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Workshop Data Setup

1. House Prices Dataset: http://tinyurl.com/housepricesdata

2. Student Survey Dataset: http://tinyurl.com/studentdataset

Slides

Waiver Test for GSBA 524

Course Content (using R)

1. Introduction

1.1 What Is Statistics?

1.2 Previews

2. Data

2.1 Data Tables

2.2 Categorical and Numerical Data

2.3 Recoding and Aggregation

2.4 Time Series

2.5 Further Attributes of Data

3. Describing Categorical Data

3.1 Looking at Data

3.2 Charts of Categorical Data

3.3 The Area Principle

3.4 Mode and Median

4. Describing Numerical Data

4.1 Summaries of Numerical Variables

4.2 Histograms

4.3 Boxplot

4.4 Shape of a Distribution

5. Association between Categorical Variables

5.1 Contingency Tables

5.2 Lurking Variables and Simpson's Paradox

5.3 Strength of Association

6. Association between Quantitative Variables

6.1 Scatterplots

6.2 Association in Scatterplots

6.3 Measuring Association

6.4 Summarizing Association with a Line

6.5 Spurious Correlation

PART TWO: PROBABILITY

7. Probability

7.1 From Data to Probability

7.2 Rules for Probability

7.3 Independent Events

8. Conditional Probability

8.1 From Tables to Probabilities

8.2 Dependent Events

8.3 O rganizing Probabilities

8.4 O rder in Conditional Probabilities

9. Random Variables

9.1 Random Variables

9.2 Properties of Random Variables

9.3 Properties of Expected Values

9.4 Comparing Random Variables

10. Association between Random Variables

10.1 Portfolios and Random Variables

10.2 Joint Probability Distribution

10.3 Sums of Random Variables

10.4 Dependence between Random Variables

10.5 IID Random Variables

10.6 Weighted Sums

11. Probability Models for Counts

11.1 Random Variables for Counts

11.2 Binomial Model

11.3 Properties of Binomial Random Variables

11.4 Poisson Model

12. The Normal Probability Model

12.1 Normal Random Variable

12.2 The Normal Model

12.3 Percentiles

12.4 Departures from Normality

13. Samples and Surveys

13.1 Two Surprising Properties of Samples

13.2 Variation

13.3 Alternative Sampling Methods

14. Sampling Variation and Quality

14.1 Sampling Distribution of the Mean

14.2 Control Limits

14.3 Using a Control Chart

14.4 Control Charts for Variation

15. Confidence Intervals

15.1 Ranges for Parameters

15.2 Confidence Interval for the Mean

15.3 Interpreting Confidence Intervals

15.4 Manipulating Confidence Intervals

15.5 Margin of Error

16. Statistical Tests

16.1 Concepts of Statistical Tests

16.2 Testing the Proportion

16.3 Testing the Mean

16.4 Significance versus Importance

16.5 Confidence Interval or Test?

17. Comparison

17.1 Data for Comparisons

17.2 Two-Sample z-test for Proportions

17.3 Two-Sample Confidence Interval for Proportions

17.4 Two-Sample T-test

17.5 Confidence Interval for the Difference between Means

17.6 Paired Comparisons

18. Inference for Counts

18.1 Chi-Squared Tests

18.2 Test of Independence

18.3 General versus Specific Hypotheses

18.4 Tests of Goodness of Fit

PART FOUR: REGRESSION MODELS

19. Linear Patterns

19.1 Fitting a Line to Data

19.2 Interpreting the Fitted Line

19.3 Properties of Residuals

19.4 Explaining Variation

19.5 Conditions for Simple Regression

20. Curved Patterns

20.1 Detecting Nonlinear Patterns

20.2 Transformations

20.3 Reciprocal Transformation

20.4 Logarithm Transformation

21. The Simple Regression Model

21.1 The Simple Regression Model

21.2 Conditions for the SRM

21.3 Inference in Regression

21.4 Prediction Intervals

22. Regression Diagnostics

22.1 Changing Variation

22.2 Outliers

22.3 Dependent Errors and Time Series

23. Multiple Regression

23.1 The Multiple Regression Model

23.2 Interpreting Multiple Regression

23.3 Checking Conditions

23.4 Inference in Multiple Regression

23.5 Steps in Fitting a Multiple Regression

24. Building Regression Models

24.1 Identifying Explanatory Variables

24.2 Collinearity

24.3 Removing Explanatory Variables

25. Categorical Explanatory Variables

25.1 Two-Sample Comparisons

25.2 Analysis of Covariance

25.3 Checking Conditions

25.4 Interactions and Inference

25.5 Regression with Several Groups

26. Analysis of Variance

26.1 Comparing Several Groups

26.2 Inference in ANOVA Regression Models

26.3 Multiple Comparisons

26.4 Groups of Different Size