The marketing research department of a large company knows that the company's monthly sales are influenced by the way in which it spends money on marketing. For example, monthly expenditures such as the ones listed below are known to have an effect on y, the company's total monthly sales (in millions of dollars). x = money spent on television advertising (in 1000's of dollars) x, = money spent on promotion (i.e., free samples) x3 = money spent on newspaper advertising (in 1000's of dollars) x = average discounts offered to retail outlets (in %) Using data from the previous 16 months, the company decides to collect data on 2 of the independent variables to use in a multiple regression model for estimating monthly sales. If the R' for this model is 0.81, fill in the missing entries in the ANOVA table associated with this model. Do all calculations to at least three decimal places.
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.
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