With an analysis of a simple regression model, what is the effect of: 1) increas

Question

With an analysis of a simple regression model, what is the effect of:
1) increasing the sample size?
2) projecting the regression line further into the future, as in the forecasting application?
3) using very homogeneous data (as opposed to using more heterogenous data)?
With an analysis of a simple regression model, what is the effect of:
1) increasing the sample size?
2) projecting the regression line further into the future, as in the forecasting application?
3) using very homogeneous data (as opposed to using more heterogenous data)?

1) increasing the sample size?
2) projecting the regression line further into the future, as in the forecasting application?
3) using very homogeneous data (as opposed to using more heterogenous data)?

Explanation / Answer

Part 1

Increasing the sample size in the analysis of the simple regression model decreases the p-value of the overall regression model and which tends to rejecting the null hypothesis that given regression model is not statistically significant. This means, when sample size in the analysis of the simple regression model increases, then there is more chance of getting statistically significant results.

Part 2

For the future use of the regression equation, it is important to check whether the statistical model is statistically significant or not for the prediction of dependent variable under study. We can check if statistical model is significant by using p-value approach. Insignificant regression models would be rejected for forecasting application.

Part 3

For using very homogeneous data for the simple regression model, there would be a problem of interpolation and extrapolation. These models would be avoided during the prediction of the dependent variable. We actually consider homogenous assumption, but we are dealing with very high homogeneity in the data.

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