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The instructor has also asked me for more 'RAW' data but Iam unsure exactly where I would go about finding that and wants at least 10 cases per independant variable and needs a much larger number.

I have attached the regression data that I did via excel. What I am having trouble with is explaining the following:

Where should I look to gather raw data?

What are the major results?

What insights do the graphs provide?

What is the importance of the intercept?

What is the importance of of the 'F'?

My null hypotheses is that the higher cost average annual cost will result in a higher rate of graduation and salary. Would I reject the hypotheses?

School Name Brigham Young University- Idaho Clemson Univeristy Mississippi State Univeristy San Francsico State University Average Annual CostGradution Ratepalary After Attending 59% 81% 60% 52% 78% 5596 72% 92% 8,518.00 16,700.00 15,656.00 13,655.00 16,989.00 11,758.00 15,619.00 29,997.00 | 42,700.00 52,400.00 40,200.00 49,200.00 46,900.00 37,500.00 44,800.00 74,000.00 12,957.00 | 84% |$ 57,700.00 42,800.00 40,200.00 44,900.00 49,600.00 52,500.00 57,900.00 47,000.00 University of California- Santa Cruz$ University of North Calolina at Greensboro | $ $ University of Oregon University of Southern California Is University of Washington- Seattle Campus $ |$ Grand Valley State University 66% 16,494.00 13,270.00 | 19,095.00 21,727.00 14,682.00 20,166.00 14,164.00 Metropolitan State University of Denver|$ University of South Carolina- Columbia S $ 26% 73% б5% 75% 80% 56% Montclair State University North Carolina State University at Raleigh $ Rutgers University- New Brunswick $ University of Nevada- RenoSUMMARY OUTPUT Regression Statistics 0.721620544 0.52073621 0.447003319 3628.184216 Adjusted R Square Standard Error Observations ANOVA df Significance F Regression Residual 185936722.8 92968361 7.06246839 0.00838948 171128369.2 13163721 357065091.9 Coefficients 2843.908327 2626.545057 0.357222144 Standard ErrorStat 5184.580278-0.54853 0.592622583-14044.513 8369.140229 0.313837 0.75862406115453.8831 0.148497029 2.4055840.031751294 0.03641382 Upper 95% 8356.69638 20706.97325 0.67803047 Lower 95% Lower 95% | Upper 95% 14044.51303 8356.69638 15453.88314 20706.9733 0.036413819 0.67803047 Intercept Gradution Rate Salary After Attendin RESIDUAL OUTPUT PROBABILITY OUTPUT Observation Predicted Average Annual Cost Residuals dard Residuals Percentile 13959.13882 18002.03354 13092.34891 16097.22461 15958.51539 11996.52187 15050.75619 26006.95182 19974.10726 14178.71919 12199.3236 15112.74385 16581.56433 17880.16305 19940.48988 15416.39769 5441.1388251.61092 1302.0335390.38548 2563.651086 0.759003 2442.22461 0.72305 1030.484608 0.305089 238.5218715-0.07062 568.2438143 0.168236 3990.048185 1.181307 7017.1072562.07751 2315.280807 0.68547 1070.676405 0.316988 3982.256149 5145.435675 1.523375 3198.16305 0.94686 225.5101174 0.066765 1252.397694-0.37079 Average Annual Cost 8518 11758 12957 13270 13655 14164 14682 15619 15656 16494 16700 16989 19095 20166 21727 29997 3.125 15.625 21.875 28.125 4 6 7 8 9 40.625 46.875 53.125 65.625 71.875 78.125 90.625 96.875Salary After Attending Residual Plot Gradution Rate Line Fit Plot 6000 35000 30000 25000 20000 15000 10000 5000 4000 10000 20000 30000 40000 60000 60000 70000 80000 -2000 8000 0.2 0.6 0.8 Salary After Attending Gradution Rate Series1 Predicted Average Annual Cost Average Annual Cost Normal Probability Plot Salary After Attending Line Fit Plot 35000 30000 25000 20000 15000 10000 哎5000 E 20000 15000 4 5000 20 80 100 120 0 10000 20000 30000 40000 50000 60000 70000 80000 Salary After Attending Sample Percentile Series1 Predicted Average Annual Cost Average Annual Cost Gradution Rate Residual Plot 6000 0.2 0.4 0.6 08 2000 Gradution Rate

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