When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Certificate?

When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Regression Modeling Fundamentals

本课程是 SAS Statistical Business Analyst 专业证书的一部分

位教师：Jordan Bakerman

8,439 人已注册

包含在中

了解更多

4个模块

深入了解一个主题并学习基础知识。

53 条评论

中级等级

需要一些相关经验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

53 条评论

中级等级

需要一些相关经验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

32 项作业

授课语言：英语（English）

91%

of learners achieved a positive career outcome

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累 Data Analysis 领域的专业知识

本课程是 SAS Statistical Business Analyst 专业证书专项课程的一部分

在注册此课程时，您还会同时注册此专业证书。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
通过 SAS 获得可共享的职业证书

该课程共有4个模块

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.

单元详情

This module focuses on building regression models and selecting the best set of predictors using practical, data-driven methods in SAS. You’ll start by setting up the course environment, then move into key model selection approaches—including all-possible regressions, stepwise selection using significance levels, and selection using information criteria. Along the way, you’ll learn how to interpret p-values and parameter estimates, evaluate models with metrics like adjusted R-square and Mallows’ Cp, and apply these through demos and practice assignments.

涵盖的内容

13个视频7篇阅读材料3个作业

13个视频总计41分钟

Welcome and Meet the Instructor2分钟
Demo: Exploring Ames Housing Data11分钟
Overview1分钟
Scenario1分钟
Approaches to Selecting Models2分钟
The All-Possible Regressions Approach to Model Building1分钟
The Stepwise Selection Approach to Model Building3分钟
Interpreting p-Values and Parameter Estimates2分钟
Demo: Performing Stepwise Regression Using PROC GLMSELECT8分钟
Scenario1分钟
Information Criteria2分钟
Adjusted R-Square and Mallows' Cp1分钟
Demo: Performing Model Selection Using PROC GLMSELECT6分钟

7篇阅读材料总计51分钟

Learner Prerequisites1分钟
Access SAS Software for this Course10分钟
Completing Demos and Practices10分钟
Frequently Asked Questions10分钟
Activity - Optional Stepwise Selection Method Code10分钟
Information Criteria Penalty Components10分钟
All-Possible Selection0分钟

3个作业总计80分钟

Knowledge Check - Using PROC GLMSELECT for Stepwise Selection30分钟
Knowledge Check -Using PROC GLMSELECT to Perform Other Model Selection Techniques30分钟
Model Building and Effect Selection20分钟

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.

涵盖的内容

18个视频7个作业

18个视频总计46分钟

Overview1分钟
Scenario1分钟
Assumptions for Regression2分钟
Verifying Assumptions Using Residual Plots3分钟
Demo: Examining Residual Plots Using PROC REG5分钟
Scenario1分钟
Identifying Influential Observations1分钟
Checking for Outliers with STUDENT Residuals1分钟
Checking for Influential Observations3分钟
Detecting Influential Observations with DFBETAS1分钟
Demo: Looking for Influential Observations Using PROC GLMSELECT and PROC REG5分钟
Demo: Examining the Influential Observations Using PROC PRINT7分钟
Handling Influential Observations2分钟
Scenario1分钟
Exploring Collinearity2分钟
Visualizing Collinearity2分钟
Demo: Calculating Collinearity Diagnostics Using PROC REG5分钟
Using an Effective Modeling Cycle2分钟

7个作业总计105分钟

Practice: Using PROC REG to Examine Residuals20分钟
Question 5.015分钟
Practice: Using PROC REG to Generate Potential Outliers20分钟
Question 5.025分钟
Question 5.035分钟
Practice: Using PROC REG to Assess Collinearity20分钟
Model Post-Fitting for Inference30分钟

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.

涵盖的内容

11个视频1篇阅读材料4个作业

11个视频总计27分钟

Overview2分钟
Scenario0分钟
Predictive Modeling Terminology2分钟
Model Complexity1分钟
Building a Predictive Model3分钟
Model Assessment and Selection2分钟
Demo: Building a Predictive Model Using PROC GLMSELECT11分钟
Scenario0分钟
Preparing for Scoring1分钟
Methods of Scoring1分钟
Demo: Scoring Data Using PROC PLM4分钟

1篇阅读材料总计10分钟

Partitioning a Data Set Using PROC GLMSELECT10分钟

4个作业总计75分钟

Question 6.015分钟
Practice: Building a Predictive Model Using PROC GLMSELECT20分钟
Practice: Scoring Using the SCORE Statement in PROC GLMSELECT20分钟
Model Building for Scoring and Prediction30分钟

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.

涵盖的内容

25个视频18个作业

25个视频总计73分钟

Overview2分钟
Scenario1分钟
Associations between Categorical Variables2分钟
Demo: Examining the Distribution of Categorical Variables Using PROC FREQ and PROC UNIVARIATE6分钟
Scenario1分钟
The Pearson Chi-Square Test3分钟
Odds Ratios4分钟
Demo: Performing a Pearson Chi-Square Test of Association Using PROC FREQ5分钟
Scenario0分钟
The Mantel-Haenszel Chi-Square Test1分钟
The Spearman Correlation Statistic1分钟
Demo: Detecting Ordinal Associations Using PROC FREQ2分钟
Scenario1分钟
Modeling a Binary Response4分钟
Demo: Fitting a Binary Logistic Regression Model Using PROC LOGISTIC7分钟
Interpreting the Odds Ratio3分钟
Comparing Pairs to Assess the Fit of a Logistic Regression Model5分钟
Scenario1分钟
Specifying a Parameterization Method5分钟
Demo: Fitting a Multiple Logistic Regression Model with Categorical Predictors Using PROC LOGISTIC7分钟
Scenario1分钟
Interactions between Variables2分钟
Demo: Fitting a Multiple Logistic Regression Model with Interactions Using PROC LOGISTIC4分钟
Demo: Fitting a Multiple Logistic Regression Model with All Odds Ratios Using PROC LOGISTIC3分钟
Demo: Generating Predictions Using PROC PLM2分钟

18个作业总计190分钟

Question 7.015分钟
Question 7.025分钟
Practice: Using PROC FREQ to Examine Distributions20分钟
Question 7.035分钟
Question 7.045分钟
Question 7.055分钟
Question 7.065分钟
Practice: Using PROC FREQ to Perform Tests and Measures of Association20分钟
Question 7.075分钟
Question 7.085分钟
Practice: Using PROC LOGISTIC to Perform a Binary Logistic Regression Analysis20分钟
Question 7.095分钟
Question 7.105分钟
Practice: Using PROC LOGISTIC to Perform a Multiple Logistic Regression Analysis with Categorical Variables20分钟
Question 7.115分钟
Question 7.125分钟
Practice: Using PROC LOGISTIC to Perform Backward Elimination and PROC PLM to Generate Predictions20分钟
Categorical Data Analysis30分钟