he owner of a movie theater company used multiple regression analysis to predict gross revenue (y) as a function of television advertising (x1) and newspaper advertising (x2). The estimated regression equation was ŷ = 82.9 + 2.27x1 + 1.50x2. The computer solution, based on a sample of eight weeks, provided SST = 25.7 and SSR = 23.435.   (a)Compute and interpret R2 and Ra2. (Round your answers to three decimal places.)   (b) The proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is________ . Adjusting for the number of independent variables in the model, the proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is .   (c) When television advertising was the only independent variable, R2 = 0.653 and Ra2 = 0.595. Do you prefer the multiple regression results? Explain. Multiple regression analysis (select IS or IS NOT)    preferred since both R2 and Ra2 show (Select Increase OR decreased)    percentage of the variability of y explained when both independent variables are used

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Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related. 

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.