Below you are given a partial computer output from a multiple regression analysis based on a sample of 16 observations. Coefficients Standard Error Constant 12.924 4.425 x1 -3.682 2.630 x2 45.216 12.560 Analysis of Variance Source of Variation Degrees of Freedom Sum of Squares Mean Square F Regression 4853 2426.5
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.
| Coefficients | Standard Error | |||
| Constant | 12.924 | 4.425 | ||
| x1 | -3.682 | 2.630 | ||
| x2 |
45.216 | 12.560 | ||
| Analysis of Variance | ||||
| Source of Variation |
Degrees of Freedom |
Sum of Squares |
Mean Square |
F |
| Regression | 4853 | 2426.5 | ||
| Error | 485.3 |
The interpretation of the coefficient of x1 is that
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