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Regression and Time Series Analysis

Course Navigation

Setting up linear regression (33:40)
  • Overview of data science (8:11)
  • What is regression? (7:51)
  • Building linear regression (10:23)
  • Assessing your model's accuracy (7:15)
Measuring model errors (38:13)
  • Calculating variance and standard deviation (8:11)
  • Identifying outliers (11:26)
  • Calculating covariance and correlation (8:05)
  • Testing a model's significance (9:59)
Modeling multiple variables (31:21)
  • Building a multivariate model (8:00)
  • Plotting multivariate regression (8:56)
  • Identifying multicollinearity (6:33)
  • Datafying categorical variables (7:52)
Adjusting your model (38:01)
  • Validating your model (9:37)
  • Testing for heteroscedasticity (7:51)
  • Identifying important variables (11:40)
  • Building nonlinear regression models (8:53)
Adding seasonality to your model (35:40)
  • Transforming variables (10:03)
  • Identifying seasonality (7:31)
  • Calculating seasonality (9:19)
  • Minimizing seasonality errors (8:47)
Refining your model (29:33)
  • Calculating seasonality components (6:34)
  • Predicting customer demand (7:35)
  • Using the LOESS method (6:52)
  • Additional considerations and tips (8:32)
  • End of course feedback survey
Return to Regression and Time Series Analysis

STATS

Setting up linear regression (33:40)

Posted May 23, 2016 by Cary Rothschild

Lesson Topics
  • Overview of data science (8:11)
  • What is regression? (7:51)
  • Building linear regression (10:23)
  • Assessing your model's accuracy (7:15)

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