Question: Use the data in DISCRIM.RAW to answer this question. (See also Computer Exercise C3.8 in Chapter 3.)

1. Use OLS to estimate the model
   \[\log (\text { psoda })=\beta_{0}+\beta_{1} \text { prpblck }+\beta_{2} \log (\text { income })+\beta_{3} p r p p o v+u\]
   and report the results in the usual form. Is \(\hat{\beta}_{1}\) statistically different from zero at the \(5 \%\) level against a two-sided alternative? What about at the \(1 \%\) level?
2. What is the correlation between log(income) and prppov? Is each variable statistically significant in any case? Report the two-sided \(p\) -values.
3. To the regression in part (i), add the variable \(\log (h\) seval \() .\) Interpret its coefficient and report the two-sided \(p\) -value for \(\mathrm{H}_{0}: \beta_{\text {log(hseval })}=0\)
4. In the regression in part (iii), what happens to the individual statistical significance of log(income) and prppov? Are these variables jointly significant? (Compute a \(p\) -value.) What do you make of your answers?
5. Given the results of the previous regressions, which one would you report as most reliable in determining whether the racial makeup of a zip code influences local fast-food prices?

Price: $2.99

Solution: The downloadable solution consists of 3 pages
Deliverable: Word Document