OPRE-6301-HW2 - Homework 2 10 Points OPRE 6301 If you've any questions please contac the TA Instructions This is a required HW 1 Save and rename this

# OPRE-6301-HW2 - Homework 2 10 Points OPRE 6301 If you've...

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Homework 2 ::: 10 Points OPRE 6301 If you've any questions, please contac the TA Instructions: This is a required HW. 2. Enter your name below and on every sheet. 4. Click on the tabs below to work on the 3 sets of problems. 5. Do all your work in the proper cells only. Copy-and-paste deal is not al 6. While working on the HW, save your file periodically to avoid losing you 7. Don't wait till the last minute. You may get into technical / internet relate This HW is to be worked out by each student independently. Collabo will result in a score of 0 and your case will be reported to th Failure to follow submission guidelines will cost 3 points. Name: ABHIJIT PATIL Scores: (Do not write below this line.) Problem Points Earned Set 1 7 Set 2 6 Set 3 4 Set 4 3 Total 20 0 1. Save and rename this file as Lastname-Firstname-HW2 (Sethi-Avant 3. When you are done, submit it through eLearning only .
llowed. ur work accidentally. ed problems. No EXCUSE! oration of any form he university authority. ti-HW2, for example).
Scatter Plot y X Predicted Residual Residual-squared 20 28 24.08145106 4.081451061 16.6582427627 33 38 32.37713895 -0.62286105 0.3879558927 28 28 24.08145106 -3.91854894 15.355025788 15 22 19.10403833 4.10403833 16.8431306134 19 18 15.78576318 -3.21423682 10.3313183614 27 31 26.57015743 -0.42984257 0.1847646381 Correlation: 0.858559359 Sum of residual squared: 59.7604380561 SUMMARY OUTPUT Regression Statistics Name: ABHIJIT PATIL A dataset has been given which has 2 variables - X and Y. Draw a scatter diagram and find the correlation coefficient in Cell B29. Change the numbers in X column (any which way you want) to get the correlation coefficient to be between .83 and .86. This will require some trial and error. You'll see the changes in the scatter plot and the correlation coefficient as you change X values. Now use Data Analysis to run the regression program. Check the boxes for the Residual and Residual Plot boxes. Send the output to Cell A46. Use the regression intercept and slope (that is, Regression equation) to predict the value of Y for each X in the table below (Column = Predicted; use the cell references directly as opposed to typing in the values). Compare this predicted value of Y to the actual value of Y. The difference is called Residual (error). Compare these residuals to the Residuals given by Excel's Regression function. They should match exactly. Now square the residuals and add them up in Cell E29. This sum should match to the SS (Sum Squared) value given by Excel.
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