"No one is harder on a talented person than the person themselves" - Linda Wilkinson ; "Trust your guts and don't follow the herd" ; "Validate direction not destination" ;

March 10, 2016

R Day #1 - Simple Linear Regression

UCLA Notes were very useful.

Linear Regression Model - Representing mean of response variable as function using slope and intercept parameters. Can be used for predictions. I have earlier used moving average algorithm for forecasting.
  • Simple Linear Regression - Explanatory variable is 1 (Dependent variable is 1)
  • Multivariate Linear Regression - Number of Explanatory variables more than 1
Good Summary of Data Quality Issues were summarized
  • Data-entry errors
  • Missing values
  • Outliers
  • Unusual (e.g. asymmetric) distributions
  • Unexpected patterns
R Cookbook had good step by step examples to try out - link

Happy Learning!!!

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