Reading the Regression Analysis

• Lets look at one more sample data and try to read the Regression Analysis Summary. Refer Here for the dataset

• The Summary which we got is as shown below

• What we really want?

  • Regression equation:
    • y-hat = 528.06717 + 14.833x
  • Significance
  • Explanatory power

• Regression Statistics

• ANOVA:

• Multiple R: This is absolute value of teh correlation coeffecient of two variables (X and Y)

• Standard error: This is measure of precision of the model

• R-squared = measures how much of total variability is explained by your model

• Adjusted R-squared is the modified version of R-squared that has been adjust for number of predictors in the model

  • Multivariate Regressions are always better than univariate, as with each additonal value you add the explanatory power may increase or stay the same

• F-Statistic: This tests the overall significance of the model

  • H0 : b1 = b2 = b3...bk=0
  • H1: at least one of b(i) !=0

Multiple Linear Regression model

• So far in Single linear regression
• So in multiple linear regression