Understanding Regression Techniques

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7:09:56

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1.Simple Linear Regression

01.Introduction.mp4

03:15

02.Simple linear regression.mp4

04:26

03.The slope.mp4

05:29

04.R-squared.mp4

05:30

05.The p-value.mp4

07:06

06.Model fit.mp4

02:34

07.The residuals.mp4

05:16

2.Multiple linear regression

08.Multiple linear regression.mp4

03:09

09.The slopes.mp4

05:10

10.R-squared.mp4

01:38

11.The p-value.mp4

01:12

12.Model fit and residuals.mp4

02:02

3.Linear Regression - Binary, Categorical, and Quadratic Variables

13.Binary variables.mp4

08:37

14.Categorical variables.mp4

11:30

15.Quadratic variables.mp4

07:23

4.Linear Regression - Checking Model Fit and Assumptions

16.Prediction.mp4

04:27

17.Normality of residuals.mp4

02:20

18.Independence of residuals.mp4

02:25

19.Constant variance.mp4

02:00

20.Multicollinearity.mp4

02:49

21.Outliers.mp4

04:10

22.Influential observations.mp4

04:33

23.Selection algorithms.mp4

08:41

5.Linear Regression Case Study

24.The dataset.mp4

03:37

25.Including continuous variables.mp4

10:32

26.Including binary variables.mp4

02:22

27.Including categorical variables.mp4

02:17

28.Multiple regression.mp4

03:51

29.Checking model fit.mp4

02:55

30.Checking model assumptions.mp4

06:41

31.Multicollinearity.mp4

02:06

32.Outliers.mp4

03:27

33.Influential observations.mp4

04:37

34.Visualizing the result.mp4

03:09

6.Logistic Regression - Contingency Tables

35.Two-by-two tables.mp4

04:03

36.The odds.mp4

03:21

37.The odds ratio.mp4

02:37

38.Two-by-three tables.mp4

07:16

7.Logistic Regression Models

39.Single independent variable.mp4

12:51

40.Examples.mp4

05:06

41.Binary variables.mp4

06:30

42.Multiple independent variables.mp4

05:39

43.Categorical variables.mp4

08:34

44.Nonlinearity - Non-graphical test.mp4

04:06

45.Nonlinearity - Graphical test.mp4

06:51

8.Logistic Regression - Prediction and Model Fit

46.Prediction.mp4

03:58

47.Goodness of fit - Likelihood ratio test.mp4

02:04

48.Goodness of fit - Hosmer-Lemeshow test.mp4

03:44

49.Goodness of fit - Classification tables.mp4

08:28

50.Goodness of fit - ROC analysis.mp4

01:41

51.Residuals.mp4

02:23

52.Influential Observations.mp4

05:00

9.Logistic Regression Case Study

53.The dataset.mp4

03:47

54.Continuous variables.mp4

03:30

55.Test of linearity - Non-graphical.mp4

02:25

56.Test of linearity - Graphical.mp4

05:14

57.Binary variables.mp4

02:41

58.Categorical variables.mp4

08:26

59.Multivariate analysis.mp4

02:25

60.Goodness of fit.mp4

07:00

61.Residual analysis.mp4

03:02

62.Influential observations.mp4

02:51

63.Combining both residuals and influence in one graph.mp4

05:07

64.Visualizing the result.mp4

03:03

10.Count Models - Count Tables

65.Count tables.mp4

04:07

66.Risk.mp4

02:05

67.Inceidence-rate ratio.mp4

02:35

68.Two-by-three tables.mp4

02:33

11.Poisson Regression

69.Single independent variable.mp4

16:44

70.Examples.mp4

05:07

71.Binary variables.mp4

06:12

72.Multiple independent variables.mp4

06:31

73.Categorical variables.mp4

08:21

74.Exposure.mp4

08:26

12.Other Count Models

75.Negative binomial regression.mp4

07:58

76.Truncated models.mp4

04:02

77.Zero-inflated models.mp4

17:31

78.Comparison of models.mp4

07:39

13.Prediction

79.Predicting the number of events.mp4

02:53

80.Predicting probabilities of certain counts.mp4

02:44

14.Count Model Case Study

81.The dataset.mp4

01:35

82.Continuous variables.mp4

06:52

83.Binary variables.mp4

01:12

84.Multivariate analysis.mp4

01:10

85.Negative binomial regression.mp4

01:57

86.Zero-inflated models.mp4

07:25

87.Comparing count models.mp4

03:53

88.Visualizing the result.mp4

03:43

15.Conclusion

89.Conclusion.mp4

01:42

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Regression Analysis

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