Analyzed the data from the two tables beow.
Coefficients^{a} |
||||||||||||||||
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||||||||||||
B |
Std. Error |
Beta |
||||||||||||||
1 |
(Constant) |
7.029 |
.059 |
119.307 |
.000 |
|||||||||||
Q1. Age |
.027 |
.001 |
.090 |
17.839 |
.000 |
|||||||||||
a. Dependent Variable: Trust in Government Index (higher scores=more trust) |
||||||||||||||||
Model Summary |
||||
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
1 |
.090^{a} |
.008 |
.008 |
4.18980 |
a. Predictors: (Constant), Q1. Age |
Here the first table shows the coefficient of fitted regression lines. The fitted line equation is
From the p value of the table we can infer that both the variables are significant. Since then value is less than 0.05 hence we reject the null hypothesis of coefficient is equals zero.the standard error value is also given in the first table.. Table two is the model summary. From the table we can find that the adjusted r squared value is 0.008.ie, the model can explain 0.8% of the total variation. Hence the model is not a good one. The model is better when it approaches to 100.
Get Answers For Free
Most questions answered within 1 hours.