Consider the accompanying table set of dependent and independent variables. The table is at the end of the page. a) perform a general stepwise regression using a= 0.05 for the p-value to enter and to remove independent variables from the regression model. -use technology to perform the general stepwise regression. Note that the coefficient is 0 for any variable that was removed or not significant. y= (__) + (__)x1 + (__)x2 + (__)x3 b) perform a residual analysis for the model developed in part a to verify that the regression conditions are met. y x1 x2 x3 63 74 22 21 43 63 29 15 51 78 20 9 49 52 17 38 40 44 13 18 42 47 17 17 23 35 8 5 37 17 15 40 30 15 10 27 27 20 10 30 20 17 7 33
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.
Consider the accompanying table set of dependent and independent variables. The table is at the end of the page.
a) perform a general stepwise regression using a= 0.05 for the p-value to enter and to remove independent variables from the regression model.
-use technology to perform the general stepwise regression. Note that the coefficient is 0 for any variable that was removed or not significant.
y= (__) + (__)x1 + (__)x2 + (__)x3
b) perform a residual analysis for the model developed in part a to verify that the regression conditions are met.
| y | x1 | x2 | x3 |
| 63 | 74 | 22 | 21 |
| 43 | 63 | 29 | 15 |
| 51 | 78 | 20 | 9 |
| 49 | 52 | 17 | 38 |
| 40 | 44 | 13 | 18 |
| 42 | 47 | 17 | 17 |
| 23 | 35 | 8 | 5 |
| 37 | 17 | 15 | 40 |
| 30 | 15 | 10 | 27 |
| 27 | 20 | 10 | 30 |
| 20 | 17 | 7 | 33 |
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