This course is part of the SAS Statistical Business Analyst Professional Certificate

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stars
36 ratings

Jordan Bakerman

3,883 already enrolled

Offered By

SAS Statistical Business Analyst Professional CertificateSAS

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.

Flexible deadlines

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Shareable Certificate

Earn a Certificate upon completion

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Course 2 of 3 in the

SAS Statistical Business Analyst Professional Certificate

Intermediate Level

Approx. 11 hours to complete

English

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Skills you will gain

Multivariate Time Series Analysis
Surrogate Model
Multivariate Analysis
Predictive Modelling

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.

Course 2 of 3 in the

SAS Statistical Business Analyst Professional Certificate

Intermediate Level

Approx. 11 hours to complete

English

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

Try Coursera for Business

Instructor

Instructor rating

4.37/5 (8 Ratings)

Jordan Bakerman

Analytical Training Consultant

Education

42,108 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

Week1

Week 1

1 hour to complete

Course Overview (Review from Introduction to Statistics: Hypothesis Testing)

1 hour to complete

2 videos (Total 13 min), 5 readings

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

Week2

Week 2

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

Week3

Week 3

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

4.8

6 reviews

5 stars
80.55%
4 stars
16.66%
3 stars
2.77%

TOP REVIEWS FROM REGRESSION MODELING FUNDAMENTALS

by RMJun 14, 2021

Thanks so much to our instructor, Jordan Bakerman for teaching this course!

by KKJan 25, 2021

Must have taken the prior Course. In the Specialization.

by SSFeb 13, 2021

Great Study material & Ease of understanding of the concepts.

View all reviews

About the SAS Statistical Business Analyst Professional Certificate

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Regression Modeling Fundamentals

About this Course

Skills you will gain