attachments_ed5c6265f1f432952f6d2f2f403b2af711ca5cdd admit gre gpa rank 0 380 3.61 3 1 660 3.67 3 1 800 4 1 1 640 3.19 4 0 520 2.93 4 1 760 3 2 1 560 2.98 1 0 400 3.08 2 1 540 3.39 3 0 700 3.92 2 0 800 4 4 0 440 3.22 1 1 760 4 1 0 700 3.08 2 1 700 4 1 0 480 3.44 3 0 780 3.87 4 0 360 2.56 3 0 800 3.75 2 1 540 3.81 1 0 500 3.17 3 1 660 3.63 2 0 600 2.82 4 0 680 3.19 4 1 760 3.35 2 1 800 3.66 1 1 620 3.61 1 1 520 3.74 4 1 780 3.22 2 0 520 3.29 1 0 540 3.78 4 0 760 3.35 3 0 600 3.4 3 1 800 4 3 0 360 3.14 1 0 400 3.05 2 0 580 3.25 1 0 520 2.9 3 1 500 3.13 2 1 520 2.68 3 0 560 2.42 2 1 580 3.32 2 1 600 3.15 2 0 500 3.31 3 0 700 2.94 2 1 460 3.45 3 1 580 3.46 2 0 500 2.97 4 0 440 2.48 4 0 400 3.35 3 0 640 3.86 3 0 440 3.13 4 Page 1 qattachments_ed5c6265f1f432952f6d2f2f403b2af711ca5cdd 0 1 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 740 680 660 740 560 380 400 600 620 560 640 680 580 600 740 620 580 800 640 300 480 580 720 720 560 800 540 620 700 620 500 380 500 520 600 600 700 660 700 720 800 580 660 660 640 480 700 400 340 580 380 540 660 3.37 3.27 3.34 4 3.19 2.94 3.65 2.82 3.18 3.32 3.67 3.85 4 3.59 3.62 3.3 3.69 3.73 4 2.92 3.39 4 3.45 4 3.36 4 3.12 4 2.9 3.07 2.71 2.91 3.6 2.98 3.32 3.48 3.28 4 3.83 3.64 3.9 2.93 3.44 3.33 3.52 3.57 2.88 3.31 3.15 3.57 3.33 3.94 3.95 4 2 3 3 3 3 2 4 2 4 3 3 3 2 4 1 1 1 3 4 4 2 4 3 3 3 1 1 4 2 2 4 3 2 2 2 1 2 2 1 2 2 2 2 4 2 2 3 3 3 4 3 2 Page 2 qattachments_ed5c6265f1f432952f6d2f2f403b2af711ca5cdd 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 1 0 1 0 0 1 0 1 740 700 480 400 480 680 420 360 600 720 620 440 700 800 340 520 480 520 500 720 540 600 740 540 460 620 640 580 500 560 500 560 700 620 600 640 700 620 580 580 380 480 560 480 740 800 400 640 580 620 580 560 480 2.97 3.56 3.13 2.93 3.45 3.08 3.41 3 3.22 3.84 3.99 3.45 3.72 3.7 2.92 3.74 2.67 2.85 2.98 3.88 3.38 3.54 3.74 3.19 3.15 3.17 2.79 3.4 3.08 2.95 3.57 3.33 4 3.4 3.58 3.93 3.52 3.94 3.4 3.4 3.43 3.4 2.71 2.91 3.31 3.74 3.38 3.94 3.46 3.69 2.86 2.52 3.58 2 1 2 3 2 4 4 3 1 3 3 2 2 1 3 2 2 3 3 3 4 1 4 2 4 2 2 2 3 2 3 4 3 2 1 2 4 4 3 4 3 2 3 1 1 1 2 2 3 3 4 2 1 Page 3 qattachments_ed5c6265f1f432952f6d2f2f403b2af711ca5cdd 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 1 0 1 1 0 1 0 0 0 660 700 600 640 700 520 580 700 440 720 500 600 400 540 680 800 500 620 520 620 620 300 620 500 700 540 500 800 560 580 560 500 640 800 640 380 600 560 660 400 600 580 800 580 700 420 600 780 740 640 540 580 740 3.49 3.82 3.13 3.5 3.56 2.73 3.3 4 3.24 3.77 4 3.62 3.51 2.81 3.48 3.43 3.53 3.37 2.62 3.23 3.33 3.01 3.78 3.88 4 3.84 2.79 3.6 3.61 2.88 3.07 3.35 2.94 3.54 3.76 3.59 3.47 3.59 3.07 3.23 3.63 3.77 3.31 3.2 4 3.92 3.89 3.8 3.54 3.63 3.16 3.5 3.34 2 3 2 2 2 2 2 1 4 3 3 3 3 3 3 2 4 2 2 3 3 3 3 4 2 2 4 2 3 2 2 2 2 3 3 4 2 2 3 4 3 4 3 2 1 4 1 3 1 1 3 2 4 Page 4 qattachments_ed5c6265f1f432952f6d2f2f403b2af711ca5cdd 0 0 0 1 1 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 1 1 0 0 0 0 1 1 580 460 640 600 660 340 460 460 560 540 680 480 800 800 720 620 540 480 720 580 600 380 420 800 620 660 480 500 700 440 520 680 620 540 800 680 440 680 640 660 620 520 540 740 640 520 620 520 640 680 440 520 620 3.02 2.87 3.38 3.56 2.91 2.9 3.64 2.98 3.59 3.28 3.99 3.02 3.47 2.9 3.5 3.58 3.02 3.43 3.42 3.29 3.28 3.38 2.67 3.53 3.05 3.49 4 2.86 3.45 2.76 3.81 2.96 3.22 3.04 3.91 3.34 3.17 3.64 3.73 3.31 3.21 4 3.55 3.52 3.35 3.3 3.95 3.51 3.81 3.11 3.15 3.19 3.95 2 2 3 2 3 1 1 1 2 3 3 1 3 2 3 2 4 2 2 4 3 2 3 1 2 2 2 4 3 2 1 3 2 1 3 2 2 3 3 4 4 2 4 4 3 2 3 2 2 2 2 3 3 Page 5 qattachments_ed5c6265f1f432952f6d2f2f403b2af711ca5cdd 1 0 0 1 1 0 1 0 1 0 0 1 0 1 1 1 0 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0 0 0 1 0 1 1 520 380 560 600 680 500 640 540 680 660 520 600 460 580 680 660 660 360 660 520 440 600 800 660 800 420 620 800 680 800 480 520 560 460 540 720 640 660 400 680 220 580 540 580 540 440 560 660 660 520 540 300 340 3.9 3.34 3.24 3.64 3.46 2.81 3.95 3.33 3.67 3.32 3.12 2.98 3.77 3.58 3 3.14 3.94 3.27 3.45 3.1 3.39 3.31 3.22 3.7 3.15 2.26 3.45 2.78 3.7 3.97 2.55 3.25 3.16 3.07 3.5 3.4 3.3 3.6 3.15 3.98 2.83 3.46 3.17 3.51 3.13 2.98 4 3.67 3.77 3.65 3.46 2.84 3 3 3 4 3 2 3 2 3 2 1 2 2 3 1 4 2 2 3 4 4 2 4 1 4 4 4 2 2 2 1 1 3 1 2 2 3 2 3 2 2 3 4 1 2 2 3 3 2 3 4 4 2 2 Page 6 qattachments_ed5c6265f1f432952f6d2f2f403b2af711ca5cdd 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 1 0 1 1 0 0 1 0 0 0 0 0 780 480 540 460 460 500 420 520 680 680 560 580 500 740 660 420 560 460 620 520 620 540 660 500 560 500 580 520 500 600 580 400 620 780 620 580 700 540 760 700 720 560 720 520 540 680 460 560 480 460 620 580 800 3.63 3.71 3.28 3.14 3.58 3.01 2.69 2.7 3.9 3.31 3.48 3.34 2.93 4 3.59 2.96 3.43 3.64 3.71 3.15 3.09 3.2 3.47 3.23 2.65 3.95 3.06 3.35 3.03 3.35 3.8 3.36 2.85 4 3.43 3.12 3.52 3.78 2.81 3.27 3.31 3.69 3.94 4 3.49 3.14 3.44 3.36 2.78 2.93 3.63 4 3.89 4 4 1 3 2 4 2 3 1 2 2 2 4 3 3 1 3 3 1 3 4 1 3 4 3 4 2 3 3 2 2 2 2 2 3 3 2 2 1 2 1 3 3 1 1 2 2 1 3 3 3 1 2 Page 7 qattachments_ed5c6265f1f432952f6d2f2f403b2af711ca5cdd 1 1 1 1 0 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 1 1 1 1 0 0 0 0 0 540 680 680 620 560 560 620 800 640 540 700 540 540 660 480 420 740 580 640 640 800 660 600 620 460 620 560 460 700 600 3.77 3.76 2.42 3.37 3.78 3.49 3.63 4 3.12 2.7 3.65 3.49 3.51 4 2.62 3.02 3.86 3.36 3.17 3.51 3.05 3.88 3.38 3.75 3.99 4 3.04 2.63 3.65 3.89 2 3 1 1 2 4 2 2 3 2 2 2 2 1 2 1 2 2 2 2 2 2 3 2 3 2 3 2 2 3 Page 8 mydata <- read.csv(file.choose()) ## view the first few rows of the data head(mydata) ## admit gre gpa rank ## 1 0 380 3.61 3 ## 2 1 660 3.67 3 ## 3 1 800 4.00 1 ## 4 1 640 3.19 4 ## 5 0 520 2.93 4 ## 6 1 760 3.00 2 This dataset has a binary response (outcome, dependent) variable called admit. A value of 1 indicates admission was granted. There are three predictor variables: gre, gpa, and rank. We will treat the variables gre and gpa as continuous. The variable rank takes on the values 1 through 4. Institutions with a rank of 1 have the highest prestige, while those with a rank of 4 have the lowest. Do a standard logistic regression analysis to see how the variables predict admission into graduate school. Don't just cut and paste tables from R to answer the questions. Explain all your findings! 1-Provide the summary statistics (including standard deviation) for all variables. 2- Perform a regression model using rank as is, versus creating dummy variables in R (hint: try mydata$rank <- factor(mydata$rank) when you want to create dummy variables, and just "rank" in your regression model). 3-What does the regression model say about how much the predictor variables influence the admission? 4-Which of the indpendent variables are the most predictive? 5-Interpret the coefficents in their logit form and then exponentiate the coefficients (exp(b)) and discuss the meaning of each coefficient. 6-What variables do and do not significantly predict admission? Please copy/paste screen images of your work in R, and put into a Word document for submission. Be sure to provide narrative of your answers (i.e., do not just copy/paste your answers without providing some explanation of what you did or your findings). mydata <- read.csv(file.choose()) ## view the first few rows of the data head(mydata) ## admit gre gpa rank ## 1 0 380 3.61 3 ## 2 1 660 3.67 3 ## 3 1 800 4.00 1 ## 4 1 640 3.19 4 ## 5 0 520 2.93 4 ## 6 1 760 3.00 2 This dataset has a binary response (outcome, dependent) variable called admit. A value of 1 indicates admission was granted. There are three predictor variables: gre, gpa, and rank. We will treat the variables gre and gpa as continuous. The variable rank takes on the values 1 through 4. Institutions with a rank of 1 have the highest prestige, while those with a rank of 4 have the lowest. Do a standard logistic regression analysis to see how the variables predict admission into graduate school. Don't just cut and paste tables from R to answer the questions. Explain all your findings! 1-Provide the summary statistics (including standard deviation) for all variables. 2- Perform a regression model using rank as is, versus creating dummy variables in R (hint: try mydata$rank <- factor(mydata$rank) when you want to create dummy variables, and just "rank" in your regression model). 3-What does the regression model say about how much the predictor variables influence the admission? 4-Which of the indpendent variables are the most predictive? 5-Interpret the coefficents in their logit form and then exponentiate the coefficients (exp(b)) and discuss the meaning of each coefficient. 6-What variables do and do not significantly predict admission? Please copy/paste screen images of your work in R, and put into a Word document for submission. Be sure to provide narrative of your answers (i.e., do not just copy/paste your answers without providing some explanation of what you did or your findings)