Abbass Al Sharif, PhD
Assistant Professor of Clinical Data Sciences
SAS on Demand
-
Enter the required information and click Submit
-
A verification email will be sent; open the link located in the email (check your spam folder it maybe there)
-
Enter your email address and a password that follows the Password rules on the page. Accept the License, Click Create Account.
-
A page will open that will display your SAS OnDemand User ID
-
Sign in with your SAS OnDemand User ID and the password you just created
Workshop Data Setup
-
House Prices Dataset: http://tinyurl.com/housepricesdata
-
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
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