Question 4. -20 points (-30 minutes) An ordinary least squares regression of ope

Question

Question 4. -20 points (-30 minutes) An ordinary least squares regression of operating profits (PROFIT) for a sample of 50 recent start-up firms in Georgia on their dollar sales (SALES) and the number of employees (NEMP) yields: PROFIT.=-1143.9 + 0.439 SALES-34.44 NEMP+e R2=0.87 (2488.3) (0.0354) (6.510) where the figures in parentheses are the estimated standard errors of the estimates, i is the firm and e is the estimated residual. All dollar values (PROFIT and SALES) are measured in $1000's. Note that: -11439-04597 0.0354 013.12.40 1: 0.439 -6510 =-5 = 6510 5.290 24883 (a) (10 points) Interpret the results of the reported regression. (b) (10 points) The accountant, who supplied the data, warns that heteroscedasticity is likely to be a problem. Explain why this may be the case and how the interpretation of the above results are affected if heteroscedasticity is actually present.

Explanation / Answer

(a) Here the reported regression have R- square value = 0.87 which is very high as we can say that the model is significant here. Here as t value for each indepenent variable if we notice thenweknow that t value for sales and NEMP is very high that means both the regression variables are significant.

As we can also see that t value for intercept is not signifcant so we can interpret that there is varaition in profit is due to the independent variables only. Intercept can be interpreted as zero.

Here as sales coefficient is positve and have value equals to 0.439 that means one dollar increase in sale will increase 0.439 dollar increase in profit.

Similarly, the NEMP coefficient is negative in nature and have value equals -34.44 that means that an increase in number of employees will decreae $ 34.44 dollar in profit.

(b) Here heteroscedecsticity can be problem as data dispersion may a problem when there is higher sales and higher sales persons are there. Here this may be problem because of intercept non signifcance in nature. The coefficient have very high value and can affect the varianceof data on eithere side of regression plot.

Here  while the ordinary least squares estimator is still unbiased in the presence of heteroscedasticity, it is inefficient because the true variance and covariance are underestimated so that would produce ineffective results for the regression analysis.

Related Questions

Navigate

• Browse (All)
• Browse Q
• Subjects

---

---

Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.