7. Write the regression equation (use a and b provided in the Coefficients Table) 8. Interpret each of the terms of the regression equation you constructed in question 7: That is, interpret: X, Ŷ, a and b 9. Use the regression equation you constructed in question 7 to predict: If a person has 112 months of previous experience, what is the person’s predicted current salary? 10. Use the regression equation you constructed in 7 to predict: If a person has 400 months of previous experience what is the person’s predicted current salary?
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.
7. Write the regression equation (use a and b provided in the Coefficients Table)
8. Interpret each of the terms of the regression equation you constructed in question 7: That is, interpret: X, Ŷ, a and b
9. Use the regression equation you constructed in question 7 to predict:
If a person has 112 months of previous experience, what is the person’s predicted current salary?
10. Use the regression equation you constructed in 7 to predict:
If a person has 400 months of previous experience what is the person’s predicted current salary?
![Output1.spv [Document1] - IBM SPSS Statistics Viewer
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itput
b. All requested variables entered.
Log
Regression
Title
Model Summary
G Notes
L Active Dataset
A Variables Entered/Removed
A Model Summary
A ANOVA
Adjusted R
Square
Std. Error of
Model
R
R Square
the Estimate
1
.097a
.009
.007
104.198
a. Predictors: (Constant), Current Salary
Coefficients
ANOVA
Sum of
Model
Squares
df
Mean Square
F
Sig.
1
Regression
49150.138
1
49150.138
4.527
.034b
Residual
5124656.672
472
10857.323
Double-click to
Total
5173806.810
473
activate
a. Dependent Variable: Previous Experience (months)
b. Predictors: (Constant), Current Salary
Coefficients
Standardized
Unstandardized Coefficients
Coefficients
Model
Std. Error
Beta
Sig.
(Constant)
116.408
10.778
10.800
.000
Current Salary
-.001
.000
-.097
-2.128
.034
a. Dependent Variable: Previous Experience (months)
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