Based on the given multiple regression output tables below, answer the following questions: iBerdasarkan kepada jadual dapatan regresi berganda yang dinyatakan di bawah, jawab soalan-soalan jung berikut:) Model Summary Adjusted R Square Std. Eror of the Estimate Durbin- Model R Square Watson 842 710 630 109.430 LIS8 ANOVA Sum of Model Squares df Mean Square Regression 321946.82 107315.6 8.96 0.0027 Residual 131723.20 11974.8 Total 453670 00 14 Coefficients Standardized Coefficients Unstandardized Coefficients Model B Sd. Error Beta Sig (Constant) 657.053 167.46 3.92 0024 X Variable I X Variable 2 X Variable 3 5.7103 1.792 101 3.19 0087 0.4169 0.322 077 -1.29 2222 -3.4715 1443 -7.996 -241 0349
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.
state the predictor available in this model
![Based on the given multiple regression output tables below, answer the following
questions:
Berdasarkan kepada jadual dapatan regresi berganda yang đinyatakan di bawah, jawab soalan-soalan
yang berikut:]
Model Summary
Durbin-
Adjusted R
Square
Std. Error of the
Model
R
R Square
Estimate
Watson
842
.710
630
109.430
LI58
ANOVA
Sum of
Model
Squares
df
Mean Square
Sig
Regression
321946.82
107315.6
8.96
0.0027
131723 20
453670 00
Residual
11974.8
Total
14
Cocfficients
Standardized
Coefficients
Unstandardized Coefficients
Model
B
Std. Error
Beta
Sig
(Constant)
657.053
167.46
3.92
0024
X Variable I
X Variable 2
X Variable 3
5.7103
1.792
- 101
3.19
0087
0.4169
0.322
077
-1.29
2222
-3.4715
1443
-7.996
-241
0349](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F97f615d6-254b-4a87-a278-d839a2cde8b0%2Fdcf9442b-af38-41cd-89e0-0fbe1b967ac2%2F41ku6y_processed.png&w=3840&q=75)
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