Abbass Al Sharif, PhD

Assistant Professor of Clinical Data Sciences

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    T: +1-213-740-4826   F: +1-213-740-7313 

     

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    asharif at usc dot edu

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    SAS on Demand

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    Workshop Data Setup

     

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

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

     

     

    Slides

     

    1. Introduction

    2. Entering Data

    3. Managing Data

    4. Exploring Data

    5. Visualizing Data

    6. Analyzing Data

     

     

    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

    13.4 Questions to Ask

    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

     

     

     

     

     

     

     

    SAS Workshop 2015