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.	Topic	size (MB)	time (min)
1	Harris Decima Poll and an Introduction to Likelihood (video, powerpoint show)	31, 55	22
2	A simple test of significance (video, powerpoint show)	5, 18	8
3	Using the t table (video, powerpoint show)	5, 16	6
4	Regression and the Striped Brown Cricket (video, powerpoint show)	9, 48	12
5	Causation and the Church of the Flying Spaghetti Monster (video, powerpoint show)	15, 51	25
6	What is a Confidence Interval? (video, powerpoint show)	13, 50	23
7	What is a Sampling Distribution(video, powerpoint show)	11, 31	14
9	Paired Confidence Intervals(video, powerpoint show)	12, 42	20
10	The empirical cdf and the qqplot(video, powerpoint show)	15, 42	20
11	Summarizing Numerical Data and boxplots part A(video, powerpoint show)	13, 35	16
12	Summarizing Numerical Data and boxplots part B(video, powerpoint show)	11, 31	15

STAT 230 Probability Videos

no.	Section	Topic	size (MB)	time (min)
1	2.1	Definition of odds, and examples of sample spaces	233	34
2	3.1	Counting techniques	128	22
3	3.1	Combinations	84	10
4	3.1	Bridge Hand example of calculation using combinations	30	6
5	3.1	Birthday Problem and geometric series	35	9
6	3.2	The Binomial Theorem	31	8
7	3.2	Multinomial series and other series	32	8
8		Waiting time problem: Return to Donnelly's example	48	9
9	4.1,4.2	The rules of probability	211	39
10	4.3	Intersection and Independence	22	6
11	4.3	Independence, part 2.	48	12
12	4.3	Mutual independence	71	16
13	4.4	An example using conditional probability	171	26
14	4.5	The multiplication rule	114	20
15	4.5	Bayes Rule	62	11
16	5.1	Definition of Random Variables	122	22
17	5.1	Cumulative Distribution Functions	158	25
18	5.2	The discrete uniform distribution	133	24
19	5.3	The Hypergeometric Distribution	10	16
20	5.4	The Binomial Distribution	32	8
21	5.4	A Binomial distribution example	81	10
22	5.4	The Statistician's stagger	77	10
23	5.5	The Negative binomial distribution	90	9
24	5.5,5.6	The Negative binomial 2	110	22
25	5.7	The Poisson distribution	112	22
26		A review of discrete distributions	130	15
27	5.8	The Poisson distribution 2	55	8
28	5.8	The Poisson process 1	100	10
29	5.8	The Poisson process 2	119	24
30	5.9	Combining Models	33	9
31		The Monty Hall Problem	44	7
32		Monty Hall 2	27	6
33	7.2	Expected Value	105	15
34	7.2,7.3	Expected value: example and interpretation through histograms	38	8
35	7.4	Expected value of Poisson	13	3
36	7.4	Expected value of binomial	25	7
37	7.2	Expected value of g(X)	69	15
38	7.2	Laws of Expected value and E(g(X)) part 2	40	11
39	7.4	Variance	43	11
40	7.4	Variance 2	115	20
41	7.4	Laws of Variance	47	8
42	7.4	Variance of the Poisson	38	8
43	7.4	Variance of binomial	28	6
44		Review of expected value and variance	78	20
45	7.5	Moment generating functions	83	19
46	7.5	Uses of the moment generating function	180	25
47	8.1,8.2	Joint distributions, independence and the multinomial distributions	29	41
48	8.2	Multinomial 2	23	27
49	8.3	Markov Chains	12	13
50	8.3	Markov Chains 2	22	27
51	8.4	Expected value of a Product	30	38
52	8.4	Covariance	17	22
53	8.4	Covariance 2 and correlation	29	38
54	8.5	Indicator Random variables and their use	30	39
55	9.1	Continuous Distributions	25	26
56	9.1	Continuous Distributions 2	27	31
57	9.1	Distribution of a Function of a Random Variable	47	16
58	9.1	Expected Value and Variance for Continuous Distributions	47	25
59	9.4	Inverse Transform: generating random variables with a given distribution	48	26
60	9.3	The exponential Distribution	36	26
61	9.3	The Exponential Distribution and the Poisson Process	40	23
62	9.5	The Normal Distribution	85	50
63	9.6	The Central Limit Theorem (video, powerpoint show)	18, 105	23
64	9.6	The normal distribution and the Central Limit Theorem	91	27
65	9.6	Normal approximations	68	27