00:20

You sir, are no normal distribution

29:26

1000 normal QQ plots in 30 minutes (n = 25, sampling from N(10,5^2))

03:57

What I envision a virtual work meeting at D2L (Brightspace) to be like (jk, obv)

11:27

Basic Probability: The Multiplication Rule

11:23

Linear Transformations (in a Descriptive Statistics Setting)

12:29

An Introduction to Boxplots

11:18

On average, what proportion of sample means would a randomly selected 95% CI for mu capture?

10:54

Deriving the mean and variance of the least squares slope estimator in simple linear regression

10:22

The Law of Total Probability

06:04

P(A) = P(A and B) + P(A and Bc)

12:13

Deriving the least squares estimators of the slope and intercept (simple linear regression)

04:23

Proof that if events A and B are independent, so are Ac and B (and A and Bc)

04:45

Proof that if two events are independent, so are their complements.

21:25

Independent Events (Basics of Probability: Independence of Two Events)

16:39

Conditional Probability Example Problems

15:16

Basics of Probability: Unions, Intersections, and Complements

05:46

Don't watch this! (A t test example where nearly everything I say is wrong)

07:40

De Morgan's Laws (in a probability context)

12:01

An Introduction to Conditional Probability

05:05

Are mutually exclusive events independent?

10:45

What Does Independence Look Like on a Venn Diagram?

11:20

The Expected Value and Variance of Discrete Random Variables

14:11

An Introduction to Discrete Random Variables and Discrete Probability Distributions

09:35

Inference for the Ratio of Variances: How Robust are These Procedures?

10:43

Inference for a Variance: How Robust are These Procedures?

10:18

The Sampling Distribution of the Ratio of Sample Variances

10:00

Inference for Two Variances: An Example of a Confidence Interval and a Hypothesis Test

12:00

The Sampling Distribution of the Sample Variance

04:29

Deriving a Confidence Interval for the Ratio of Two Variances

16:26

An Introduction to Inference for the Ratio of Two Variances

04:18

Deriving a Confidence Interval for a Variance (Assuming a Normally Distributed Population)

12:04

Inference for One Variance: An Example of a Confidence Interval and a Hypothesis Test

13:34

An Introduction to Inference for One Variance (Assuming a Normally Distributed Population)

13:23

Inference for Two Proportions: An Example of a Confidence Interval and a Hypothesis Test

15:10

An Introduction to Inference for Two Proportions

11:22

Confidence Intervals for a Proportion: Determining the Minimum Sample Size

09:49

The Sampling Distribution of the Sample Proportion

08:40

Inference for a Proportion: An Example of a Confidence Interval and a Hypothesis Test

10:27

An Introduction to Inference for a Proportion

14:40

Poisson or Not? (When does a random variable have a Poisson distribution?)

10:48

An Introduction to the Geometric Distribution

06:53

The Sample Variance: Why Divide by n-1?

06:58

Proof that the Sample Variance is an Unbiased Estimator of the Population Variance

08:08

Z-Scores (As a Descriptive Measure of Relative Standing)

08:32

Measures of Central Tendency

12:12

Measures of Variability (Variance, Standard Deviation, Range, Mean Absolute Deviation)

06:10

An Introduction to the t Distribution (Includes some mathematical details)

05:28

An Introduction to the Chi-Square Distribution

05:07

Deriving the Mean and Variance of the Sample Mean

14:51

Discrete Probability Distributions: Example Problems (Binomial, Poisson, Hypergeometric, Geometric)

06:21

Overview of Some Discrete Probability Distributions (Binomial,Geometric,Hypergeometric,Poisson,NegB)

09:03

An Introduction to the Poisson Distribution

14:11

An Introduction to the Binomial Distribution

11:40

The Sampling Distribution of the Sample Mean

15:35

An Introduction to the Hypergeometric Distribution

10:28

Standardizing Normally Distributed Random Variables

09:17

The Poisson Distribution: Mathematically Deriving the Mean and Variance

13:54

The Binomial Distribution: Mathematically Deriving the Mean and Variance

03:12

The Bernoulli Distribution: Deriving the Mean and Variance

13:40

Calculating Power and the Probability of a Type II Error (A Two-Tailed Example)