• Frequency Polygon
13. Graphs
A pie chart is a circle that is divided
into sectors that represent categories.
The area of each sector is
proportional to the frequency of each
category.
14. Summarizing
the Data
Central Tendency (or Groups’ “Middle Values”)
Mean, Median, Mode
Variation (or Summary of Differences Within
Groups)
Range
Interquartile Range
Variance
Standard Deviation