Develop a scatterplot for these variables with mean base salary as the dependent variable.

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Problem set 8

Regression

1. Access bschools2002.xls which contains data regarding the top 30 business schools based on the 2002 Business week ratings. Students from better schools might command a higher salary. Comparing a school’s mean base salary to its rank might help us understand this relationship.

Use R to solve this exercise.

(a) Develop a scatterplot for these variables with mean base salary as the dependent variable.

(b) Does this relationship appear linear?

(e) Use R to perform a regression of mean base salary vs. rank. Write the estimated regression equation.

(d) Use your regression equation to estimate the mean base salary for a school ranked eighth.

(e) Use your regression equation to estimate the mean base salary for a school ranked 25th.

(f) Use the coefficient on the rank variable to estimate the expected increase in mean base salary from a one-unit improvement in a school’s rank. Provide a 95% confidence intervaL for your estimate.

(g) How confident are you that the true slope, ß, is significantly different from zero?

2. Estimating betas

Access the stock.xls dataset and use it to estimate betas for the following stocks: Apple, IBM and HP. Suppose the excess returns on the stock market (as measured by the S&P500, stored under ESP in the dataset) were to be negative 20% next month.

(a) Vhat would you expect to be the excess return on Apple shares next month? How about IBM and HP shares? Base your estimate on the estimated beta and the theoretical CAPM equation; that is, discard the estimated constant (alpha) as we did in the chapter.  

(b) How much money would you expect to lose next month if you had $ 10,000 invested in Apple shares at the beginning of the month? For the purposes of answering this part of the question only, assume that the risk free rate next month is 0.25%.

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