Probability and Statistics Videos

STAT 230 & STAT 231

The following powerpoint shows and videos are housed in this temporary location pending the development of a permanent home at the University of Waterloo. For additional information about these videos, or for assistance in developing a powerpoint show of your own, please contact Don McLeish, Department of Statistics and Actuarial Science, University of Waterloo, dlmcleis@uwaterloo.ca.

For assistance posting videos to this site please contact pkates@uwaterloo.ca.

STAT 231 Statistics Videos

no.Topicsize (MB)time (min)
1Harris Decima Poll and an Introduction to Likelihood (video, powerpoint show)31, 5522
2A simple test of significance (video, powerpoint show)5, 188
3Using the t table (video, powerpoint show)5, 166
4Regression and the Striped Brown Cricket (video, powerpoint show)9, 4812
5Causation and the Church of the Flying Spaghetti Monster (video, powerpoint show)15, 5125
6What is a Confidence Interval? (video, powerpoint show)13, 5023
7What is a Sampling Distribution(video, powerpoint show)11, 3114
9Paired Confidence Intervals(video, powerpoint show)12, 4220
10The empirical cdf and the qqplot(video, powerpoint show)15, 4220
11Summarizing Numerical Data and boxplots part A(video, powerpoint show)13, 3516
12Summarizing Numerical Data and boxplots part B(video, powerpoint show)11, 3115

STAT 230 Probability Videos

no.SectionTopicsize (MB)time (min)
1 2.1 Definition of odds, and examples of sample spaces233 34
2 3.1 Counting techniques128 22
3 3.1 Combinations84 10
4 3.1 Bridge Hand example of calculation using combinations30 6
5 3.1 Birthday Problem and geometric series35 9
6 3.2 The Binomial Theorem31 8
7 3.2 Multinomial series and other series32 8
8 Waiting time problem: Return to Donnelly's example48 9
9 4.1,4.2 The rules of probability211 39
10 4.3 Intersection and Independence22 6
11 4.3 Independence, part 2.48 12
12 4.3 Mutual independence71 16
13 4.4 An example using conditional probability171 26
14 4.5 The multiplication rule114 20
15 4.5 Bayes Rule62 11
16 5.1 Definition of Random Variables122 22
17 5.1 Cumulative Distribution Functions158 25
18 5.2 The discrete uniform distribution133 24
19 5.3 The Hypergeometric Distribution10 16
20 5.4 The Binomial Distribution32 8
21 5.4 A Binomial distribution example81 10
22 5.4 The Statistician's stagger77 10
23 5.5 The Negative binomial distribution90 9
24 5.5,5.6 The Negative binomial 2110 22
25 5.7 The Poisson distribution112 22
26 A review of discrete distributions130 15
27 5.8 The Poisson distribution 255 8
28 5.8 The Poisson process 1100 10
29 5.8 The Poisson process 2119 24
30 5.9 Combining Models33 9
31 The Monty Hall Problem44 7
32 Monty Hall 227 6
33 7.2 Expected Value105 15
34 7.2,7.3 Expected value: example and interpretation through histograms38 8
35 7.4 Expected value of Poisson13 3
36 7.4 Expected value of binomial25 7
37 7.2 Expected value of g(X)69 15
38 7.2 Laws of Expected value and E(g(X)) part 240 11
39 7.4 Variance43 11
40 7.4 Variance 2115 20
41 7.4 Laws of Variance47 8
42 7.4 Variance of the Poisson38 8
43 7.4 Variance of binomial28 6
44 Review of expected value and variance78 20
45 7.5 Moment generating functions83 19
46 7.5 Uses of the moment generating function180 25
47 8.1,8.2 Joint distributions, independence and the multinomial distributions29 41
48 8.2 Multinomial 223 27
49 8.3 Markov Chains12 13
50 8.3 Markov Chains 222 27
51 8.4 Expected value of a Product30 38
52 8.4 Covariance17 22
53 8.4 Covariance 2 and correlation29 38
54 8.5 Indicator Random variables and their use30 39
55 9.1 Continuous Distributions25 26
56 9.1 Continuous Distributions 227 31
57 9.1 Distribution of a Function of a Random Variable47 16
58 9.1 Expected Value and Variance for Continuous Distributions47 25
59 9.4 Inverse Transform: generating random variables with a given distribution48 26
60 9.3 The exponential Distribution36 26
61 9.3 The Exponential Distribution and the Poisson Process40 23
62 9.5 The Normal Distribution85 50
639.6 The Central Limit Theorem (video, powerpoint show)18, 10523
64 9.6 The normal distribution and the Central Limit Theorem91 27
65 9.6 Normal approximations68 27