Saturday, September 7, 2019

Econometrics Essay Example | Topics and Well Written Essays - 750 words - 2

Econometrics - Essay Example d) Assume that you run a regression with 223 observations. The dependent variable is ‘annual salary’ and there are 3 independent variables ‘work experience in years’, ‘education duration in years’ and ‘number of employees in company’. The regression yields following result for the variable ‘number of employees in company’: e) A researcher wants to find out whether age has an effect on how happy people are. The researcher runs a regression with the dependent variable ‘happiness score’ (0 to 10 with 10 being extremely satisfied) and the independent variable ‘age’ (in years). The modelling results show that age is not significant. You also have a look at the residual plot (shown below). Please explain why the residual plot indicates that the regression generated by the researcher is misleading. Discuss what relationship you expect between age and happiness. Outline how you could work this into the initial regression model and hence, improve it (10 marks). From the analysis of the residual below it can be observed that the residua are symmetrical. The residual also have constant variance. This means that the assumption of constant variance is fulfilled. We therefore expect a significant relationship between the age and happiness. To improve the initial regression model, we would ensure that other variables that influence the happiness are introduced into the regression model. f) You want to know whether people with higher incomes are happier. Your friend has run a survey in their company and run a regression on the data. The dependent variable is ‘happiness score’ (0 to 10 with 10 being extremely satisfied). There is only one independent variable: ‘monthly income’ (in  £). Your friend sends you the gretl output of the regression via email. Unfortunately, the file got corrupted and only the critical F-value is legible (see below). Using this output, show that ‘monthly income’ is indeed highly significant (provide

No comments:

Post a Comment

Note: Only a member of this blog may post a comment.