MIS 660 Topic 8 Multiple Regression Analysis GCU

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MIS 660 Topic 8 Multiple Regression Analysis GCU

The purpose of this assignment is to apply multiple regression concepts, interpret multiple regression analysis models, and justify business predictions based upon the analysis.

For this assignment, you will use the “Strength” dataset. You will use SPSS to analyze the dataset and address the questions presented. Findings should be presented in a Word document along with the SPSS outputs.

The compressive strength (Y) of concrete is influenced by the mixing proportions and by the time that it is allowed to cure, although the exact relationship between the strength and the components is unknown. The provided data includes the results of n = 1030 concrete strength experiments that include the following:

1. Strength (in MPa): The compressive strength of the concrete.
2. Age (in days): The number of days the concrete was allowed to cured.
3. Coarse_Aggregate (in kg/m³): The proportion of coarse aggregate in the mix.
4. Fine_Aggregate (in kg/m³): The proportion of fine aggregate in the mix.
5. Cement (in kg/m³): The proportion of cement in the mix.
6. Slag (in kg/m³): The proportion of furnace slag in the mix.
7. Superplasticizer (in kg/m³): The proportion of plasticizer in the mix.
8. Water (in kg/m³): The proportion of water in the mix.
9. Ash (in kg/m³): The proportion of fly ash in the mix.

Part 1:

Derive various transformations of compressive strength to determine which transformation, if any, results in a variable that most closely mimics a normal distribution. To do this, plot Q-Q plots after each transformation listed below, and decide which one should be used to build a multiple linear model. Explain your answer and provide the SPSS output as an illustration.

1. Strength (no transformation)
2. Square root of Strength
3. Squared Strength
4. (Natural) Log of Strength
5. Reciprocal of Strength

Part 2:

Based on the transformation selected in Part 1, build a multiple linear regression model with all eight predictors.

1. Use t-tests to determine if any of the predictors significantly affect the compressive strength of concrete. Explain why each variable should or should not be included in the model. Assume α = 0.05. Show the appropriate model results to explain your answer.
2. If any predictors from question 1 are found to be not significant, remove them and re-run the model to create a reduced model (RM). Are all the remaining variables still statistically significant? Show the appropriate model results to explain your answer.
3. Based on the RM, should there be concern about multicollinearity among the predictors selected? Show the appropriate model results to explain your answer.
4. After fitting the RM, derive the residual plot (standardized residuals vs. standardized predicted values) and normal probability plot. Interpret each plot.
5. What is the coefficient of determination, R², of the RM? How would you interpret the R²?
6. Based on the RM, what would be the new estimated compressive strength that is currently 50 MPa, after a 10-day increase in curing time? Assume all other predictors are held constant.
7. How would you interpret the intercept (constant) in the RM? Does the interpretation make sense given the data you used to build the RM?

Part 3:

Given the following components and aging time below, what is the estimated compressive strength based on the RM?

1. Age: 50 days
2. Coarse_Aggregate: 900 kg/m³
3. Fine_Aggregate: 600 kg/m³
4. Cement: 300 kg/m³
5. Slag: 200 kg/m³
6. Superplasticizer: 7 kg/m³
7. Water: 190 kg/m³
8. Ash: 70 kg/m³

Part 4:

What is a 95% confidence interval of the estimate in Part 3? How would you interpret the 95% confidence interval? (Hint: Use the SPSS scoring wizard to address this question.)

APA format is not required, but solid academic writing is expected.

This assignment uses a grading rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.

You are not required to submit this assignment to LopesWrite.

 

 

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MIS 660 Topic 8 Multiple Regression Analysis GCU

ACCT 553 DeVry, BIAM 500 DeVry, CIS 500 STR, CIS 558 STR, ENG 105 GCU, FIN 390 DeVry, FIN 504 GCU, HCA 545 GCU, HCA 699 GCU, HLT 306 GCU, HLT 362 GCU, HLT 555 GCU, HLT 610 GCU, HLT 665 GCU, HOSP 594 DeVry, HRM 600 DeVry, MAT 144 GCU, MGMT 600, MGT 599 STR, MGT 655 GCU, MKT 373 GCU, PSY 362 GCU, PSY 565 GCU, PSY 575 GCU, PSY 665 GCU, SOC 102 GCU, SOC 320 GCU, SOC 372 GCU, SOC 412 GCU, NSG 6440 SU, HIM 515 GCU, NSG 4029 SU, HIM 615 GCU, NSG 3029 SU, NSG 4055 SU, NSG 6630 SU, NSG 6005 SU, CRMJ 310, PSY 510 GCU, CRMJ 300 Devry, CRMJ 425, SPD 200 GCU, HLT 490 GCU ,ECH 340 GCU , ECH 440 GCU , ECH 355 GCU ,ECH 350 GCU ,LDR 461 GCU ,ECH 425 GCU ,REL 212 STR ,SCI 115 STR ,CIS 505 STR ,JUS 652 GCU, MGT 640 GCU, CIS 527 STR, SOC 436 GCU,ACC 502 GCU, FIN 504 GCU , MATH 260 DeVry ,ETHC 445 DeVry ,ECET 220 DeVry , CARD 405 DeVry, NETW 203 DeVry, NETW 205 DeVry, ECET 365 DeVry ,MATH 270 DeVry ,PHYS 310 DeVry, BIB 106 GCU ,CIS 512 STR ,SYM 506 GCU ,ECN 601 GCU  ,BIAM 570 DeVry, PSY 402 GCU , SOC 480 GCU , BIAM 530 DeVry,  DNP 810 GCU , DNP 820 GCU , DNP 825 GCU , ECN 450 GCU , DNP 830 GCU , LDR 804 GCU , MAT 105 GCU , ECS 501 GCU , ECS 555 GCU , TSL 552 GCU , ECS 560 GCU , NRS 493 GCU , ECS 570 GCU , BUS 352 GCU , MIS 660 GCU