Answer the following questions after taking a quick look at this data table:

New Car Sales Data.png

Question $1(2$ points$)$

Is this cross$-$sectional data table appropriate to start regression analysis?

Explain Why or Why Not?

Question $2(1$ point$)$

The number of included independent variables $(6)$ is large enough to start regression analysis.

A$.$ True

B$.$ False

Question $3(1$ point$)$

Multiple regression analysis must be done only using data collected by periods, such as days, months, quarters, years, etc.

A$.$ True

B$.$ False

Question $4(2$ points$)$

If Personal Income and Unemployment Rate had a correlation coefficient of $0\mathrm{.}88,$ how would you handle these independent variables?

Question $5(1$ point$)$

If the final regression output indicates that Gas Prices has a P$-$value of $0\mathrm{.}05,$ this independent variable should not be included in the multiple regression model.

A$.$ True

B$.$ False

Question $6(1$ point$)$

Once the multiple regression model is completed$/$finalized$,$ it can be applied to forecast the dependent variable Car Sales by plugging in the most accurate forecasts for period t$+1$ of each independent variable $($done by MA$,$ WMA, or ES$).$

A$.$ True

B$.$ False

Question $7(2$ points$)$

If my co$-$worker in the Marketing Research department reports the company$$s Advertising Expenses for period t$+1(2021),$ I should plug this planned number in my model instead of the one that I had forecast $($through MA$,$ WMA, or ES$)$ for Advertising Expenses for $2021.$

A$.$ True

B$.$ False