For the following multiple regression which was conducted to attempt to predict the Y variable based on the independent variables shown, answer the following questions. Regression Statistics Multiple R 0.890579188 R Square 0.793131289 Adjusted R Square 0.7379663 Standard Error 30.28395534 Observations 20 ANOVA df SS MS F Regression 4 52743.23074 13185.81 14.37743932 Residual 15 13756.76926 917.1179509 Total 19 66500 Coefficients Standard Error t Stat P-value Intercept 73.33291 62.25276 1.17799 0.25715 X1 -0.13882 0.05353 -2.59326 0.02037 X2 3.73984 0.95568 3.91328 0.00138 X3 0.37644 0.16876 2.23061 0.04140 X4 17.70752 15.57847 1.13667 0.27351 Interpret R Square.
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.
For the following multiple regression which was conducted to attempt to predict the Y variable based on the independent variables shown, answer the following questions.
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Regression Statistics |
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Multiple R |
0.890579188 |
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R Square |
0.793131289 |
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Adjusted R Square |
0.7379663 |
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Standard Error |
30.28395534 |
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Observations |
20 |
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ANOVA |
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df |
SS |
MS |
F |
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Regression |
4 |
52743.23074 |
13185.81 |
14.37743932 |
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Residual |
15 |
13756.76926 |
917.1179509 |
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Total |
19 |
66500 |
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Coefficients |
Standard Error |
t Stat |
P-value |
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Intercept |
73.33291 |
62.25276 |
1.17799 |
0.25715 |
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X1 |
-0.13882 |
0.05353 |
-2.59326 |
0.02037 |
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X2 |
3.73984 |
0.95568 |
3.91328 |
0.00138 |
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X3 |
0.37644 |
0.16876 |
2.23061 |
0.04140 |
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X4 |
17.70752 |
15.57847 |
1.13667 |
0.27351 |
- Interpret R Square.
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