Regression model building by using excel (transferring to minitab17)

Anonymous
timer Asked: Mar 28th, 2017
account_balance_wallet $9.99

Question Description

The question/requirements are as follows on the attached items. I am very confused with this, mostly with the wording, I would appreciate anything if someone could help me. Thank you. And please no plagiarism. This is for a statistics course, I am doing good in class but have no idea where to begin with this assignment/project, I am not even being lazy, I have been trying to figure it out for about two weeks now, so that is why I am posting this on studypool.

Unformatted Attachment Preview

INFO 561 Each Excel project file has two tabs – DATA and Variable INFO. The DATA worksheet contains a YELLOW column representing the numerical response variable for simple and multiple regression analysis. In addition, there is a BROWN column for the numerical predictor variable for simple (and then again for multiple) regression analysis and the BLUE column for the “dummy” categorical predictor variable in a multiple regression analysis. All other WHITE column numerical variables are potential predictor variables in the multiple regression analysis. Note: For those wishing to practice logistic regression modeling after Module 7, the YELLOWhighlighted categorical variable column is the response variable while the BROWNhighlighted numerical variable column is joined with all other WHITE column numerical variables along with the BLUE column for the “dummy” categorical variable as the potential set of predictors. Developing the Team Project Report To develop your team project report on regression modeling do the following: 1. Using the YELLOW-highlighted numerical variable column in your Excel worksheet as the response variable of interest, create an introductory “scenario” of just two to three sentences that describes the data file for your project and why you (the ?????? Corporation/Group) are performing a multiple regression analysis that will enable you to predict the outcomes of this numerical response variable based on the set of possible independent variables X1, X 2,..., Xk. One of these independent variables should be a twocategory “dummy” variable – the BLUE-highlighted categorical variable column in your Excel worksheet. Moreover, the BROWN-highlighted numerical variable column will be the predictor variable for a simple linear regression analysis. 2. Next, copy this Excel worksheet into a Minitab worksheet. 3. As you learned in Module 2, first develop a simple linear regression model using the numerical variable whose Excel worksheet column was highlighted in BROWN as the predictor of Y . A. Cut and paste into your report the scatter plot and the Minitab printout for this simple linear regression model. B. Write the sample regression equation. C. Interpret the meaning of the Y intercept and slope for your fitted model. D. Interpret the meaning of the coefficient of determination r2 . E. Interpret the meaning of the standard error of the estimate SYX . F. Obtain the residual plots and cut and paste them into the report. Briefly comment on the appropriateness of your fitted model. (1) If the assumptions are met and the fitted model is appropriate continue to Step 3G. (2) If any of the linearity, normality, or equality of variance assumptions are problematic state this but continue to Step 3G with caution. Note -- you do not need to check the assumption of independence in your project. (That assumption is automatically met because your project is not time-dependent). G. Comment on the statistical significance of your fitted model H. Select a value for your independent variable in its relevant range: (1) Predict Yˆ . (2) Determine the 95% confidence interval estimate of the average value of Y for all occasions when the independent variable has the particular value you selected. (3) Determine the 95% prediction interval estimate of Y for an individual occasion when the independent variable has the particular value you selected. 4. As you learned in Modules 3 and 4, you will be using the set of potentially meaningful numerical independent variables and the one selected “two-category” dummy variable in your study to develop a “best” multiple regression model for predicting your numerical dependent variable Y . Follow the “9-step modeling process” described in the PowerPoints at the end of Module 4. A. Start with a visual assessment of the possible relationships of your numerical dependent variable Y with each potential predictor variable by developing the scatterplot matrix and paste this into your report. B. Then fit a preliminary multiple regression model using these potential numerical predictor variables and, at most, one categorical dummy variable. C. Then assess collinearity until you are satisfied that you have a final set of possible predictors that are “independent,” i.e., not unduly correlated with each other. D. Use both stepwise regression approaches and best subsets regression approaches to fit a multiple regression model with this set of potentially meaningful numerical independent variables (and, if appropriate, the one selected categorical dummy variable). (1) Based on the stepwise modeling criterion determine which independent variables should be included in your regression model. (2) Based on the forward selection modeling criterion determine which independent variables should be included in your regression model. (3) Based on the backward elimination modeling criterion determine which independent variables should be included in your regression model. (4) Based on the adjusted r2 criterion determine which independent variables should be included in your regression model. (5) Based on Minitab’s “predicted” r2 criterion determine which independent variables should be included in your regression model. (6) Based on the smallest SYX criterion determine which independent variables should be included in your regression model. (7) Based on Mallows’ Cp criterion determine which independent variables should be included in your regression model. E. Comment on the consistency of your findings in Step 4D (1)-(7). F. Cut and paste the Minitab printouts from Step 4D into your report. G. Based on Step 4D (along with the principle of parsimony if necessary) select a “best” multiple regression model. H. Using the predictor variables from your selected “best” multiple regression model, rerun the multiple regression model in order to assess its assumptions. I. Look at the set of residual plots, cut and pasted them into the report, and briefly comment on the appropriateness of your fitted model. (1) If the assumptions are met and the fitted model is appropriate continue to Step 4J. (2) If the normality assumption is problematic state this but continue to Step 4J with caution because your sample size is large enough for the central limit theorem to enable the use of classical inferential methods. Note: You do not need to check the assumption of independence in your project. That assumption is met because your project is not time-dependent. (3) If the either the linearity or equality of variance assumption is violated in each plot of Y with the individual predictors X1, X 2,..., Xk then transform the dependent variable Y (likely to logY ) and rerun the multiple regression model as in Step 4H. (4) If either the linearity or equality of variance assumption is violated in one or two scatter plots of Y with individual predictors then transform the particular independent variables involved following Tukey’s “ladder of powers” and rerun the multiple regression model as in Step 4H. J. Assess the significance of the overall fitted model. K. Assess the contribution of each predictor variable. L. If the dummy variable is not a significant predictor go to Step 5; however, if the dummy variable is a significant predictor, develop an interaction term for it in combination with every other significant predictor and then rerun the multiple regression model to determine whether any interaction term significantly belongs in the final model and comment on your findings. 5. Write the sample multiple regression equation for the “final best” model you have developed. A. Interpret the meaning of the Y intercept and interpret the meaning of all the slopes for your fitted model (but do this in whatever units you used for Y to build this model). B. Interpret the meaning of the coefficient of multiple determination r2 . C. Very briefly comment on how much r2 has changed from the simple regression model in Step 3D to the “final” multiple regression model in Step 5B. D. Interpret the meaning of the standard error of the estimate SYX (in the units you used to build this model). E. Select one value for each of your independent variables in their respective relevant ranges: ˆ (1) Predict Y . (If you used log Y take the antilog of the predicted value so you are back in units of Y). (2) Determine the 95% confidence interval estimate of the average value of Y for all occasions when the independent variables have the particular values you selected. (If your lower and upper boundaries are in units of log Y convert back to Y by taking the antilogs). (3) Determine the 95% prediction interval estimate of Y for an individual occasion when the independent variables have the particular values you selected. (If your lower and upper boundaries are in units of log Y convert back to Y by taking the antilogs). F. For your “final best” model, as per Module 1, prepare a brief descriptive analysis highlighting the key measures of central tendency, variation, and shape for your dependent variable Y and for each of the predictor variables. Show the individual histograms and boxplots for these variables. If a dummy variable was included as a predictor in your “final best” model show its summary table and bar chart. Specific instructions for the written team project report follows. Writing the Report Each team report has a title page followed by an Introduction section describing the study “scenario” and mentioning the possible predictor variables and the dependent variable. A section on the Simple Linear Regression Model is then followed by a section on the “final best” Multiple Regression Model. The final section of the report is a Discussion section assessing the gains (if any) by using the “best” multiple regression model in lieu of the simple linear regression model. In addition, a short descriptive analysis of the response variable and the predictor variables included in the “best” multiple regression model should be provided in the Discussion section with tables and charts relegated to the Appendix of the report. Note: All discussed Minitab printouts pertaining to regression modeling should be “cut and pasted” into the report. These should be placed either in the body of the report or in an Appendix to the report. If the latter approach is taken, be sure to number and reference these printouts when discussing them in the body of the report. 5-Year Return Expense Ratio 1-Year Return 3-Year Return 15,9 1,31 18,5 16,0 15,8 1,33 18,6 16,1 10,5 1,87 0,6 3,5 8,1 1,51 4,6 10,7 7,3 1,28 8,5 11,9 6,1 1,18 1,4 9,7 7,8 1,22 8,4 9,5 7,3 1,21 6,9 9,3 8,6 1,47 13,0 11,2 6,1 0,75 9,4 8,8 6,3 0,80 13,1 10,4 8,1 0,15 14,7 12,1 8,1 1,58 -0,9 7,5 6,4 1,50 11,6 10,3 8,0 0,63 10,9 12,4 8,0 1,30 10,3 10,7 8,3 1,18 10,2 14,1 8,0 1,22 7,1 10,2 7,9 0,92 9,1 13,2 7,7 0,86 12,3 15,0 8,9 1,58 15,5 12,6 10,0 1,24 8,6 7,8 7,4 1,26 6,6 9,2 6,2 1,13 12,3 11,0 7,6 1,37 10,8 11,4 6,7 2,42 6,9 8,4 6,2 0,72 14,0 10,2 7,3 1,36 8,6 12,0 7,2 1,09 7,5 12,8 6,3 0,59 9,4 11,3 6,5 0,41 11,2 10,2 8,4 0,46 12,3 13,0 6,6 1,42 4,4 10,3 4,3 0,93 8,0 10,1 6,7 1,62 5,8 6,2 5,4 1,33 6,5 9,4 5,0 0,15 15,4 6,6 5,0 1,04 12,4 8,0 5,1 1,25 11,7 9,3 5,4 1,35 7,2 6,3 4,5 1,15 11,2 9,3 5,0 0,92 16,3 8,5 4,7 1,12 13,2 8,9 Assets ObjectiveCODE 1706,8 1 1169,7 1 579,7 1 904,8 1 675,9 1 607,5 1 2303,4 1 2403,8 1 223,3 1 8957,1 1 909,7 1 64,4 1 85,5 1 52,2 1 8411,5 1 44,3 1 1740,4 1 282,3 1 454,4 1 9870,7 1 56,0 1 4835,5 1 2692,0 1 424,8 1 198,4 1 221,8 1 15422,9 1 497,9 1 547,3 1 103,7 1 5527,1 1 22592,9 1 240,8 1 2403,4 1 196,8 1 233,3 1 71,2 1 68,0 1 226,7 1 817,4 1 506,9 1 4360,3 1 221,6 1 Objective Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth 7,1 5,5 4,6 4,6 5,3 5,6 4,1 4,5 4,0 5,3 5,7 5,8 3,8 4,3 5,8 5,6 3,8 6,5 5,2 4,1 4,5 5,4 4,7 5,3 8,5 5,1 4,9 4,2 5,3 1,7 3,2 3,7 1,5 3,9 2,5 3,0 2,7 1,1 1,7 1,5 1,7 2,0 3,1 1,0 1,19 0,56 1,27 1,34 0,87 0,94 0,94 0,73 0,45 1,41 0,74 0,87 0,92 0,85 1,25 1,18 1,00 0,89 1,00 1,04 0,85 1,17 0,25 1,19 1,32 1,47 1,00 0,90 1,88 1,38 1,62 1,47 2,12 1,53 1,92 0,72 1,00 0,99 1,32 1,10 0,75 1,28 1,59 1,91 14,2 13,7 12,4 12,4 12,4 9,6 5,7 13,0 13,2 3,3 8,1 7,8 8,7 14,0 14,6 9,2 9,7 10,3 3,2 9,2 9,7 -3,9 13,1 2,7 6,7 7,7 6,5 12,6 6,6 5,7 6,6 8,1 4,2 -2,0 14,0 7,3 7,9 -3,3 -1,6 3,3 8,4 4,1 -5,5 3,0 12,3 9,6 8,2 9,6 10,2 11,7 10,8 8,9 9,6 7,8 10,8 10,7 8,7 6,0 8,2 9,7 7,9 9,7 9,4 9,3 8,3 9,4 8,4 7,7 9,5 9,7 9,2 10,1 9,8 6,0 8,1 6,0 6,5 7,9 9,1 8,2 7,5 3,0 3,4 3,2 6,1 7,4 3,2 5,5 434,9 7834,2 505,3 152,1 479,3 30110,2 173,0 815,4 85,7 166,1 47,2 6955,2 3928,9 1790,7 135,4 142,0 601,8 2425,8 70,3 52,6 8752,9 793,8 460,3 1665,0 71,0 1027,1 46,2 209,0 105,4 52,3 178,1 989,8 224,4 975,8 421,5 353,5 3960,2 13308,4 730,5 434,5 1260,8 95,5 41,5 861,8 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth 3,6 2,8 3,5 3,8 4,0 2,3 3,6 3,4 1,2 1,1 3,4 2,5 3,4 4,1 1,2 2,4 1,3 3,1 1,5 4,2 4,1 1,7 2,5 3,0 2,4 3,0 2,2 3,6 2,9 4,8 1,4 2,5 2,8 3,1 2,8 2,7 2,4 3,3 1,2 2,8 2,7 4,6 3,1 2,5 1,35 1,09 1,04 0,75 1,03 0,86 1,13 0,77 0,85 0,76 1,30 1,22 1,37 1,38 1,04 0,85 1,11 1,14 1,10 0,94 1,20 0,61 0,96 1,46 0,95 0,99 1,39 1,00 0,88 1,19 0,97 0,20 0,25 0,18 0,87 1,16 1,04 0,66 2,35 1,28 1,17 1,10 1,35 0,93 3,7 13,4 10,5 11,4 15,4 10,3 9,7 4,6 2,6 1,6 5,9 3,5 13,1 16,4 9,0 8,3 7,2 7,4 7,9 9,1 4,9 5,5 4,4 13,8 8,4 6,3 8,2 7,8 2,3 11,4 2,9 8,9 9,2 10,8 10,6 10,1 1,5 11,4 7,8 1,4 4,3 -3,5 5,2 4,7 6,6 7,1 6,7 7,3 9,7 7,7 6,7 7,9 6,5 5,1 8,3 5,7 8,2 7,7 6,1 8,0 6,6 6,2 11,5 8,8 6,8 5,3 6,8 7,9 5,3 7,0 8,4 7,8 8,5 9,7 4,3 6,7 6,9 6,8 6,4 6,1 8,0 6,7 6,3 4,0 3,2 7,2 5,1 8,9 115,9 61,0 51,0 52,4 198,5 1219,3 181,5 2046,5 591,2 67,0 164,7 798,6 322,9 94,2 334,0 896,9 261,4 356,4 678,2 142,0 2289,2 20574,1 785,4 454,4 901,4 184,8 1370,5 458,9 7992,9 395,5 128,8 7221,2 266,9 4295,1 11495,8 166,3 1525,7 684,3 52,1 1258,7 2460,5 224,1 59,7 265,4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth 2,5 2,4 2,9 2,0 2,4 2,7 3,5 2,2 1,4 2,1 2,1 2,7 2,6 3,5 3,3 3,8 3,6 4,0 1,9 1,2 3,9 2,7 2,1 3,5 3,5 2,9 3,7 2,6 3,1 1,1 3,7 2,4 3,0 2,0 0,9 1,7 2,0 2,4 2,8 3,0 1,5 2,9 3,4 3,1 1,40 1,27 1,21 1,34 1,27 1,01 0,65 0,20 1,00 1,31 1,41 1,07 1,13 1,54 1,18 1,23 1,14 0,91 1,18 1,20 1,34 1,75 0,80 1,00 1,18 1,52 0,91 1,03 0,90 0,45 1,35 0,95 0,99 1,45 1,00 0,80 1,13 1,16 1,24 0,96 2,45 0,88 0,22 0,94 6,5 9,0 14,4 9,4 7,5 0,0 4,1 7,1 8,0 5,3 7,7 7,5 10,3 10,8 2,6 6,6 10,9 16,9 10,1 6,3 12,0 8,4 8,4 9,5 14,1 -0,3 7,2 6,8 -5,1 5,4 -6,5 9,5 5,3 -1,0 -0,5 6,4 4,0 11,3 5,8 3,6 4,6 6,1 9,0 10,2 9,9 7,6 9,3 8,7 9,7 7,3 9,1 5,9 5,4 6,1 6,8 6,2 8,2 8,2 5,7 5,5 9,3 8,7 8,1 5,7 7,7 5,9 5,5 10,6 8,0 2,5 7,5 4,3 3,6 5,6 6,8 7,9 8,5 6,9 7,1 5,7 8,8 10,1 9,1 8,6 6,0 6,5 7,1 6,1 202,8 232,3 702,9 523,4 2152,4 1364,7 1016,0 18780,9 196,4 95,3 123,1 5444,1 158,4 547,9 94,4 387,9 2423,8 801,8 134,7 717,9 380,7 124,2 81,3 107,2 99,4 718,3 822,5 63,3 2715,0 526,0 380,3 1073,9 1172,1 233,3 909,3 131,3 199,9 299,5 193,1 1412,9 105,2 774,6 6741,7 2779,7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth 4,6 1,6 3,7 0,3 0,3 0,5 0,9 0,6 -0,1 -0,2 1,0 -0,7 1,3 0,8 0,7 1,4 -1,0 0,7 -0,8 0,0 -0,1 1,5 1,0 -0,2 1,4 0,5 -0,9 0,6 1,5 0,7 -1,1 0,1 -1,1 -0,4 -6,1 -17,6 1,6 1,3 1,8 -5,9 -16,6 -5,4 0,5 -0,9 1,25 1,19 1,38 0,97 1,54 1,00 1,59 1,52 1,17 1,45 1,29 1,33 1,80 1,44 1,44 1,24 2,00 1,05 1,49 1,27 1,22 0,96 1,20 0,77 0,95 1,27 2,27 1,46 1,42 1,16 1,14 1,37 1,62 1,40 1,38 1,39 1,26 1,04 1,25 1,65 1,66 1,39 1,38 2,90 4,7 3,9 5,0 7,8 -9,0 -1,8 7,5 -2,0 2,7 1,8 6,3 4,7 8,9 7,8 4,0 5,9 -2,4 14,1 4,6 3,3 5,0 7,5 7,3 7,9 5,9 6,2 8,9 7,5 4,5 4,6 6,1 1,1 2,6 8,9 5,3 -8,0 9,6 5,2 9,6 5,3 -8,0 -1,1 9,3 8,6 9,6 6,8 6,7 5,6 7,0 2,1 5,4 4,1 5,1 3,9 4,7 3,1 6,1 5,0 6,7 5,8 0,3 4,0 5,1 4,8 4,7 7,0 5,9 5,5 5,8 4,6 3,7 6,2 8,7 6,1 3,9 3,1 2,3 6,1 5,6 -11,7 6,0 5,2 6,0 5,4 -11,9 -4,0 6,9 4,3 40,5 410,8 382,9 428,6 1154,5 2590,2 729,1 256,7 185,6 95,5 3012,5 112,7 45,3 134,3 85,8 208,1 90,5 152,9 121,0 701,7 67,4 3950,2 65,9 48,2 348,4 423,9 40,0 133,2 121,4 1020,7 138,6 482,9 171,1 386,3 255,3 225,7 367,2 5694,8 43,8 349,9 343,3 105,4 374,2 118,6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth 0,7 0,1 -0,1 0,8 1,2 1,0 0,2 -0,4 0,3 1,9 -3,4 0,8 13,9 9,4 10,3 10,5 12,9 12,2 12,0 9,4 9,7 9,9 11,3 13,2 9,4 10,2 11,1 12,8 11,3 9,9 10,8 10,6 10,0 14,0 11,5 10,7 10,9 13,1 9,6 11,1 11,2 10,5 11,8 10,9 0,21 1,55 1,60 1,05 1,64 1,10 1,09 0,58 0,95 1,88 1,14 1,41 1,53 1,20 0,97 0,83 1,10 1,49 0,86 1,10 0,84 0,99 1,07 1,23 0,80 1,18 1,27 0,52 1,05 0,84 1,00 0,60 0,48 1,22 0,50 0,20 0,25 1,32 1,07 1,10 0,85 0,80 0,84 0,76 8,6 5,9 4,8 6,9 0,1 6,5 2,6 1,8 2,2 10,8 -0,1 4,2 12,3 18,8 21,0 21,7 22,1 21,2 18,7 19,7 19,8 18,5 19,0 15,8 15,7 16,6 15,1 18,5 14,9 17,1 16,7 19,2 14,2 13,4 20,0 22,0 22,0 16,5 20,4 19,9 24,7 20,6 21,0 18,4 5,5 4,1 5,5 6,0 5,8 5,8 5,8 6,6 3,7 6,4 -2,8 6,1 16,1 14,1 14,7 13,5 15,7 18,0 15,7 12,4 12,6 12,5 15,4 16,8 11,2 12,8 17,4 15,6 14,7 15,3 14,4 14,9 14,0 13,6 15,8 14,9 14,9 13,2 14,4 13,3 17,4 15,8 14,8 14,2 206,4 49,3 65,6 123,4 45,8 1091,2 2785,9 4644,9 1448,4 47,6 527,6 138,2 90,0 149,0 620,8 388,3 1964,1 111,4 2829,9 475,0 1417,7 2543,7 359,7 1690,5 362,2 1342,7 331,6 84842,6 7365,6 1269,2 262,0 31686,7 48,8 3033,8 394,0 8581,2 663,9 5768,7 143,9 85,3 93,9 1470,0 3856,7 11261,9 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Value Value Value Value Value Value Value Value Value Value Value Value Value Value Value Value Value Value Value Value Value Value Value Value Value Value Value Value Value Value Value Value 10,2 13,9 13,2 9,8 9,2 9,9 10,8 9,3 9,8 11,2 10,6 12,1 9,5 9,3 10,5 9,5 10,1 10,3 9,2 11,5 7,3 7,7 7,9 7,7 8,9 9,2 8,7 7,7 8,1 7,3 8,1 6,5 7,3 9,0 7,7 8,2 7,1 8,7 9,4 7,6 9,3 8,8 9,8 8,9 0,82 1,15 1,10 1,07 0,96 0,98 1,63 0,71 0,90 1,16 1,55 1,19 0,86 1,15 1,38 0,21 0,35 0,89 1,49 0,90 0,85 1,11 0,58 1,30 1,35 1,48 1,11 1,14 1,59 0,74 1,47 1,11 0,80 0,96 1,18 0,76 1,09 1,16 0,87 1,15 1,01 0,81 1,20 1,23 13,2 16,6 19,7 18,1 19,4 16,6 19,3 19,1 19,8 18,2 18,4 14,9 19,0 16,6 15,1 22,1 18,2 18,7 18,5 17,7 16,4 20,6 16,2 20,8 11,8 15,6 19,7 21,2 21,4 15,6 14,6 15,3 20,1 17,7 20,4 20,9 19,7 19,1 15,1 23,4 23,6 20,0 19,8 20,0 16,8 14,7 17,0 13,7 14,4 12,8 12,9 12,6 13,7 12,6 15,4 14,9 14,2 14,4 13,0 14,7 ...
Purchase answer to see full attachment

Tutor Answer

Ace_Tutor
School: Cornell University

attached is my work

Comparison between Simple Linear Regression and “final best” Multiple Linear Regression

Introduction:
The objectives of this report are to apply both simple linear regression and multiple linear
regression model to predict the response variable ‘5-Year Return’ and then to compare both
methods using different approaches. In the simple linear model, the predictor variable is ‘3-Year
Return’ and in the multiple linear model, the predictor variables are Expense Ratio, 1-Year
Return, 3-Year Return, Assets, and ObjectiveCODE. The residual plots for both models are
generated, and comments and conclusions were made based on the findings.
Simple Linear Regression:
The simple linear regression model demonstrates the relationship between 3-year-return and 5year-return can be described as:
Y X  

where X = 3-Year Return and Y = 5-Year Return.
A. The scatter plot and the Minitab printout for this simple linear regression model are shown
below:
Regression Analysis: 5-Year Return versus 3-Year Return
The regression equation is
5-Year Return = - 3.258 + 0.9987 3-Year Return
S = 2.65307

R-Sq = 70.1%

R-Sq(adj) = 70.1%

Analysis of Variance
Source
Regression
Error
Total

DF
1
866
867

SS
14294.9
6095.6
20390.5

MS
14294.9
7.0

F
2030.89

P
0.000

Fitted Line Plot

5-Year Return = - 3.258 + 0.9987 3-Year Return
30

S
R-Sq
R-Sq(adj)

2.65307
70.1%
70.1%

5-Year Return

20

10

0

-10

-20
-10

0

10

20

30

3-Year Return

B. The sample regression equation is

5  Year Return   3.258  0.9987 * 3  Year Return
C. For our fitted model, Y intercept = -3.258 means that the 5-Year Return is equal to -3.258%
when the 3-Year Return is 0. In addition, the slope of 0.9987 means that for each increment of 3Year Return by 1%, the 5-Year Return will increase by the amount of 0.9987%.
D. The coefficient of determination r2 = 70.1% implies that there is 70.1% of the variance in 5Year Return that is predictable from 3-Year Return.
E. The standard error of the estimate SYX = 2.65307 means that the variability of predictions in
the regression model is 2.65307.
F. The residual plots of the given model can be generated as follow:

According to the above residual plots, it appears that the data is pretty symmetrically distributed,
as it tends to cluster around the middle of the plot. The plots are evenly distributed vertically;
however, there may be a few outliers at both ends of the plot.
G. The assumptions are met and the fitted model is appropriate and statistically significant. A
value for our independent variable 3-Year Return can be selected as x  23.6. Therefore,
(1) Y can be predicted as

Y  3.258  0.9987 x
 3.258  0.9987  23.6 
 20.3
(2) The 95% confidence interval estimate of the average value of Y is calculated by

95%CI     t1 /2,nk 1  SE ,   t1 /2,n k 1  SE 
  0.9987  1.963  2.65307, 0.9987  1.963  2.65307 
  4.2085, 6.2059 
(3) The 95% prediction interval estimate of the average value of Y is calculated by



95% PI  y  t1 /2,n 2 SE , y  t1 /2, n 2 SE



  20.3  1.963  2.65307, 20.3  1.963  2.65307 
 15.092, 25.508 

“Final best” Multiple Linear Regression:
A. The scatterplot matrix that demonstrates the possible relationships of the numerical dependent
variable Y with each potential predictor variable can be developed as follow:

Matrix Plot of 5-Year Retur, Expense Rati, 1-Year Retur, ...
0.0

1.5

0

3.0

15

0.0

30

0.5

1.0
20

5-Year Return

0
-20

3.0
1.5

Expense Ratio

0.0

40
20

1-Year Return

0

30
15

3-Year...

flag Report DMCA
Review

Anonymous
Tutor went the extra mile to help me with this essay. Citations were a bit shaky but I appreciated how well he handled APA styles and how ok he was to change them even though I didnt specify. Got a B+ which is believable and acceptable.

Similar Questions
Related Tags

Brown University





1271 Tutors

California Institute of Technology




2131 Tutors

Carnegie Mellon University




982 Tutors

Columbia University





1256 Tutors

Dartmouth University





2113 Tutors

Emory University





2279 Tutors

Harvard University





599 Tutors

Massachusetts Institute of Technology



2319 Tutors

New York University





1645 Tutors

Notre Dam University





1911 Tutors

Oklahoma University





2122 Tutors

Pennsylvania State University





932 Tutors

Princeton University





1211 Tutors

Stanford University





983 Tutors

University of California





1282 Tutors

Oxford University





123 Tutors

Yale University





2325 Tutors