The below multiple linear regression model regresses calories on sugar (gm/servi

Question

The below multiple linear regression model regresses calories on sugar (gm/serving) and fiber (gm/serving). Examine the graphical and numerical output for Question A in the output packet to help you answer the following questions.

Will there likely be high multicollinearity (e.g., VIF > 5) between fiber and sugar based on the infomation below? Briefly explain your reasoning.

model l (Calories Sugar+Fiber); summary (model) Estimate Std. Error t value Pr (>ltD (Intercept) 109.3082 6.3913 17.103 K 2e-16 1.0050 0.134 Sugar 2.6546 1.535 Fiber 0.8346 4.486 8.31e-05 3.7442

Explanation / Answer

If we consider the correlation matrix, the is a strong negative correlation between fiber and sugar.

As the correlation between this pair is = -0.715.

This implies that there arises a high multicollinearity between this two pair.

That means the model contain some redundencies that we have to eliminate.

Due to this we can correctly estimate the coefficients.

This will improve our model.

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