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Regression Analysis
Understanding Regression Techniques

Understanding Regression Techniques

Loại khoá học: Data Science

An Introduction to Predictive Analytics for Data Scientists

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50.000 VND
1.699.000 VND
Đầy Đủ Bài Giảng
Học Online Tiện Lợi
Kích Hoạt Nhanh 2-5 Phút
Thanh toán tự động
Được phép tải xuống

Tổng quan

Nội dung khoá học

Đánh giá

Mô tả

Included in this course is an e-book and a set of slides. The purpose of the course is to introduce the students to regression techniques. The course covers linear regression, logistic regression and count model regression. The theory behind each of these three techniques is described in an intuitive and non-mathematical way. Students will learn when to use each of these three techniques, how to test the assumptions, how to build models, how to assess the goodness-of-fit of the models, and how to interpret the results. The course does not assume the use of any specific statistical software. Therefore, this course should be of use to anyone intending on applying regression techniques no matter which software they use. The course also walks students through three detailed case studies.

Bạn sẽ học được gì

Understand what regression is

Build linear regression models

Build logistic regression models

Build count models

Interpret regression results

Visualise the results

Test model assumptions

Yêu cầu

  • none

Nội dung khoá học

15 sections

Simple Linear Regression

7 lectures
Introduction
03:15
Simple linear regression
04:26
The slope
05:29
R-squared
05:30
The p-value
07:06
Model fit
02:34
The residuals
05:16

Multiple linear regression

5 lectures
Multiple linear regression
03:09
The slopes
05:10
R-squared
01:38
The p-value
01:12
Model fit and residuals
02:02

Linear Regression: Binary, Categorical, and Quadratic Variables

3 lectures
Binary variables
08:37
Categrical variables
11:30
Quadratic variables
07:23

Linear Regression: Checking Model Fit and Assumptions

8 lectures
Prediction
04:27
Normality of residuals
02:20
Independence of residuals
02:25
Constant variance
02:00
Multicolinearity
02:49
Outliers
04:10
Influencial observations
04:33
Selection algorithms
08:41

Linear Regression Case Study

11 lectures
The dataset
03:37
Including continuous variables
10:32
Including binary variables
02:22
Including categorical variables
02:17
Multiple regression
03:51
Checking model fit
02:55
Checking model assumptions
06:41
Multicollinearity
02:06
Outliers
03:27
Influential observations
04:37
Visualizing the result
03:10

Logistic Regression: Contingency Tables

4 lectures
Two-by-two tables
04:03
The odds
03:21
The odds ratio
02:37
Two-by-three tables
07:16

Logistic Regression Models

7 lectures
Single independent variable
12:51
Examples
05:06
Binary variables
06:30
Multiple independent variables
05:39
Categorical variables
08:34
Nonlinearity: Non-graphical test
04:06
Nonlinearity: Graphical test
06:51

Logistic Regression: Prediction and Model Fit

7 lectures
Prediction
03:58
Goodness of fit: Likelihood ratio test
02:04
Goodness of fit: Hosmer-Lemeshow test
03:44
Goodness of fit: Classification tables
08:28
Goodness of fit: ROC analysis
01:41
Residuals
02:23
Influential Observations
05:01

Logistic Regression Case Study

12 lectures
The dataset
03:47
Continuous variables
03:30
Test of linearity: Non-graphical
02:25
Test of linearity: Graphical
05:14
Binary variables
02:41
Categorical variables
08:26
Multivariate analysis
02:25
Goodness of fit
07:00
Residual analysis
03:02
Influential observations
02:51
Combining both residuals and influence in one graph
05:07
Visualizing the result
03:03

Count Models: Count Tables

4 lectures
Count tables
04:07
Risk
02:05
Inceidence-rate ratio
02:35
Two-by-three tables
02:33

Poisson Regression

6 lectures
Single independent variable
16:44
Examples
05:07
Binary variables
06:12
Multiple independent variables
06:31
Categorical variables
08:21
Exposure
08:26

Other Count Models

4 lectures
Negative binomial regression
07:58
Truncated models
04:02
Zero-inflated models
17:31
Comparison of models
07:39

Prediction

2 lectures
Predicting the number of events
02:53
Predicting probabilities of certain counts
02:44

Count Model Case Study

8 lectures
The dataset
01:35
Continuous variables
06:52
Binary variables
01:12
Multivariate analysis
01:10
Negative binomial regression
01:57
Zero-inflated models
07:25
Comparing count models
03:53
Visualizing the result
03:43

Conclusion

1 lectures
Conclusion
01:49

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