SAS programming Data Analysis Modeling Certification Course

Name: SAS programming Data Analysis Modeling Certification Course
Rating: 4.5 (46 reviews)

Complete Prep Course for SAS Certified Statistical Business Analyst SAS 9.4: Regression and Modeling (exam ID A00-240)

(46 ratings)

603 students

Created by sharon cheng

Last updated 4/2021

English

English [Auto]

30-Day Money-Back Guarantee

What you'll learn

the most essential data analyses topics (ANOVA, Linear Regression , Logistic Regression, predictive modeling )
the complete process of predictive modeling (data preparation for predictive modeling, sampling for training & validation data, modeling, validation, scoring, measuring model performance)
Write SAS programs to generate and make conclusions and interpretations on major statistical outputs and results
Be completely prepared for to obtain the SAS certification: SAS® Certified Statistical Business Analyst Using SAS®9: Regression and Modeling (exam ID A00-240).

Requirements

basic SAS programming skills; basic statistics knowledge

Description

This course is for anyone who wants to move up their careers by equipping themselves with the critical analytical skills.

Course Highlights:

includes the most essential data analyses topics (Analysis of Variance, Linear Regression, Logistic Regression, predictive modeling )
utilizes step by step/ code by code explanations for all SAS programs; presents statistical knowledges in PowerPoint presentations; provides detailed explanations on all statistical outputs
shows the complete process of predictive modeling (data preparation for predictive modeling, sampling for training and validation data, modeling, validation, scoring and measuring model performance)
It is also a Complete Prep Course for SAS® Certified Statistical Business Analyst Using SAS®9: Regression and Modeling (exam ID A00-240).
Data, SAS programs and PowerPoint slides used in the course are downloadable in lecture 4 (the course materials are ONLY for practice, they are protected by copyright)
Quizzes at the end of each section to test what you have learned

A Note on Course ratings and reviews:
Please be sincere and considerate when you provide ratings and reviews. As you may know, this is crucial to an online instructor like me. And it will encourage me providing more contents to the course and better service to you! So Please provide fair ratings to this course with the consideration of the comparison among other available SAS courses. Thank you!

References:

SAS Certification Prep Guide, Statistical Business Analysis Using SAS9

Note: The course was created with SAS software licence for SAS University Edition.

Who this course is for:

anyone who wants to move up their careers by equipping themselves with the critical analytical skills
anyone who is interested in learning the most essential data analyses topics (ANOVA, Linear Regression , Logistic Regression, predictive modeling )
anyone who wants to master the complete process of predictive modeling (data preparation for predictive modeling, sampling for training & validation data, modeling, validation, scoring, measuring model performance)
anyone who wants to be able to write SAS programs to generate and make conclusions and interpretations on major statistical outputs and results
anyone who wants to be completely prepared for to obtain the SAS certification: SAS® Certified Statistical Business Analyst Using SAS®9: Regression and Modeling (exam ID A00-240)

7 sections • 90 lectures • 11h 52m total length

Preview02:28
Downloadable course materials
01:26

ANOVA 0. Using TTEST to compare means
09:42
Using Proc Univariate to Test the Normality Assumption Using the K-S Test
02:39
ANOVA 1. One-factor ANOVA model and Test Statistic in PowerPoint Presentation
10:07
Preview07:01
ANOVA 3. generate Predicted Values & Residuals Use OUTPUT Statement in Proc GLM
03:56
ANOVA 4. Measures of fit: output explanation of one-way ANOVA
04:01
ANOVA 5. The Normality Assumption and the PLOTS Option in Proc GLM
03:21
ANOVA 6. Levene’s Test for Equal Variances and the MEANS Statement in Proc GLM
04:08
ANOVA 7. Post Hoc Tests: The Tukey-Kramer Procedure and the MEANS Statement
11:36
ANOVA 8. Other Post Hoc Procedures, the LSMEANS Statement, and the Diffogram
09:30
ANOVA 9. the Randomized Block Design with example and Interpretation
16:27
ANOVA 10. Randomized block design: Post Hoc Tests Using the LSMEANS Statement
03:14
ANOVA 11. Assess Assumptions of a Randomized Block Design Using the PLOTS Option
03:04
ANOVA 12. Unbalanced Designs, the LSMEANS Statement and Type III Sums of Squares
04:37
ANOVA 13. Two factor ANOVA: overview in PowerPoint Presentation
07:47
ANOVA 14. Example and Interpretation of the Two-Factor ANOVA
10:42
ANOVA 15. Analyze Simple Effects When Interaction Exists Use LSMEANS with Slice
03:23
ANOVA 16. Assessing the Assumptions of a Two-Factor Analysis of Variance
02:50
Analysis of Variance (ANOVA)
10 questions

Prepare Inputs Vars_1. Chapter Overview
06:04
Preview13:18
Prepare Inputs Vars_3.Categorical Input Variable_1.Knowledge points
04:56
Prepare Inputs Vars_3. Categorical Input Variables_2. Proc freq and Proc Means
07:29
Prepare Inputs Vars_3. Categorical Input Variables_3. Proc Cluster
08:19
Prepare Inputs Vars_3. Categorical Input Variables_4. Cut off point
05:42
Prepare Inputs Vars_3. Categorical Input Variables_5. cluster var
09:37
Prepare Inputs Vars_4. Variable Cluster_1. Slides on VARCLUS for redundancy
10:39
Prepare Inputs Vars_4. Variable Cluster_2. Proc VARCLUS for reduce redundancy
18:39
Prepare Inputs Vars_5. Variable Screening_1. Overview on Knowledge Points
04:45
Prepare Inputs Vars_5. Variable Screening_2. Proc CORR detect Association_Part A
08:21
Prepare Inputs Vars_5. Variable Screening_3. Proc CORR detect Association_Part B
06:15
Prepare Inputs Vars_5. Variable Screening_4. Proc CORR detect Association_Part C
07:25
Prepare Inputs Vars_5. Variable Screening_5. Empirical Logit detect Non-Linear
09:31
Prepare Inputs Vars
10 questions

Exploring the Relationship between Two Continuous Variables using Scatter Plots
09:58
Producing Correlation Coefficients Using the CORR Procedure
14:46
Multiple Linear Regression: fit multiple regression with Proc REG
10:17
Preview06:17
Multiple Linear Regression: Quantifying the Relative Impact of a Predictor
03:27
Multiple Linear Regression: Check Collinearity Using VIF, COLLIN, and COLLINOINT
11:05
fit simple linear regression with Proc GLM
15:22
Multiple Linear Reg: Var Selection With Proc REG:all possible subset: adjust R2
12:28
Multiple Linear Reg: Var Selection With Proc REG:all possible subset: Mallows Cp
06:27
Multiple Linear Regression:Variable Selection With Proc REG:Backward Elimination
08:06
Multiple Linear Regression:Variable Selection With Proc REG: Forward selection
08:31
Multiple Linear Regression:Variable Selection With Proc REG: Stepwise selection
03:44
Multiple Linear Regression:Variable Selection With Proc GLMSELECT
15:07
Multiple Linear Regression: PowerPoint Slides on regression assumptions
07:45
Multiple Linear Regression: regression assumptions
13:07
Multiple Linear Regression: PowerPoint Slides on influential observations
11:23
Multiple Linear Regression: Using statistics to identify influential observation
18:22
Linear Regression Analysis
10 questions

Logistic Regression Analysis: Overview
09:54
logistic regression with a continuous numeric predictor Part 1
05:20
logistic regression with a continuous numeric predictor Part 2
15:14
Plots for Probabilities of an Event
04:46
Plots of the Odds Ratio
05:33
logistic regression with a categorical predictor: Effect Coding Parameterization
10:29
logistic reg with categorical predictor: Reference Cell Coding Parameterization
04:52
Multiple Logistic Regression: full model SELECTION=NONE
07:48
Preview07:57
Multiple Logistic Regression: Forward Selection
06:29
Multiple Logistic Regression: Stepwise Selection
06:40
Multiple Logistic Regression: Customized Options
12:18
Multiple Logistic Regression: Best Subset Selection
05:05
Multiple Logistic Regression: model interaction
14:24
Multiple Logistic Reg: Scoring New Data: SCORE Statement with PROC LOGISTIC
06:20
Multiple Logistic Reg: Scoring New Data: Using the PLM Procedure
05:25
Multiple Logistic Reg: Scoring New Data: the CODE Statement within PROC LOGISTIC
04:29
Multiple Logistic Reg: Score New Data: OUTMODEL & INMODEL Options with Logistic
05:18
Logistic Regression Analysis
10 questions

Measure of Model Performance: Overview
09:31
Preview10:20
Measures of Performance Using the Classification Table: PowerPoint Presentation
07:08
Using The CTABLE Option in Proc Logistic for Producing Classification Results
09:58
Assessing the Performance & Generalizability of a Classifier: PowerPoint slides
03:52
The Effect of Cutoff Values on Sensitivity and Specificity Estimates
10:45
Measure of Performance Using the Receiver-Operator-Characteristic (ROC) Curve
07:23
Model Comparison Using the ROC and ROCCONTRAST Statements
05:17
Measures of Performance Using the Gains Charts
10:53
Measures of Performance Using the Lift Charts
03:32
Adjust for Oversample: PEVENT Option for Priors & Manually adjust Classification
15:48
Manually Adjusting Posterior Probabilities to Account for Oversampling
05:20
Manually Adjusted Intercept Using the Offset to account for oversampling
06:44
Automatically Adjusted Posterior Probabilities to Account for Oversampling
06:18
Decision Theory: Decision Cutoffs and Expected Profits for Model Selection
11:32
Decision Theory: Using Estimated Posterior Probabilities to Determine Cutoffs
05:06
Measure of Model Performance
10 questions

sharon cheng

SC Statistical Programming

4.5 Instructor Rating
4,221 Reviews
20,801 Students
4 Courses

Welcome to SC Statistical Programming!

Before I become an online SAS programming instructor, I have worked as a statistician/SAS programmer for 7 years. I am a SAS certified professional and highly proficient in SAS programming, and I enjoy teaching as well. Now I would like to use my SAS programming skills to help people learning SAS in an easy and affordable way.

I created my business SC Statistical Programming to offer free SAS programming courses and tutorial videos and coding articles, as well as affordable online video-based SAS programming courses at various levels. No matter you are a beginner or experienced professional in SAS programming, all courses are designed to help you learn SAS programming easy and efficiently.

Information about my current published courses:

A SAS BASE certificate course for absolute SAS Beginners

SAS Advanced Certificate: SAS SQL, SAS Macro & Advanced Tech

SAS Programming on Report and Table Generating

All courses offer:

1. Lifetime access 24/7; 30 days money back guarantee!

2. Each lecture/video shows hands-on coding in SAS programming environment as well as detailed step-by-step and code-by-code explanations

3. The course supplements (including SAS codes and data used in lectures) are in their own formats and stored in Zip folders for easy downloading

4. The SAS code and data are compatible across all SAS programming environments

5. Quizzes, Coding Exercises and Projects for practice

SAS programming Data Analysis Modeling Certification Course

What you'll learn

Requirements

Description

Who this course is for:

Course content

Course Overview and downloadable course materials2 lectures • 4min

Free cloud-based SAS software option for learning: SAS OnDemand for Academics5 lectures • 25min

Analysis of Variance (ANOVA)18 lectures • 1hr 58min

Prepare Inputs Vars for predictive Modeling14 lectures • 2hr 1min

Linear Regression Analysis17 lectures • 2hr 56min

Logistic Regression Analysis18 lectures • 2hr 18min

Measure of Model Performance16 lectures • 2hr 9min

Instructor