How do you analyse the regression analysis of the table below: Table 4.10: Regre

Question

How do you analyse the regression analysis of the table below:

Table 4.10: Regression analysis (Contribution of different predicting items [A1] and the predicted variable[A2] )

Model

R

R square

Adjusted R square

Std. error of the estimate

R square change

F change

Sig. F change

1

.26

.07

.06

.80

.07

31.55

.000

2

.37

.13

.13

.77

.07

36.29

.000

a Model with variable 1 predicting variable 3

b Model with variable 1 and variable 2 predicting variable 3

Model

R

R square

Adjusted R square

Std. error of the estimate

R square change

F change

Sig. F change

1

.26

.07

.06

.80

.07

31.55

.000

2

.37

.13

.13

.77

.07

36.29

.000

Explanation / Answer

The 2 models are both significant and when multiple predictors are involved, the adjusted R square is considered rather than R square as adjusted R square penalises for the larger number of predictors.

Here the Adjusted R square is higher for model 2, although 0.13 is not good enough. This means 13% of the variation in variable 3 is explained by this model and we will choose model 2 due to this reason over model 1

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