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 non-linear 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: Holt-Winters 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

Exercise set 1: basic linear regression (5:13)

Regression and Time Series Analysis Setting up linear regression (33:40) Building linear regression (10:23) Exercise set 1: basic linear regression (5:13)

How can you build a linear regression model?

Click here to download the R scripts to follow along with the video and then practice these new skills on your own. Once you answer the quiz question, you will be able to download the solutions to the exercise.