A manager of boiler drums wants to use regression analysis to predict the number

Question

A manager of boiler drums wants to use regression analysis to predict the number of worker-hours needed to erect the drums in future projects. Data for 30 randomly selected boilers have been collected. In addition to work hours (Y, the variables measured include boiler capacity, boiler design pressure boiler type, and drum type. Here is the screenshot of the dataset. Boiler capacity is measured in pounds per hour. Design pressure means in pounds per square inch. Boiler type and Drum type have been recoded into dummy variables. If the boiler type is industrial, it is coded as 1,0 otherwise. If the Dru type is steam, it is coded as 1,0 otherwise. Here is the regression analysis output (Pay attention to the numbers of p values. Ifa p-value includes.an 1. Based on the multiple regression output, please write the complete multiple regression equation to predict the number of worker-hour (5 points). 2. How well does the regression equation fit the given data (5 points)? 3. Give complete interpretations of the estimated regression coefficients for each independent variable (10 points). 4. Interpret the ANOVA table for this model. In particular, does this set of independent variables provide at least some power in explaining the variation in the number of worker-hours (5 points)? (hint: report F statistics and p-value 5. which of the independent variables have significant effects on the number of worker hours at the 5% significant level (5 points)? 6. Using the regression output, determine which of the independent variables could be excluded from the regression equation. Justify your choices (5 points). UMMARY OUTPUT 94703920 89688325 8018457 A Square Standard E 765490023 44137230 0298247 08 813929.83 667196-12 Revicual 173 170936 4360 12850 697 638412 2127 094975 009116383 1547 083 7173 1709 1547.086 1626 272836 5769 00397 1626 27286 5769 00397 433 152872 2821 03708 3433 15287 282108708 0.00956017 001123673 000695604 001127673 4056 .77538387 365 860537 1922209 1317TE 06 02237 334940574 6.31296774 09

Explanation / Answer

ANSWER:

6)

High correlation may lead to Multi-collinearity problem.

Steam and Boiler capacity highly correlated to an output variable. From two highly correlated variables, Boiler capacity can be removed(because of very high correlated p-value).

Please rate it thank you if you have any doubt please comment

Related Questions

Navigate

• Browse (All)
• Browse A
• Subjects

---

---

Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.