- Introduction to Non-Parametric Tests
- Steps for Sign Test
- Steps for Wilcoxon Signed-Rank Test
- Comments About These Tests

Transcript – Unit 4B Case CQ Paired Samples B

- Non-Parametric Test: Wilcoxon Rank-Sum Test (Mann-Whitney U Test)

Transcript – Unit 4B Case CQ Two Independent Samples C

- Non-Parametric Procedure – Kruskal-Wallis Test

Transcript – Unit 4B Case CQ More than Two Independent Samples NP

This document linked from Details for Non-Parametric Alternatives

]]>From now on, we will also provide links to videos presented in the full course summary within the associated sections along with any other videos we may have on that topic.

Here we will look at Parts A and B of this course summary.

- Slides 1 – 7: Introduction

- Slides 8 – 16: One Categorical Variable

- Presentation
- Transcript
- Links for pepper plant example for one-sample t-test

This document linked from Wrap-Up for Unit 4A

]]>

There are currently no transcripts or captions for this video.

This document linked from Means (All Steps)

]]>- On Slide 5 notes we have P(Z < (63‐62)/1.5) = P( Z < 0.67) when it should be P(Z > (63‐62)/1.5) = P( Z > 0.67). It is correctly spoken in the video.
- The first part seems to end without completing the example on Slides 5 and 6. This is perplexing, if this happens elsewhere, please do let me know! Review the notes on slides 5 and 6 to fill in the missing discussion.

Transcript – All Parts – Normal Applications

Normal Applications – Part A (4:05)

Normal Applications – Part B (2:07)

Normal Applications – Part C (3:29)

This document linked from Normal Applications

]]>Transcript – Standard Normal Distribution

This document linked from Standard Normal Distribution

]]>View Lecture Slides with Transcript – Part E of Course Summary

- Slides 42 – 51: ANOVA

- The following videos are old but cover all of the important points :-)
- There are more details in these videos than in the course summary video above.
- There is no presentation available but the videos generally follow the course materials.

- Review of Methods Covered So Far
- Comparing Means With More Than Two Groups
- Idea of and Steps in the ANOVA F-Test
- There is one section where audio was added after the fact – it is easy to tell due to the quality and volume difference. Apologies for the lack of consistency and quality.

Transcript – Unit 4B Case CQ More than Two Independent Samples A

- Comments about ANOVA Conclusion
- Concept of Multiple Comparison Procedures
- Example 1: Academic Frustration
- Example 2: Flicker Frequency

Transcript – Unit 4B Case CQ More than Two Independent Samples B

This document linked from k > 2 Independent Samples

]]>View Lecture Slides with Transcript – Part D of Course Summary – All Parts

- Slides 26 – 41: Two-Sample t-test (equal and unequal variances assumed)

- The following videos are old but cover all of the important points :-)
- There are more details in these videos than in the course summary video above.
- There is no presentation available but the videos generally follow the course materials.
- Part C is presented in Details for Non-Parametric Alternatives instead and is skipped here on purpose.

- Introduction to Two Independent Samples

Transcript – Unit 4B Case CQ Two Independent Samples A

- Steps for Two Independent Samples T-test
- Test for Equality of Variances

Transcript – Unit 4B Case CQ Two Independent Samples B

- Example 1 – Looks vs. Personality Score and Gender

Transcript – Unit 4B Case CQ Two Independent Samples D

- Example 2 – BMI and Gender

Transcript – Unit 4B Case CQ Two Independent Samples E

This document linked from Two Independent Samples

]]>View Lecture Slides with Transcript – Part C of Course Summary

- Slides 17 – 25: One Quantitative Variable and Paired t-test

- The following videos are old but cover all of the important points :-)
- There are more details in these videos than in the course summary video above.
- There is no presentation available but the videos generally follow the course materials.
- Part B is presented in Details for Non-Parametric Alternatives instead and is skipped here on purpose.

- Analysis: Dependent (Paired/Matched) Samples
- Reduce paired samples to one sample by calculating the differences
- Software can use paired samples or differences
- Steps for Paired T-Test

Transcript – Unit 4B Case CQ Paired Samples A

- Example of Paired T-test – Drinking and Driving (Test and Confidence Interval)

Transcript – Unit 4B Case CQ Paired Samples C

This document is linked from Paired Samples

]]>This document linked from Case C→Q

]]>This document linked from Unit 4B: Inference for Relationships

]]>