1. Write a detailed analysis of the following regression result. Assume that the sample size is large enough and adjusted R-square is sufficient. [Note: this is a made-up result just for the purpose of this exercise and does not resemblance any reality or output of any real study] Income, the dependent variable, is measured in $/hour. Education is measured in number of successful years in school. Experience is measured in number of years in any full time job. GPA is the final GPA of your last year in school (measures one's intellect). Computer-skill is one's score in a standardized test that measures his/her computer skill (out of 100) Age is one's age in years. Urban = 1 if the person is from a city, Urban = 0 if that person is from rural area. Variable Regression coefficient p-value Intercept 5.50 0.0065 Education 1.10 0.0132 Experience 0.55 0.0326 GPA 0.056 0.3281 Computer-skill 0.011 0.0451 Age -0.052 0.5619 Urban 0.072 0.0346 2. Is the intercept statistically significant? Which of the variables is/are statistically significant? 3. Find out the expected hourly wage of an individual from a city who is a high school graduate with 3.2 GPA, aged 43, worked 16 years full-time, and scored 75 in the computer test. 4. Find out the expected hourly wage of an individual from a rural area who is a college graduate with 2.3 GPA, aged 32, worked 14 years full-time, and scored 95 in the computer test. 5. Say the average score in the standardized computer test is 66 and the standard deviation (SD) is 17. If a person improves her computer skill in such a way that her score improves from (Mean - SD) to (Mean + SD), how much additional income she is expected to make according to the above model in a year. She works 48 weeks a year and 40 hours a week. [hint: ignore all other independent variables]