À propos de ce cours

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This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression.

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Dates limites flexibles

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Niveau intermédiaire

Approx. 23 heures pour terminer

Recommandé : 10 hours/week...

Anglais

Sous-titres : Anglais

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau intermédiaire

Approx. 23 heures pour terminer

Recommandé : 10 hours/week...

Anglais

Sous-titres : Anglais

Programme du cours : ce que vous apprendrez dans ce cours

Semaine

1

1 heure pour terminer

Course Overview and Data Setup

In this module you learn about the course and the data you analyze in this course. Then you set up the data you need to do the practices in the course.

2 vidéos (Total 13 min), 5 lectures

2 vidéos

Welcome and Meet the Instructor1 min

Demo: Exploring Ames Housing Data10 min

5 lectures

Learner Prerequisites10 min

Choosing and Setting Up SAS Software for this Course10 min

Follow These Instructions to Set Up Data for This Course (REQUIRED)30 min

Completing Demos and Practices10 min

Using Forums and Getting Help10 min

3 heures pour terminer

Introduction and Review of Concepts

In this module you learn about the models required to analyze different types of data and the difference between explanatory vs predictive modeling. Then you review fundamental statistical concepts, such as the sampling distribution of a mean, hypothesis testing, p-values, and confidence intervals. After reviewing these concepts, you apply one-sample and two-sample t tests to data to confirm or reject preconceived hypotheses.

17 vidéos (Total 41 min), 2 lectures, 9 quiz

17 vidéos

Overview1 min

Statistical Modeling: Types of Variables1 min

Overview of Models3 min

Explanatory versus Predictive Modeling1 min

Population Parameters and Sample Statistics1 min

Normal (Gaussian) Distribution2 min

Standard Error of the Mean51s

Confidence Intervals2 min

Statistical Hypothesis Test4 min

p-Value: Effect Size and Sample Size Influence3 min

Scenario48s

Performing a t Test4 min

Demo: Performing a One-Sample t Test Using PROC TTEST3 min

Scenario1 min

Assumptions for the Two-Sample t Test2 min

Testing for Equal and Unequal Variances2 min

Demo: Performing a Two-Sample t Test Using PROC TTEST4 min

2 lectures

Parameters and Statistics10 min

Normal Distribution10 min

9 exercices pour s'entraîner

Question 1.015 min

Question 1.025 min

Question 1.035 min

Question 1.045 min

Question 1.055 min

Practice - Using PROC TTEST to Perform a One-Sample t Test20 min

Question 1.065 min

Practice - Using PROC TTEST to Compare Groups20 min

Introduction and Review of Concepts30 min

Semaine

2

4 heures pour terminer

ANOVA and Regression

In this module you learn to use graphical tools that can help determine which predictors are likely or unlikely to be useful. Then you learn to augment these graphical explorations with correlation analyses that describe linear relationships between potential predictors and our response variable. After you determine potential predictors, tools like ANOVA and regression help you assess the quality of the relationship between the response and predictors.

29 vidéos (Total 70 min), 2 lectures, 14 quiz

29 vidéos

Overview1 min

Scenario58s

Identifying Associations in ANOVA with Box Plots1 min

Demo: Exploring Associations Using PROC SGPLOT1 min

Identifying Associations in Linear Regression with Scatter Plots1 min

Demo: Exploring Associations Using PROC SGSCATTER2 min

Scenario56s

The ANOVA Hypothesis1 min

Partitioning Variability in ANOVA2 min

Coefficient of Determination1 min

F Statistic and Critical Values1 min

The ANOVA Model2 min

Demo: Performing a One-Way ANOVA Using PROC GLM6 min

Scenario49s

Multiple Comparison Methods2 min

Tukey's and Dunnett's Multiple Comparison Methods1 min

Diffograms and Control Plots1 min

Demo: Performing a Post Hoc Pairwise Comparison Using PROC GLM6 min

Scenario53s

Using Correlation to Measure Relationships between Continuous Variables1 min

Hypothesis Testing for a Correlation1 min

Avoiding Common Errors When Interpreting Correlations5 min

Demo: Producing Correlation Statistics and Scatter Plots Using PROC CORR6 min

Scenario45s

The Simple Linear Regression Model1 min

How SAS Performs Simple Linear Regression1 min

Comparing the Regression Model to a Baseline Model2 min

Hypothesis Testing and Assumptions for Linear Regression1 min

Demo: Performing Simple Linear Regression Using PROC REG7 min

2 lectures

What Does a CLASS Statement Do?10 min

Correlation Analysis and Model Building10 min

14 exercices pour s'entraîner

Question 2.015 min

Question 2.025 min

Question 2.035 min

Question 2.045 min

Practice - Performing a One-Way ANOVA20 min

Question 2.055 min

Question 2.065 min

Practice - Using PROC GLM to Perform Post Hoc Parwise Comparisons20 min

Question 2.075 min

Question 2.085 min

Practice - Describing the Relationship between Continuous Variables20 min

Question 2.095 min

Practice - Using PROC REG to Fit a Simple Linear Regression Model20 min

ANOVA and Regression30 min

Semaine

3

2 heures pour terminer

More Complex Linear Models

In this module you expand the one-way ANOVA model to a two-factor analysis of variance and then extend simple linear regression to multiple regression with two predictors. After you understand the concepts of two-way ANOVA and multiple linear regression with two predictors, you'll have the skills to fit and interpret models with many variables.

13 vidéos (Total 43 min), 1 lecture, 5 quiz

13 vidéos

Overview1 min

Scenario1 min

Applying the Two-Way ANOVA Model3 min

Demo: Performing a Two-Way ANOVA Using PROC GLM7 min

Interactions3 min

Demo: Performing a Two-Way ANOVA With an Interaction Using PROC GLM5 min

Demo: Performing Post-Processing Analysis Using PROC PLM4 min

Scenario44s

The Multiple Linear Regression Model2 min

Hypothesis Testing for Multiple Regression1 min

Multiple Linear Regression versus Simple Linear Regression2 min

Adjusted R-Square1 min

Demo: Fitting a Multiple Linear Regression Model Using PROC REG7 min

1 lecture

The STORE Statement10 min

5 exercices pour s'entraîner

Question 3.015 min

Practice - Performing a Two-Way ANOVA Using PROC GLM20 min

Question 3.025 min

Practice - Performing Multiple Regression Using PROC REG20 min

More Complex Linear Models30 min

2 heures pour terminer

Model Building and Effect Selection

In this module you explore several tools for model selection. These tools help limit the number of candidate models so that you can choose an appropriate model that's based on your expertise and research priorities.

11 vidéos (Total 28 min), 3 lectures, 4 quiz

11 vidéos

Overview47s

Scenario1 min

Approaches to Selecting Models2 min

The All-Possible Regressions Approach to Model Building1 min

The Stepwise Selection Approach to Model Building3 min

Interpreting p-Values and Parameter Estimates2 min

Demo: Performing Stepwise Regression Using PROC GLMSELECT7 min

Scenario37s

Information Criteria2 min

Adjusted R-Square and Mallows' Cp56s

Demo: Performing Model Selection Using PROC GLMSELECT5 min

3 lectures

Activity - Optional Stepwise Selection Method Code10 min

Information Criteria Penalty Components10 min

All-Possible Selection

4 exercices pour s'entraîner

Question 4.015 min

Practice - Using PROC GLMSELECT to Perform Stepwise Selection20 min

Practice - Using PROC GLMSELECT to Perform Other Model Selection Techniques20 min

Model Building and Effect Selection20 min

Semaine

4

3 heures pour terminer

Model Post-Fitting for Inference

In this module you learn to verify the assumptions of the model and diagnose problems that you encounter in linear regression. You learn to examine residuals, identify outliers that are numerically distant from the bulk of the data, and identify influential observations that unduly affect the regression model. Finally, you learn to diagnose collinearity to avoid inflated standard errors and parameter instability in the model.

18 vidéos (Total 46 min), 7 quiz

18 vidéos

Overview1 min

Scenario40s

Assumptions for Regression2 min

Verifying Assumptions Using Residual Plots3 min

Demo: Examining Residual Plots Using PROC REG5 min

Scenario47s

Identifying Influential Observations1 min

Checking for Outliers with STUDENT Residuals1 min

Checking for Influential Observations2 min

Detecting Influential Observations with DFBETAS1 min

Demo: Looking for Influential Observations Using PROC GLMSELECT and PROC REG5 min

Demo: Examining the Influential Observations Using PROC PRINT6 min

Handling Influential Observations1 min

Scenario38s

Exploring Collinearity1 min

Visualizing Collinearity2 min

Demo: Calculating Collinearity Diagnostics Using PROC REG5 min

Using an Effective Modeling Cycle1 min

7 exercices pour s'entraîner

Practice: Using PROC REG to Examine Residuals20 min

Question 5.015 min

Practice: Using PROC REG to Generate Potential Outliers20 min

Question 5.025 min

Question 5.035 min

Practice: Using PROC REG to Assess Collinearity20 min

Model Post-Fitting for Inference30 min

2 heures pour terminer

Model Building for Scoring and Prediction

In this module you learn how to transition from inferential statistics to predictive modeling. Instead of using p-values, you learn about assessing models using honest assessment. After you choose the best performing model, you learn about ways to deploy the model to predict new data.

11 vidéos (Total 27 min), 1 lecture, 4 quiz

11 vidéos

Overview1 min

Scenario27s

Predictive Modeling Terminology1 min

Model Complexity51s

Building a Predictive Model2 min

Model Assessment and Selection1 min

Demo: Building a Predictive Model Using PROC GLMSELECT10 min

Scenario29s

Preparing for Scoring1 min

Methods of Scoring1 min

Demo: Scoring Data Using PROC PLM4 min

1 lecture

Partitioning a Data Set Using PROC GLMSELECT10 min

4 exercices pour s'entraîner

Question 6.015 min

Practice: Building a Predictive Model Using PROC GLMSELECT20 min

Practice: Scoring Using the SCORE Statement in PROC GLMSELECT20 min

Model Building for Scoring and Prediction30 min

Semaine

5

4 heures pour terminer

Categorical Data Analysis

In this module you look for associations between predictors and a binary response using hypothesis tests. Then you build a logistic regression model and learn about how to characterize the relationship between the response and predictors. Finally, you learn how to use logistic regression to build a model, or classifier, to predict unknown cases.

25 vidéos (Total 73 min), 18 quiz

25 vidéos

Overview1 min

Scenario59s

Associations between Categorical Variables2 min

Demo: Examining the Distribution of Categorical Variables Using PROC FREQ and PROC UNIVARIATE6 min

Scenario43s

The Pearson Chi-Square Test3 min

Odds Ratios4 min

Demo: Performing a Pearson Chi-Square Test of Association Using PROC FREQ5 min

Scenario22s

The Mantel-Haenszel Chi-Square Test1 min

The Spearman Correlation Statistic40s

Demo: Detecting Ordinal Associations Using PROC FREQ2 min

Scenario49s

Modeling a Binary Response3 min

Demo: Fitting a Binary Logistic Regression Model Using PROC LOGISTIC6 min

Interpreting the Odds Ratio3 min

Comparing Pairs to Assess the Fit of a Logistic Regression Model4 min

Scenario50s

Specifying a Parameterization Method4 min

Demo: Fitting a Multiple Logistic Regression Model with Categorical Predictors Using PROC LOGISTIC7 min

Scenario35s

Interactions between Variables2 min

Demo: Fitting a Multiple Logistic Regression Model with Interactions Using PROC LOGISTIC3 min

Demo: Fitting a Multiple Logistic Regression Model with All Odds Ratios Using PROC LOGISTIC3 min

Demo: Generating Predictions Using PROC PLM2 min

18 exercices pour s'entraîner

Question 7.015 min

Question 7.025 min

Practice: Using PROC FREQ to Examine Distributions20 min

Question 7.035 min

Question 7.045 min

Question 7.055 min

Question 7.065 min

Practice: Using PROC FREQ to Perform Tests and Measures of Association20 min

Question 7.075 min

Question 7.085 min

Practice: Using PROC LOGISTIC to Perform a Binary Logistic Regression Analysis20 min

Question 7.095 min

Question 7.105 min

Practice: Using PROC LOGISTIC to Perform a Multiple Logistic Regression Analysis with Categorical Variables20 min

Question 7.115 min

Question 7.125 min

Practice: Using PROC LOGISTIC to Perform Backward Elimination and PROC PLM to Generate Predictions20 min

Categorical Data Analysis30 min

Enseignant

Jordan Bakerman

Analytical Training Consultant

Education

Foire Aux Questions

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