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Solution: a. Regression model: Sales = 119.59 - 12.163 Price + 2.32 Advert + 13.23 MDH b. Goodness of Fit Multiple R: 0.99375 or 99.39% There is almost perfect correlation between sales and the Price, Advert and Hours of Sunshine. R Square: 0.987936 or 98.78% 98% sales are accounted for by the variations of the Price, Advert and Hours of METHOD USED: MULTIPLE REGRESSION (R) AND CORRELATION COEFFICIENT Multiple Regression Multiple regression is the most common data mining technique for predicting the future value of variable based on with other variables[13][15]. The most basic use of the regression equation is to make predictions. We can predict the MULTIPLE REGRESSION 4 Data checks Amount of data Power is concerned with how likely a hypothesis test is to reject the null hypothesis, when it is false. For regression, the null hypothesis states that there is no relationship between X and Y. If the data set is too small, the power of the test may not be adequate to detect a relationship Regression Analysis | Chapter 3 | Multiple Linear Regression Model | Shalabh, IIT Kanpur 4 Assumptions in multiple linear regression model Some assumptions are needed in the model yX for drawing the statistical inferences. The following assumptions are made: (i) ( ) 0E (ii) (')2 E In (iii) Rank X k() Section 1: Introduction. 1.1 Overview. A multiple linear regression analysis is carried out to predict the values of a dependent variable, Y, given a set of p explanatory variables (x1,x2,….,xp). In these notes, the necessary theory for multiple linear regression is presented and examples of regression analysis with census data are