Below is some of the regression output from a regression of the amount various customers paid for a new car (expressed in dollars) versus the age of the customer (expressed in years), the number of previous cars the customer had purchased from the dealership in the past, a dummy variable indicating the gender of the customer (=1 for a Man and = 0 for a woman), and an interactive term the multiplies the age of the customer with the gender dummy variable. Regression Statistics Multiple R 0.963 R Square Adjusted R Square Standard Error Observations 20 ANOVA df SS MS F Significance F Regression 24686354.49 6171589 47.8 2.3289E-08 Residual 129243 Total 26625000 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 6579.1 352.1 18.68 0.000 5828.5 7329.6 Age 68.7 37.6 0.089 -11.2 # of Prev. -847 295.3 -2.73 0.016 -1435.0 -176.2 Man -630 326.7 0.051 2.7 Age*Man 15.4 8.8 1.77 0.097 -3.2 34.4 Based on the regression output, how much more or less on average does a customer who has bought 1 previous car pay versus a customer who has not bought a previous car? (please express your answer using 1 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.
QUESTION 13
- Below is some of the regression output from a regression of the amount various customers paid for a new car (expressed in dollars) versus the age of the customer (expressed in years), the number of previous cars the customer had purchased from the dealership in the past, a dummy variable indicating the gender of the customer (=1 for a Man and = 0 for a woman), and an interactive term the multiplies the age of the customer with the gender dummy variable.
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Regression Statistics |
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Multiple R |
0.963 |
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R Square |
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Adjusted R Square |
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Standard Error |
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Observations |
20 |
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ANOVA |
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df |
SS |
MS |
F |
Significance F |
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Regression |
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24686354.49 |
6171589 |
47.8 |
2.3289E-08 |
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Residual |
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129243 |
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Total |
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26625000 |
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Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
Upper 95% |
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Intercept |
6579.1 |
352.1 |
18.68 |
0.000 |
5828.5 |
7329.6 |
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Age |
68.7 |
37.6 |
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0.089 |
-11.2 |
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# of Prev. |
-847 |
295.3 |
-2.73 |
0.016 |
-1435.0 |
-176.2 |
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Man |
-630 |
326.7 |
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0.051 |
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2.7 |
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Age*Man |
15.4 |
8.8 |
1.77 |
0.097 |
-3.2 |
34.4 |
Based on the regression output, how much more or less on average does a customer who has bought 1 previous car pay versus a customer who has not bought a previous car? (please express your answer using 1 decimal places)
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