Part I requires estimation of Ordinary Least Squares (OLS) regression models and interpretation of the results using SPSS.

Part I: Regression Analysis 

The main objective of this component of the individual project is to 1) run the regression using OLS and 2) to write up a concise report, discussing and analyzing your results, based on suitable results Tables and Graphs.  

Step 1: Obtain the Data The excel dataset “CEO.xlsx” contains information on chief executive officers for UK corporations in 2016. 

Step 2: Run the Regression You are required to run the following regression model using OLS: 

Yi=alpha+beta1RoAi+beta2Sizei+beta3sigmai+beta4ceoteni+beta5femalei+beta6foreigni+epsiloni

where:   Yi is the log salary for CEO i;  

RoAi is the return on assets % for firm i; 

Sizei is measured by the log of firm i’s total assets; 

sigmai is the volatility measured by the daily return standard deviation (%);

ceoteni is the number of years as CEO with company i;  

femalei is a dummy variable, = 1 if CEO is female, 0 otherwise;  

foreigni is a dummy variable, = 1 if CEO is foreign, 0 otherwise.

Empirical Discussions 

1) Report the results in a simple table – which should include the estimated coefficients, their standard errors, the value of 2 R , adj- 2 R , and any other statistics you think are appropriate.

 2) Discuss your findings, for example, what this equation means and how it can be justified? What are the interpretations of the estimated coefficients? Are the estimated coefficients statistically significant? How well does the model describe the relationship between the variables? Do the assumptions underlying the model hold?