ESMA 3102 - Introduction to Statistics II

Resma3.RData (Ver 3.1)

All these pages are also available as a single pdf here.

For a nice introduction to Statistics watch the PBS-NOVA episode Prediction by the Numbers

1 General Information

1.1. Syllabus

2 Computer and R

2.1. Introduction to Using the Computer and to R

2.2. Short List of Important R commands

2.3. R routines

2.4. Resma3 vs base R

3 Some Basic Ideas and Concepts

3.1. Introduction to ESMA 3102

3.2. Graphs

3.3. Outliers

3.4. Describing a Population: Probability Distributions

3.5. Confidence Intervals

3.6. Hypothesis Testing - Concept

3.7. Hypothesis Testing

3.8. The Lady Tasting Tea

3.9. Methods for one Variable

3.10. Bayesian Statistics

3.11. Exercises

4 Is there a relationship?

Method	Predictor	Response
4.1. Categorical Data Analysis	Categorical	Categorical
4.2. ANOVA	Categorical	Quantitative
4.3. Correlation	Quantitative	Quantitative

4.4. Correlation vs Causation

4.5. Non-Normal Residuals, No Equal Variance - Transformation

4.6. Non-Normal Residuals, No Equal Variance - Non-Parametric

4.7. Exercises

5 If so, what is there a relationship?

Method	Predictor	Response
5.1. ANOVA - Multiple Comparison	Categorical	Quantitative
5.2. Simple Regression	Quantitative	Quantitative

5.3. Assumptions of SLR

5.4. Hypothesis Tests in SLR

5.5. Prediction

5.6. Non-Normal Residuals, No Equal Variance

5.7. Non-Linear Models: Transformations and Polynomials

5.8. Coefficient of Determination and Over-fitting

5.9. Exercises

6 More Than One Predictor

Method	Predictors	Response
6.1. Categorical Data - Simpson’s Paradox	All Categorical	Categorical
6.2. ANOVA	All Categorical	Quantitative
6.3. Mulitple Regression	Quantitative	Quantitative
6.4. Regression with Dummy Variables	Quantitative/Categorical	Quantitative

6.5. Exercises