(a) State the predictors available in this model. (b) Determine the multiple regression model for this analysis.
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.
(a) State the predictors available in this model.
(b) Determine the multiple regression model for this analysis.
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Model Summary
R Square
Adjusted R
Square
Std. Error of the
Estimate
Durbin-
Watson
Model
842
.710
630
109.430
1.158
ANOVA
Sum of
Squares
Model
Mean Square
Sig.
JP
Regression
321946.82
107315.6
8.96
0.0027
Residual
131723 20
11
11974.8
Total
453670.00
14
Coefficients
Standardized
Coefficients
Unstandardized Cocfficients
Model
B.
Std. Erron
Beta
Sig.
(Constant)
657 053
167 46
3.92
0024
X Variable 1
X Variable 2
X Variable 3
5.7103
1792
-101
319
0087
-0.4169
0 322
077
-1 29
2222
3 4715
1443
-7.996
-2 41
0349
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