ANOVA^{b} 

Model 
Sum of Squares 
df 
Mean Square 
F 
Sig. 

1 
Regression 
109,780 
3 
36,593 
617,763 
,030^{a} 
Residual 
10,722 
181 
,059 

Total 
120,501 
184 

a. Predictors: (Constant), F4, F2, F3 

b. Dependent Variable: F1 
Coefficients^{a} 

Model 
Unstandardized Coefficients 
Standardized Coefficients 
t 
Sig. 

B 
Std. Error 
Beta 

1 
(Constant) 
,356 
,105 
3,373 
,001 

F2 
,269 
,026 
,699 
2,997 
,030 

F3 
,030 
,028 
,570 
2,103 
,021 

F4 
,859 
,024 
,989 
1,112 
,141 

a. Dependent Variable: F1 
a Write down the hypothesis, p value and your conclusion.
b Can you predict satisfaction score by using any of the independent variables? If yes, what are these variables write down the hypothesis for each of the variable and give your conclusion.
c What is the most important variable?
(a) The hypothesis being tested is:
H_{0}: β_{1} = β_{2} = β_{3} = 0
H_{1}: At least one β_{i} ≠ 0
The pvalue is 0.030.
Since the pvalue (0.030) is less than the significance level (0.05), we can reject the null hypothesis.
Therefore, we can conclude that the model is significant.
(b) The hypothesis being tested is:
H_{0}: β_{1} = 0
H_{1}: β_{1} ≠ 0
The pvalue is 0.030.
Since the pvalue (0.030) is less than the significance level (0.05), we can reject the null hypothesis.
Therefore, we can conclude that the slope, F2, is significant.
The hypothesis being tested is:
H_{0}: β_{2} = 0
H_{1}: β_{2} ≠ 0
The pvalue is 0.021.
Since the pvalue (0.021) is less than the significance level (0.05), we can reject the null hypothesis.
Therefore, we can conclude that the slope, F3, is significant.
(c) F3
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