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# Multiple regression

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My data set contains five variables for 96 nations in the world.

Onlinepop Online Population
PC's Number of Personal Computeres
Phones Number of landline phones
Educ Percent of GNP spent on education
GNPPC Gross National Product per capita

Here is correlation matrix on data set:

Assignment was to Regress Onlinepop against PC's, Phones, and Educ and then to regress the predicted values of the dependent variable Onlinepop against the residuals resulting in the following scatterplot in order to detect for heteroskedasticity.
Next based on results of model I was to consider three scenarios. First, triple Education expenditure. Second, double PC's. Third, double Phones. Here are results of model. R square was .98

QUESTION - I don't understand why variable Educ has a negative coefficient. Intuitively, I would expect a positive sign. Can you explain why? Could it be multilcollinearity or is it the heteroskedasticity at work?

See attached file for full problem description.

https://brainmass.com/math/interpolation-extrapolation-and-regression/multiple-regression-predicted-values-1846

#### Solution Preview

PROBLEM 1846 - MULTIPLE REGRESSION

My data set contains five variables for 96 nations in the world.

Onlinepop Online Population
PC's Number of Personal Computers
Phones Number of landline phones
Educ Percent of GNP spent on education
GNPPC Gross National Product per capita

Here is correlation matrix on data set:

Onlinepop PCs Phones Educ GNPPC
Onlinepop 1
PCs 0.990643 1
Phones 0.319927 0.275276 1
Educ 0.049997 0.049423 0.369801 1
GNPPC 0.509078 0.477851 0.874735 0.318304 1

Assignment was to Regress Onlinepop against PC's, Phones, and Educ and then to regress the predicted values of the dependent variable Onlinepop against the residuals resulting in the following scatter plot in order to detect for heteroskedasticity.

Next based on results of model I was to consider three scenarios. First, triple Education expenditure. Second, double PC's. Third, double Phones. Here are results of model. R square was .98

Coefficients Standard Error T stat ...

#### Solution Summary

This shows how to regress the predicted values of a dependent variable against the residuals in order to detect for heteroskedasticity.

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