이 강좌는 SAS 비즈니스 통계 분석가 전문 자격증의 일부입니다.

4.6
별점
31개의 평가

Michael J Patetta

2,152명이 이미 등록했습니다.

제공자:

SAS 비즈니스 통계 분석가 전문 자격증SAS

이 강좌에 대하여

최근 조회 8,253회

This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This course also discusses selecting variables and interactions, recoding categorical variables based on the smooth weight of evidence, assessing models, treating missing values, and using efficiency techniques for massive data sets. You learn to use logistic regression to model an individual's behavior as a function of known inputs, create effect plots and odds ratio plots, handle missing data values, and tackle multicollinearity in your predictors. You also learn to assess model performance and compare models.

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지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

다음 특화 과정의 3개 강좌 중 3번째 강좌:

SAS 비즈니스 통계 분석가

중급 단계

완료하는 데 약 17시간 필요

영어

자막: 영어

귀하가 습득할 기술

Oversampling
Logistic Regression
Predictive Modelling
regression

유연한 마감일

일정에 따라 마감일을 재설정합니다.

공유 가능한 수료증

완료 시 수료증 획득

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

다음 특화 과정의 3개 강좌 중 3번째 강좌:

SAS 비즈니스 통계 분석가

중급 단계

완료하는 데 약 17시간 필요

영어

자막: 영어

강사

강사 평가

4.7/5 (10개의 평가)

Michael J Patetta

Principal Analytical Training Consultant

Education

2,152명의 학습자

1개의 강좌

제공자:

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.

강의 계획표 - 이 강좌에서 배울 내용

주

주 1

완료하는 데 1시간 필요

Course Overview and Logistics

완료하는 데 1시간 필요

1개 동영상 (총 1분), 6 개의 읽기 자료

완료하는 데 2시간 필요

Understanding Predictive Modeling

In this module, you review the fundamentals of predictive modeling. Then you explore the business scenario data that is used throughout the course. Finally, you learn about common analytical challenges that you might encounter as a modeler.

완료하는 데 2시간 필요

15개 동영상 (총 34분), 1 개의 읽기 자료, 6 개의 테스트

15개의 동영상

Overview53

Introduction19

Goals of Predictive Modeling1m

Terms for Elements in Predictive Modeling49

Basic Steps of Predictive Modeling2m

Applications of Predictive Modeling1m

Demonstration Scenario: Target Marketing for a Bank1m

Demo: Examining the Code for Generating Descriptive Statistics and Frequency Tables2m

Introduction21

Data Challenges6m

Analytical Challenges2m

Separate Sampling1m

Avoiding the Optimism Bias: Honest Assessment2m

Splitting the Data for Model Training and Assessment3m

Demo: Splitting the Data5m

1개의 읽기 자료

Summary10m

6개 연습문제

Practice: Exploring the Bank Data for the Target Marketing Project20m

Practice: Exploring the Veterans' Organization Data Used in the Practices20m

Question 1.015m

Question 1.025m

Question 1.035m

Practice: Splitting the Data20m

주

주 2

완료하는 데 2시간 필요

Fitting the Model

In this module, you investigate the concepts behind the logistic regression model. Then you learn to use the LOGISTIC procedure to fit a logistic regression model. Finally, you learn how to score new cases and adjust the model for oversampling.

완료하는 데 2시간 필요

18개 동영상 (총 54분), 1 개의 읽기 자료, 4 개의 테스트

18개의 동영상

Overview54

Introduction22

Understanding the Logistic Regression Model2m

Constraining the Posterior Probability Using the Logit Transformation1m

Understanding the Fitted Surface1m

Interpreting the Model by Calculating the Odds Ratio3m

Understanding Logistic Discrimination1m

Estimating Unknown Parameters Using Maximum Likelihood Estimation2m

Interpreting Concordant, Discordant, and Tied Pairs1m

Using PROC LOGISTIC to Fit Logistic Regression Models24

Demo: Fitting a Basic Logistic Regression Model, Part 18m

Demo: Fitting a Basic Logistic Regression Model, Part 212m

Scoring New Cases26

Demo: Scoring New Cases7m

Introduction16

Understanding the Effect of Oversampling53

Understanding the Offset1m

Demo: Correcting for Oversampling6m

1개의 읽기 자료

Summary10m

4개 연습문제

Question 2.015m

Question 2.025m

Practice: Fitting a Logistic Regression Model20m

Fitting the Model Review30m

주

주 3

완료하는 데 3시간 필요

Preparing the Input Variables, Part 1

In this module, you learn how to deal with common problems with your predictor variables such as missing values, categorical predictors with many levels, a high number of redundant predictors, and nonlinear relationships with the response variable.

완료하는 데 3시간 필요

26개 동영상 (총 76분)

26개의 동영상

Overview34

Introduction22

Reasons for Missing Data2m

Complete Case Analysis1m

Methods for Imputing Missing Values2m

Missing Value Imputation with Missing Value Indicator Variables3m

Demo: Imputing Missing Values4m

Cluster Imputation1m

Introduction25

Problems Caused by Categorical Inputs4m

Solutions to Problems Caused by Categorical Inputs39

Linking to Other Data Sets56

Collapsing Categories by Thresholding53

Collapsing Categories by Using Greenacre's Method3m

Demo: Collapsing the Levels of a Nominal Input, Part 16m

Demo: Collapsing the Levels of a Nominal Input, Part 210m

Replacing Categorical Levels by Using Smoothed Weight-of-Evidence Coding2m

Demo: Computing the Smoothed Weight of Evidence4m

Introduction20

Problem of Redundancy2m

Variable Clustering Method1m

Understanding Principal Components5m

Divisive Clustering3m

PROC VARCLUS Syntax1m

Selecting a Representative Variable from Each Cluster1m

Demo: Reducing Redundancy by Clustering Variables8m

9개 연습문제

Question 3.015m

Practice: Imputing Missing Values20m

Question 3.025m

Question 3.035m

Question 3.045m

Practice: Collapsing the Levels of a Nominal Input20m

Practice: Computing the Smoothed Weight of Evidence20m

Question 3.055m

Practice: Reducing Redundancy by Clustering Variables20m

주

주 4

완료하는 데 4시간 필요

Preparing the Input Variables, Part 2

In this module, you learn how to select the most predictive variables to use in your model.

완료하는 데 4시간 필요

23개 동영상 (총 92분), 1 개의 읽기 자료, 12 개의 테스트

23개의 동영상

Introduction28

Detecting Nonlinear Relationships4m

Demo: Performing Variable Screening, Part 15m

Demo: Performing Variable Screening, Part 24m

Univariate Binning and Smoothing2m

Demo: Creating Empirical Logit Plots10m

Remedies for Nonlinear Relationships2m

Demo: Accommodating a Nonlinear Relationship, Part 16m

Demo: Accommodating a Nonlinear Relationship, Part 27m

Introduction26

Specifying a Subset Selection Method in PROC LOGISTIC1m

Best-Subsets Selection54

Stepwise Selection2m

Backward Elimination1m

Scalability of the Subset Selection Methods in PROC LOGISTIC2m

Detecting Interactions2m

BIC-based Significance Level2m

Demo: Detecting Interactions7m

Demo: Using Backward Elimination to Subset the Variables4m

Demo: Displaying Odds Ratios for Variables Involved in Interactions3m

Demo: Creating an Interaction Plot3m

Demo: Using the Best-Subsets Selection Method3m

Demo: Using Fit Statistics to Select a Model9m

1개의 읽기 자료

Summary of Preparing the Input Variables, Parts 1 and 210m

12개 연습문제

Question 3.065m

Practice: Performing Variable Screening20m

Practice: Creating Empirical Logit Plots20m

Question 3.075m

Question 3.085m

Question 3.095m

Practice: Using Forward Selection to Detect Interactions20m

Question 3.105m

Practice: Using Backward Elimination to Subset the Variables20m

Question 3.115m

Practice: Using Fit Statistics to Select a Model20m

Preparing the Input Variables Review30m

주

주 5

완료하는 데 3시간 필요

Measuring Model Performance

In this module, you learn how to assess the performance of your model and how to determine allocation rules that maximize profit. Finally, you learn how to generate a family of increasingly complex predictive models and how to select the best model.

완료하는 데 3시간 필요

30개 동영상 (총 78분), 1 개의 읽기 자료, 9 개의 테스트

30개의 동영상

Overview40

Introduction22

Fit versus Complexity2m

Assessing Models when Target Event Data Is Rare1m

Demo: Preparing the Validation Data5m

Introduction15

Understanding the Confusion Matrix5m

Measuring Performance across Cutoffs by Using the ROC Curve4m

Choosing Depth by Using the Gains Chart2m

Effects of Oversampled Data on Performance Measures3m

Adjusting a Confusion Matrix for Oversampling46

Demo: Measuring Model Performance Based on Commonly-Used Metrics6m

Introduction26

Understanding the Effect of Cutoffs on Confusion Matrices43

Understanding the Profit Matrix1m

Choosing the Optimal Cutoff by Using the Profit Matrix2m

Using the Central Cutoff33

Using Profit to Assess Fit28

Calculating Sampling Weights52

Demo: Using a Profit Matrix to Measure Model Performance6m

Introduction25

Plotting Class Separation1m

Assessing Overall Predictive Power3m

Demo: Using the K-S Statistic to Measure Model Performance2m

Introduction21

Comparing ROC Curves of Several Models"1m

Demo: Comparing ROC Curves to Measure Model Performance4m

Using Macros to Compare Many Models44

Demo: Comparing and Evaluating Many Models, Part 17m

Demo: Comparing and Evaluating Many Models, Part 27m

1개의 읽기 자료

Summary10m

9개 연습문제

Question 4.015m

Question 4.025m

Question 4.035m

Practice: Assessing Model Performance20m

Question 4.045m

Question 4.055m

Question 4.065m

Question 4.075m

Measuring Model Performance Review30m

주

주 6

완료하는 데 1시간 필요

SAS Certification Practice Exam - Statistical Business Analysis Using SAS®9: Regression and Modeling

완료하는 데 1시간 필요

1 개의 읽기 자료

검토

4.6

8개의 리뷰

5 stars
83.87%
4 stars
6.45%
2 stars
3.22%
1 star
6.45%