Regression and Time Series Analysis
Setting up linear regression (33:40)
4 Topics

6 Quizzes
Overview of data science (8:11)
Concept review 1: overview of data science
What is regression? (7:51)
Concept review 2: what is regression?
Building linear regression (10:23)
Concept review 3: building linear regression
Exercise set 1: basic linear regression (5:13)
Assessing your model’s accuracy (7:15)
Concept review 4: assessing your model’s accuracy
Feedback for “Setting up linear regression”
Measuring model errors (38:13)
4 Topics

7 Quizzes
Calculating variance and standard deviation (8:11)
Concept review 5: calculating variance and standard deviation
Identifying outliers (11:26)
Concept review 6: identifying outliers
Exercise set 2: finding residuals and outliers (9:52)
Calculating covariance and correlation (8:05)
Concept review 7: calculating covariance and correlation
Testing a model’s significance (9:59)
Concept review 8: testing a model’s significance
Exercise set 3: calculating statistical significance (6:47)
Feedback for “Measuring model errors”
Modeling multiple variables (31:21)
4 Topics

7 Quizzes
Building a multivariate model (8:00)
Concept review 9: building a multivariate model
Exercise set 4: visualizing correlations (5:22)
Plotting multivariate regression (8:56)
Concept review 10: plotting multivariate regression
Identifying multicollinearity (6:33)
Concept review 11: identifying multicollinearity
Exercise set 5: multivariate regression (8:55)
Datafying categorical variables (7:52)
Concept review 12: datafying categorical variables
Feedback for “Modeling multiple variables”
Adjusting your model (38:01)
4 Topics

8 Quizzes
Validating your model (9:37)
Concept review 13: validating your model
Exercise set 6: categorical variables (7:34)
Testing for heteroscedasticity (7:51)
Concept review 14: testing for heteroscedasticity
Identifying important variables (11:40)
Concept review 15: identifying important variables
Exercise set 7: model selection (9:45)
Building nonlinear regression models (8:53)
Concept review 16: building nonlinear regression models
Exercise set 8: building nonlinear regression (7:35)
Feedback for “Adjusting your model”
Adding seasonality to your model (35:40)
4 Topics

6 Quizzes
Transforming variables (10:03)
Concept review 17: transforming variables
Identifying seasonality (7:31)
Concept review 18: identifying seasonality
Calculating seasonality (9:19)
Concept review 19: calculating seasonality
Exercise set 9: calculating seasonality (10:19)
Minimizing seasonality errors (8:47)
Concept review 20: minimizing seasonality errors
Feedback for “Adjusting seasonality to your model”
Refining your model (29:33)
5 Topics

7 Quizzes
Calculating seasonality components (6:34)
Concept review 21: calculating seasonality components
Predicting customer demand (7:35)
Concept review 22: predicting customer demand
Exercise set 10: HoltWinters modeling (5:13)
Using the LOESS method (6:52)
Concept review 23: using the LOESS method
Additional considerations and tips (8:32)
Concept review 24: additional considerations and tips
Exercise set 11: running LOESS models (9:42)
Feedback for “Refining your model”
End of course feedback survey
Next Lesson
Setting up linear regression (33:40)
Regression and Time Series Analysis
Setting up linear regression (33:40)
Lesson Content
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0/4 Steps
Overview of data science (8:11)
Concept review 1: overview of data science
What is regression? (7:51)
Concept review 2: what is regression?
Building linear regression (10:23)
Concept review 3: building linear regression
Exercise set 1: basic linear regression (5:13)
Assessing your model’s accuracy (7:15)
Concept review 4: assessing your model’s accuracy
Feedback for “Setting up linear regression”
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