A microcomputer manufacturer has developed a regression model relating his sales (y=$10,000s) with three independent variables. The three independent variables are price per unit(Price in $100s), advertising( ADV in $1000s) and the number of product lines (Lines). Part of the regression results is shown below. Coefficient Standard Error Intercept 1.0211 22.8752 Price(X1) -0.1524 0.1411 ADV (X2) 0.8849 0.2886 Lines(X3) -0.1463 1.5340 Source d.f. S.S. Regression 3 2708.61 Error 14 2840.51 Total 17 5549.12 What has been the sample size (n) for this analysis? Use the above results to find the estimated multiple regression equation that can be used to predict sales. Interpret the meaning of the coefficient of X2 If the manufacturer has 10 product lines, advertising of $40,000, and the price per unit is $3,000, what is your estimate of their sales? Give answer in dollars.
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.
A microcomputer manufacturer has developed a regression model relating his sales (y=$10,000s) with three independent variables. The three independent variables are price per unit(Price in $100s), advertising( ADV in $1000s) and the number of product lines (Lines). Part of the regression results is shown below.
Coefficient Standard Error
Intercept 1.0211 22.8752
Price(X1) -0.1524 0.1411
ADV (X2) 0.8849 0.2886
Lines(X3) -0.1463 1.5340
Source d.f. S.S.
Regression 3 2708.61
Error 14 2840.51
Total 17 5549.12
- What has been the sample size (n) for this analysis?
- Use the above results to find the estimated multiple regression equation that can be used to predict sales.
- Interpret the meaning of the coefficient of X2
- If the manufacturer has 10 product lines, advertising of $40,000, and the price per unit is $3,000, what is your estimate of their sales? Give answer in dollars.
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