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Table of Contents

I Randomness
1 Outcomes, Events, and Sample Spaces
1.1 Introduction
1.2 Complements and DeMorgan's Laws
1.3 Exercises
1.3.1 Practice
1.3.2 Extensions
1.3.3 Advanced

2 Probability
2.1 Introduction
2.2 Equally-Likely Events
2.3 Complementary Probabilities; Probabilities of Subsets
2.4 Inclusion-Exclusion
2.5 More Examples of Probabilities of Events
2.6 Exercises
2.6.1 Practice
2.6.2 Extensions
2.6.3 Advanced

3 Independent Events
3.1 Introduction
3.2 Some Nice Facts About Independence
3.3 Probability of Good Occurring Before Bad
3.4 Exercises
3.4.1 Practice
3.4.2 Extensions
3.4.3 Advanced

4 Conditional Probability
4.1 Introduction
4.2 Distributive Laws
4.3 Conditional Probabilities Satisfy the Standard Probability Axioms
4.4 Exercises
4.4.1 Practice
4.4.2 Extensions
4.4.3 Advanced 

5 Bayes' Theorem
5.1 Introduction to Versions of Bayes' Theorem
5.2 Multiplication with Conditional Probabilities 
5.3 Exercises
5.3.1 Practice
5.3.2 Extensions
5.3.3 Advanced

6 Review of Randomness
6.1 Summary of Randomness
6.2 Exercises

II Discrete Random Variables
7 Discrete Versus Continuous Random Variables
7.1 Introduction
7.2 Examples
7.3 Exercises
7.3.1 Practice
7.3.2 Extensions
7.3.3 Advanced 

8 Probability Mass Functions and CDFs
8.1 Introduction 
8.2 Examples 
8.3 Properties of the Mass and CDF 
8.4 More Examples  
8.5 Exercises 
8.5.1 Practice
8.5.2 Extensions
8.5.3 Advanced 

9 Independence and Conditioning
9.1 Joint Probability Mass Functions 
9.2 Independent Random Variables
9.3 Three or More Random Variables That Are Independent 
9.4 Conditional Probability Mass Functions 
9.5 Exercises 
9.5.1 Practice
9.5.2 Extensions
9.5.3 Advanced 

10 Expected Values of Discrete Random Variables
10.1 Introduction 
10.2 Examples
10.3 Exercises 
10.3.1 Practice
10.3.2 Extensions

11 Expected Values of Sums of Random Variables
11.1 Introduction
11.2 Examples  
11.3 Exercises 
11.3.1 Practice
11.3.2 Extensions

12 Variance of Discrete Random Variables
12.1 Introduction. 
12.2 Variance 
12.3 Five Friendly Facts with Independence
12.4 Exercises 
12.4.1 Practice
12.4.2 Extensions
12.4.3 Advanced 

13 Review of Discrete Random Variables
13.1 Summary of Discrete Random Variables
13.2 Exercises
 
III Named Discrete Random Variables
14 Bernoulli Random Variables
14.1 Introduction 
14.2 Examples
14.3 Exercises
14.3.1 Practice
14.3.2 Extensions
14.3.3 Advanced 

15 Binomial Random Variables
15.1 Introduction 
15.2 Examples 
15.3 Exercises 
15.3.1 Practice
15.3.2 Extensions
15.3.3 Advanced 

16 Geometric Random Variables
6.1 Introduction
16.2 Special Features of the Geometric Distribution 
16.3 The Number of Failures 
16.4 Geometric Memoryless Property 
16.5 Random Variables That Are Not Geometric 
16.6 Exercises
16.6.1 Practice
16.6.2 Extensions
16.6.3 Advanced 

17 Negative Binomial Random Variables
17.1 Introduction 
17.2 Examples 
17.3 Exercises 
17.3.1 Practice
17.3.2 Extensions

18 Poisson Random Variables
18.1 Introduction 
18.2 Sums of Independent Poisson Random Variables
18.3 Using the Poisson as an Approximation to the Binomial
18.4 Exercises 
18.4.1 Practice
18.4.2 Extensions 
18.4.3 Advanced 

19 Hypergeometric Random Variables
19.1 Introduction
19.2 Examples
19.3 Using the Binomial as an Approximation to the Hypergeometric
19.4 Exercises
19.4.1 Practice
19.4.2 Extensions

20 Discrete Uniform Random Variables
20.1 Introduction
20.2 Examples
20.3 Exercises
20.3.1 Practice
20.3.2 Extensions
20.3.3 Advanced

21 Review of Named Discrete Random Variables
21.1 Summing up: How do you tell all these random variables apart?
21.2 Exercises
21.3 Review Problems

IV Counting
22 Introduction to Counting
22.1 Introduction
22.2 Sampling With Versus Without Replacement; With Versus Without Regard to Order 
22.3 Counting: Seating Arrangements
22.4 Exercises
22.4.1 Practice
22.4.2 Extensions
22.4.3 Seating Arrangement Problems

23 Two Case Studies in Counting
23.1 Poker Hands 
23.1.1 Straight Flush
23.1.2 Four Of A Kind
23.1.3 Full House
23.1.4 Flush 
23.1.5 Straight
23.1.6 Three Of A Kind
23.1.7 Two Pair
23.1.8 One Pair
23.2 Yahtzee
23.2.1 Upper Section
23.2.2 Three Of A Kind
23.2.3 Four Of A Kind
23.2.4 Full House
23.2.5 Small Straight
23.2.6 Large Straight
23.2.7 Yahtzee

V Continuous Random Variables
24 Continuous Random Variables and PDFs
24.1 Introduction
24.2 Examples
24.3 Exercises
24.3.1 Practice
24.3.2 Extensions
24.3.3 Advanced

25 Joint Densities
25.1 Introduction
25.2 Examples 
25.3 Exercises 
25.3.1 Practice
25.3.2 Extensions
25.3.3 Advanced

26 Independent Continuous Random Variables
26.1 Introduction
26.2 Examples 
26.3 Exercises 
26.3.1 Practice 
26.3.2 Extensions
26.3.3 Advanced

27 Conditional Distributions
27.1 Introduction
27.2 Examples 
27.3 Exercises 
27.3.1 Practice 
27.3.2 Extensions

28 Expected Values of Continuous Random Variables
28.1 Introduction
28.2 Some Generalizations about Expected Values
28.3 Some Applied Problems with Expected Values
28.4 Exercises 
28.4.1 Practice
28.4.2 Extensions 
28.4.3 Advanced 

29 Variance of Continuous Random Variables
29.1 Variance of a Continuous Random Variable
29.2 Expected Values of Functions of One Continuous Random Variable
29.3 Expected Values of Functions of Two Continuous Random Variables
29.4 More Friendly Facts about Continuous Random Variables
29.5 Exercises
29.5.1 Practice
29.5.2 Extensions
29.5.3 Advanced

30 Review of Continuous Random Variables
30.1 Summary of Continuous Random Variables
30.2 Exercises

VI Named Continuous Random Variables
31 Continuous Uniform Random Variables
31.1 Introduction 
31.2 Examples 
31.3 Linear Scaling of a Uniform Random Variable
31.4 Exercises 
31.4.1 Practice
31.4.2 Extensions
31.4.3 Advanced 

32 Exponential Random Variables
32.1 Introduction 
32.2 Average and Variance
32.3 Properties of Exponential Random Variables
32.3.1 Complement of the CDF
32.3.2 Memoryless Property of Exponential Random Variables
32.3.3 Minimum of Independent Exponential Random Variables
32.3.4 Poisson Process
32.3.5 Moments of an Exponential Random Variable (Optional)
32.4 Exercises .
32.4.1 Practice
32.4.2 Extensions
32.4.3 Advanced

33 Gamma Random Variables
33.1 Introduction
33.2 Examples
33.3 Exercises
33.3.1 Practice
33.3.2 Extensions
33.3.3 Advanced

34 Beta Random Variables
34.1 Introduction 
34.2 Examples 
34.3 Exercises 
34.3.1 Practice
34.3.2 Extensions

35 Normal Random Variables
35.1 Introduction 
35.2 Using the Normal Distribution: Scaling and Transforming to Standard Normal
35.3 \Backwards" Normal Problems
35.4 Summary: How to Distinguish a \Forward" Versus \Backwards" Normal Problem?
35.5 Exercises
35.5.1 Practice
35.5.2 Extensions
35.5.3 Advanced

36 Sums of Independent Normal Random Variables
36.1 The Sum of Independent Normal Random Variables is Normally Distributed
36.2 Why the Sum of Independent Normals is Normal Too (Optional)
36.3 Exercises
36.3.1 Practice
36.3.2 Extensions 
36.3.3 Advanced 

37 Central Limit Theorem
37.1 Introduction
37.2 Laws of Large Numbers
37.3 Central Limit Theorem 
37.4 Applications of the Central Limit Theorem to Sums of Continuous Random Variables
37.5 Applications of the Central Limit Theorem to Sums of Discrete Random Variables
37.6 Normal Approximations to Binomial Random Variables
37.7 Normal Approximations to Poisson Random Variables 
37.8 Exercises 
37.8.1 Practice 
37.8.2 Extensions
38 Review of Named Continuous Random Variables
38.1 Summing up: How do you tell all these random variables apart?
38.2 Exercises

VII Additional Topics
39 Variance of Sums; Covariance; Correlation
39.1 Introduction 
39.2 Motivation for Covariance
39.3 Properties of the Covariance
39.4 Examples of Covariance 
39.5 Linearity of the Covariance 
39.6 Correlation
39.7 Exercises 
39.7.1 Practice
39.7.2 Extensions
39.7.3 Advanced

40 Conditional Expectation
40.1 Introduction
40.2 Examples 
40.3 Exercises 
40.3.1 Practice
40.3.2 Extensions
40.3.3 Advanced.

41 Markov and Chebyshev Inequalities
41.1 Introduction
41.2 Markov Inequality
41.3 Chebyshev Inequality
41.4 Exercises
41.4.1 Practice
41.4.2 Extensions

42 Order Statistics
42.1 Introduction
42.2 Examples
42.3 Joint Density and Joint CDF of Order Statistics
42.4 Exercises
42.4.1 Practice
42.4.2 Extensions  

43 Moment Generating Functions
43.1 A Brief Introduction to Generating Functions
43.2 Moment Generating Functions
43.3 Moment Generating Functions of Discrete Random Variables
43.4 Moment Generating Functions of Continuous Random Variables
43.5 Appendix: Building a Generating Function
43.6 Exercises

44 Transformations of One or Two Random Variables
44.1 Distribution of a Function of One Continuous Random Variable
44.2 Joint Density of Two Random Variables That Are Functions of Another Pair of Random Variables
44.3 Exercises
44.3.1 Practice 
44.3.2 Extensions
44.3.3 Advanced
45 Review Questions for All Chapters

Authors

Mark Ward

Mark Daniel Ward is an Associate Professor of Statistics at Purdue University. He has held visiting faculty positions at The George Washington University, the University of Maryland, the University of Paris 13, and a lecturer position at the University of Pennsylvania. He received his Ph.D. from Purdue University in Mathematics with Specialization in Computational Science (2005), M.S. in Applied Mathematics Science from the University of Wisconsin, Madison (2003), and B.S. in Mathematics and Computer Science from Denison University (1999). His research interests include probabilistic, combinatorial, and analytic techniques for the analysis of algorithms and data structures. Since 2008, he has been the Undergraduate Chair in Statistics at Purdue, and the Associate Director for Actuarial Science. Dr. Ward is currently the Principal Investigator for the NSF grant "MCTP: Sophomore Transitions: Bridges into a Statistics Major and Big Data Research Experiences via Learning Communities" (NSF-DMS #1246818, 2013-2018). He is also an Associate Director of the Center for Science of Information (NSF-CCF #0939370, 2010-2015).

Ellen Gundlach

Ellen Gundlach has been teaching introductory statistics and probability classes at Purdue University as a continuing lecturer since 2002, with prior experience teaching mathematics or chemistry classes at Purdue, Ivy Tech Community College of Indiana, The Ohio State University, and Florida State University.  She is an associate editor of CAUSEweb and editor of the MERLOT Statistics Board.  Her research interests include K12 outreach activities (ASA’s first Hands-on Statistics Activity grand prize winner in 2010), online and hybrid teaching (Indiana Council for Continuing Education’s Course of the Year award in 2011), T.A. training, academic misconduct, statistical literacy, and using social media in statistics courses. She enjoys spending time with her sons Philip and Callum, playing the flute with several local groups, and supporting (and formerly skating with) the Lafayette Brawlin’ Dolls roller derby team. 

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