4.7
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277 ratings

Jordan Bakerman

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Statistics with SAS

SAS

About this Course

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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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Coursera Labs

Includes hands on learning projects.

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Intermediate Level

Approx. 20 hours to complete

English

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Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Intermediate Level

Approx. 20 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.66/5 (97 Ratings)

Jordan Bakerman

Analytical Training Consultant

Education

42,232 Learners

4 Courses

Offered by

SAS

Through innovative software and services, SAS empowers and inspires customers around the world to transform data into intelligence. SAS is a trusted analytics powerhouse for organizations seeking immediate value from their data. A deep bench of analytics solutions and broad industry knowledge keep our customers coming back and feeling confident. With SAS®, you can discover insights from your data and make sense of it all. Identify what’s working and fix what isn’t. Make more intelligent decisions. And drive relevant change.

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Syllabus - What you will learn from this course

Content Rating95%(1,823 ratings)

Week1

Week 1

1 hour to complete

Course Overview and Data Setup

1 hour to complete

2 videos (Total 13 min), 4 readings

3 hours to complete

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.

3 hours to complete

17 videos (Total 41 min), 2 readings, 9 quizzes

17 videos

Overview1mStatistical Modeling: Types of Variables1mOverview of Models3mExplanatory versus Predictive Modeling1mPopulation Parameters and Sample Statistics1mNormal (Gaussian) Distribution2mStandard Error of the Mean51sConfidence Intervals2mStatistical Hypothesis Test4mp-Value: Effect Size and Sample Size Influence3mScenario48sPerforming a t Test4mDemo: Performing a One-Sample t Test Using PROC TTEST3mScenario1mAssumptions for the Two-Sample t Test2mTesting for Equal and Unequal Variances2mDemo: Performing a Two-Sample t Test Using PROC TTEST4m

2 readings

Parameters and Statistics10mNormal Distribution10m

9 practice exercises

Question 1.015mQuestion 1.025mQuestion 1.035mQuestion 1.045mQuestion 1.055mPractice - Using PROC TTEST to Perform a One-Sample t Test20mQuestion 1.065mPractice - Using PROC TTEST to Compare Groups20mIntroduction and Review of Concepts30m

Week2

Week 2

4 hours to complete

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.

4 hours to complete

29 videos (Total 70 min), 2 readings, 14 quizzes

29 videos

Overview1mScenario58sIdentifying Associations in ANOVA with Box Plots1mDemo: Exploring Associations Using PROC SGPLOT1mIdentifying Associations in Linear Regression with Scatter Plots1mDemo: Exploring Associations Using PROC SGSCATTER2mScenario56sThe ANOVA Hypothesis1mPartitioning Variability in ANOVA2mCoefficient of Determination1mF Statistic and Critical Values1mThe ANOVA Model2mDemo: Performing a One-Way ANOVA Using PROC GLM6mScenario49sMultiple Comparison Methods2mTukey's and Dunnett's Multiple Comparison Methods1mDiffograms and Control Plots1mDemo: Performing a Post Hoc Pairwise Comparison Using PROC GLM6mScenario53sUsing Correlation to Measure Relationships between Continuous Variables1mHypothesis Testing for a Correlation1mAvoiding Common Errors When Interpreting Correlations5mDemo: Producing Correlation Statistics and Scatter Plots Using PROC CORR6mScenario45sThe Simple Linear Regression Model1mHow SAS Performs Simple Linear Regression1mComparing the Regression Model to a Baseline Model2mHypothesis Testing and Assumptions for Linear Regression1mDemo: Performing Simple Linear Regression Using PROC REG7m

2 readings

What Does a CLASS Statement Do?10mCorrelation Analysis and Model Building10m

14 practice exercises

Question 2.015mQuestion 2.025mQuestion 2.035mQuestion 2.045mPractice - Performing a One-Way ANOVA20mQuestion 2.055mQuestion 2.065mPractice - Using PROC GLM to Perform Post Hoc Parwise Comparisons20mQuestion 2.075mQuestion 2.085mPractice - Describing the Relationship between Continuous Variables20mQuestion 2.095mPractice - Using PROC REG to Fit a Simple Linear Regression Model20mANOVA and Regression30m

Week3

Week 3

2 hours to complete

More Complex Linear Models

2 hours to complete

13 videos (Total 43 min), 1 reading, 5 quizzes

13 videos

Overview1mScenario1mApplying the Two-Way ANOVA Model3mDemo: Performing a Two-Way ANOVA Using PROC GLM7mInteractions3mDemo: Performing a Two-Way ANOVA With an Interaction Using PROC GLM5mDemo: Performing Post-Processing Analysis Using PROC PLM4mScenario44sThe Multiple Linear Regression Model2mHypothesis Testing for Multiple Regression1mMultiple Linear Regression versus Simple Linear Regression2mAdjusted R-Square1mDemo: Fitting a Multiple Linear Regression Model Using PROC REG7m

1 reading

The STORE Statement10m

5 practice exercises

Question 3.015mPractice - Performing a Two-Way ANOVA Using PROC GLM20mQuestion 3.025mPractice - Performing Multiple Regression Using PROC REG20mMore Complex Linear Models30m

2 hours to complete

Model Building and Effect Selection

2 hours to complete

11 videos (Total 28 min), 3 readings, 4 quizzes

11 videos

Overview47sScenario1mApproaches to Selecting Models2mThe All-Possible Regressions Approach to Model Building1mThe Stepwise Selection Approach to Model Building3mInterpreting p-Values and Parameter Estimates2mDemo: Performing Stepwise Regression Using PROC GLMSELECT7mScenario37sInformation Criteria2mAdjusted R-Square and Mallows' Cp56sDemo: Performing Model Selection Using PROC GLMSELECT5m

3 readings

Activity - Optional Stepwise Selection Method Code10mInformation Criteria Penalty Components10mAll-Possible Selection

4 practice exercises

Question 4.015mPractice - Using PROC GLMSELECT to Perform Stepwise Selection20mPractice - Using PROC GLMSELECT to Perform Other Model Selection Techniques20mModel Building and Effect Selection20m

Week4

Week 4

3 hours to complete

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.

3 hours to complete

18 videos (Total 46 min)

18 videos

Overview1mScenario40sAssumptions for Regression2mVerifying Assumptions Using Residual Plots3mDemo: Examining Residual Plots Using PROC REG5mScenario47sIdentifying Influential Observations1mChecking for Outliers with STUDENT Residuals1mChecking for Influential Observations2mDetecting Influential Observations with DFBETAS1mDemo: Looking for Influential Observations Using PROC GLMSELECT and PROC REG5mDemo: Examining the Influential Observations Using PROC PRINT6mHandling Influential Observations1mScenario38sExploring Collinearity1mVisualizing Collinearity2mDemo: Calculating Collinearity Diagnostics Using PROC REG5mUsing an Effective Modeling Cycle1m

7 practice exercises

Practice: Using PROC REG to Examine Residuals20mQuestion 5.015mPractice: Using PROC REG to Generate Potential Outliers20mQuestion 5.025mQuestion 5.035mPractice: Using PROC REG to Assess Collinearity20mModel Post-Fitting for Inference30m

2 hours to complete

Model Building for Scoring and Prediction

2 hours to complete

11 videos (Total 27 min), 1 reading, 4 quizzes

11 videos

Overview1mScenario27sPredictive Modeling Terminology1mModel Complexity51sBuilding a Predictive Model2mModel Assessment and Selection1mDemo: Building a Predictive Model Using PROC GLMSELECT10mScenario29sPreparing for Scoring1mMethods of Scoring1mDemo: Scoring Data Using PROC PLM4m

1 reading

Partitioning a Data Set Using PROC GLMSELECT10m

4 practice exercises

Question 6.015mPractice: Building a Predictive Model Using PROC GLMSELECT20mPractice: Scoring Using the SCORE Statement in PROC GLMSELECT20mModel Building for Scoring and Prediction30m

Week5

Week 5

4 hours to complete

Categorical Data Analysis

4 hours to complete

25 videos (Total 73 min)

25 videos

Overview1mScenario59sAssociations between Categorical Variables2mDemo: Examining the Distribution of Categorical Variables Using PROC FREQ and PROC UNIVARIATE6mScenario43sThe Pearson Chi-Square Test3mOdds Ratios4mDemo: Performing a Pearson Chi-Square Test of Association Using PROC FREQ5mScenario22sThe Mantel-Haenszel Chi-Square Test1mThe Spearman Correlation Statistic40sDemo: Detecting Ordinal Associations Using PROC FREQ2mScenario49sModeling a Binary Response3mDemo: Fitting a Binary Logistic Regression Model Using PROC LOGISTIC6mInterpreting the Odds Ratio3mComparing Pairs to Assess the Fit of a Logistic Regression Model4mScenario50sSpecifying a Parameterization Method4mDemo: Fitting a Multiple Logistic Regression Model with Categorical Predictors Using PROC LOGISTIC7mScenario35sInteractions between Variables2mDemo: Fitting a Multiple Logistic Regression Model with Interactions Using PROC LOGISTIC3mDemo: Fitting a Multiple Logistic Regression Model with All Odds Ratios Using PROC LOGISTIC3mDemo: Generating Predictions Using PROC PLM2m

18 practice exercises

Question 7.015mQuestion 7.025mPractice: Using PROC FREQ to Examine Distributions20mQuestion 7.035mQuestion 7.045mQuestion 7.055mQuestion 7.065mPractice: Using PROC FREQ to Perform Tests and Measures of Association20mQuestion 7.075mQuestion 7.085mPractice: Using PROC LOGISTIC to Perform a Binary Logistic Regression Analysis20mQuestion 7.095mQuestion 7.105mPractice: Using PROC LOGISTIC to Perform a Multiple Logistic Regression Analysis with Categorical Variables20mQuestion 7.115mQuestion 7.125mPractice: Using PROC LOGISTIC to Perform Backward Elimination and PROC PLM to Generate Predictions20mCategorical Data Analysis30m

Reviews

4.7

77 reviews

5 stars
82.73%
4 stars
12.23%
3 stars
2.51%
2 stars
0.71%
1 star
1.79%

TOP REVIEWS FROM STATISTICS WITH SAS

by DPFeb 7, 2023

The best part about this course was how well he explained the different types of statistical studies in general and also with the examples. Very very useful. A must take course for every statistician.

by RPMar 2, 2021

Excellent, it's very valuable statistics course !! Thanks SAS

by CKMar 18, 2021

The theoretical parts on introductory statistical analysis (in shorts tests, anova, regression) are well covered. The technical parts i.e coding applications complement the theoretical parts 100%.

by BKFeb 11, 2020

excellent course. lots of things to learn and build the skills in SAS programming.

View all reviews

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Statistics with SAS

Statistics with SAS

About this Course