Dataset is at the bottom, please use Stata

. The dataset contains data on 60 students. For each student, the

dataset contains information on:

gpa = college G.P.A.

sat = S.A.T. score

study = average amount of time the student spends studying each week

gender = students gender. This variable equals 0 if the student is a female and 1 if the student is a male.

hsgpa = high school G.P.A.

Part 1: Examine the impact a students SAT scores has

on his/her college GPA:

What is your independent variable? What is your dependent variable?

Based on your intuition, what do you expect the relationship between these two variables to be? In other words, do you expect it to be positive, negative, or zero? Explain.

Plot the two variables and print the graph. Does the graph support your intuition?

Run a regression. Please print your regression results (a separate page or a screen shot are both fine). What are the values of the coefficients? Does your slope coefficient (b) have the sign that you expected?

Is your slope statistically significant? How do you know?

What is the R^2 of your regression? Interpret its meaning.

Could this equation suffer from omitted variable bias? Explain.

Part 2 : In addition to the data on SAT scores and GPA, run a regression that includes data on time spent studying and high school GPA. Attach your regression results.

What are the values of the coefficients? Do they have the signs that you expected? Explain.

What is the R^2 of your regression? What is the adjusted R^2? Did including these additional variables improve your model?

Could this equation suffer from omitted variable bias? Explain.

Part 3: In addition to the variables included above, run another regression including data on the students gender. (To do this, you will need to create a dummy variable.) Again, attach your regression results.

According to your regressions, who has a higher GPA? Men or women?

Does this equation suffer from multicollinearity? Explain.

data below:

gpa sat study hsgpa gender female male 3.18 1270 44.52 3.4 Female 1 0 3.33 1220 19.98 4 Female 1 0 3.25 1160 45.5 3.8 Male 0 1 2.42 950 12.1 3.8 Female 1 0 2.63 1070 31.56 4 Male 0 1 2.91 1110 20.37 4 Female 1 0 2.83 1220 19.81 2.8 Male 0 1 2.51 1150 12.55 3.8 Male 0 1 3.82 1440 38.2 4 Male 0 1 2.54 850 20.32 2.6 Male 0 1 3.38 1280 30.42 3.8 Female 1 0 3.02 1020 24.16 3.8 Female 1 0 2.6 880 28.6 2.8 Female 1 0 3.81 1080 57.15 3.8 Female 1 0 3.02 1200 18.12 3.5 Female 1 0 3.48 1120 45.24 3.8 Female 1 0 2.15 970 10.75 3.2 Male 0 1 3.8 1210 57 4 Female 1 0 3.13 1030 37.56 3.8 Female 1 0 3.4 1020 34 4 Female 1 0 3.59 1400 32.31 4 Male 0 1 1.62 720 19.44 3.3 Male 0 1 3.61 1170 43.32 3.9 Male 0 1 2.54 1070 35.56 3.5 Male 0 1 2.51 860 17.57 3.5 Female 1 0 1 920 15 2 Male 0 1 3.72 1350 52.08 4 Female 1 0 .87 910 13.05 2.3 Male 0 1 2.79 980 30.69 3.5 Female 1 0 2 800 18 3.5 Female 1 0 3.69 1070 44.28 4 Male 0 1 3.48 1320 48.72 3.8 Male 0 1 2.49 900 24.9 3 Female 1 0 3.91 1200 50.83 4 Female 1 0 3.05 1260 15.25 2.8 Male 0 1 2.4 830 26.4 2.8 Male 0 1 2.71 1300 29.81 3.5 Female 1 0 1.58 1070 23.7 3.8 Female 1 0 1.38 810 12.42 2 Male 0 1 2.52 1240 27.72 3.8 Male 0 1 2.16 820 19.44 2.8 Male 0 1 2.57 1140 20.56 3 Male 0 1 2.64 1170 18.48 2.3 Female 1 0 2.47 870 17.29 3.5 Male 0 1 1 1010 5 3 Male 0 1 2.73 760 40.95 3 Male 0 1 2.42 810 33.88 2.7 Male 0 1 2.3 830 13.8 3 Female 1 0 3.33 1240 26.64 3.8 Male 0 1 2.37 910 33.18 3.1 Male 0 1 2.11 1230 27.43 3 Male 0 1 2.91 1100 26.19 3 Female 1 0 3.29 970 26.32 3 Female 1 0 2.67 1090 32.04 3.3 Female 1 0 2.78 1300 19.46 3.2 Female 1 0 3.13 1070 37.56 3.8 Male 0 1 2.91 1040 34.92 2.5 Female 1 0 4 1320 24 4 Female 1 0 2.9 1230 23.2 3.8 Male 0 1 2.92 890 32.12 3.5 Female 1 0