Introductory Statistics Notes

Introductory Statistics Notes

Jamie DeCoster Department of Psychology

University of Alabama 348 Gordon Palmer Hall

Box 870348 Tuscaloosa, AL 35487-0348

Phone: (205) 348-4431 Fax: (205) 348-8648 August 1, 1998

These were compiled from Jamie DeCoster's introductory statistics class at Purdue University. Textbook references refer to Moore's The Active Practice of Statistics. CD-ROM references refer to Velleman's ActivStats. If you wish to cite the contents of this document, the APA reference for them would be

DeCoster, J. (1998). Introductory Statistics Notes. Retrieved from

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ALL RIGHTS TO THIS DOCUMENT ARE RESERVED.

Contents

I Understanding Data

2

1 Introduction

3

2 Data and Measurement

4

3 The Distribution of One Variable

5

4 Measuring Center and Spread

7

5 Normal Distributions

10

II Understanding Relationships

12

6 Comparing Groups

13

7 Scatterplots

14

8 Correlation

15

9 Least-Squares Regression

16

9 Association vs. Causation

18

III Generating Data

19

10 Sample Surveys

20

11 Designed Experiments

21

IV Experience with Random Behavior

23

12 Randomness

24

13 Intuitive Probability

25

14 Conditional Probability

26

15 Random Variables

27

16 Sampling Distributions

28

i

V Statistical Inference

29

17 Estimating With Confidence

30

18 Confidence Intervals for a Mean

31

19 Testing Hypotheses

32

20 Tests for a Mean

34

VI Topics in Inference

36

21 Comparing Two Means

37

22 Inference for Proportions

39

23 Two-Way Tables

41

24 Inference for Regression

42

25 One-Way Analysis of Variance

45

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Part I

Understanding Data

2

Chapter 1

Introduction

? It is important to know how to understand statistics so that we can make the proper judgments when a person or a company presents us with an argument backed by data.

? Data are numbers with a context. To properly perform statistics we must always keep the meaning of our data in mind.

? You will spend several hours every day working on this course. You are responsible for material covered in lecture, as well as the contents of the textbook and the CD-ROM. You will have homework, CDROM, and reading assignments every day. It is important not to get behind in this course. A good work schedule would be: Review the notes from the previous day's lecture, and take care of any unfinished assignments. Attend the lecture. Attend the lab section. Do your homework. You will want to plan on staying on campus for this, as your homework will often require using the CD-ROM. Do the CD-ROM assignments. Do the Reading assignments. This probably seems like a lot of work, and it is. This is because we need to cover 15 weeks of material in 4 weeks during Maymester. Completing the course will not be easy, but I will try to make it as good an experience as I can.

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Chapter 2

Data and Measurement

? Statistics is primarily concerned with how to summarize and interpret variables. A variable is any characteristic of an object that can be represented as a number. The values that the variable takes will vary when measurements are made on different objects or at different times.

? Each time that we record information about an object we observe a case. We might include several different variables in the same case. For example, we might measure the height, weight, and hair color of a group of people in an experiment. We would have one case for each person, and that case would contain that person's height, weight, and hair color values. All of our cases put together is called our data set.

? Variables can be broken down into two types: Quantitative variables are those for which the value has numerical meaning. The value refers to a specific amount of some quantity. You can do mathematical operations on the values of quantitative variables (like taking an average). A good example would be a person's height. Categorical variables are those for which the value indicates different groupings. Objects that have the same value on the variable are the same with regard to some characteristic, but you can't say that one group has "more" or "less" of some feature. It doesn't really make sense to do math on categorical variables. A good example would be a person's gender.

? Whenever you are doing statistics it is very important to make sure that you have a practical understanding of the variables you are using. You should make sure that the information you have truly addresses the question that you want to answer. Specifically, for each variable you want to think about who is being measured, what about them is being measured, and why the researcher is conducting the experiment. If the variable is quantitative you should additionally make sure that you know what units are being used in the measurements.

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