Chat with us, powered by LiveChat Purpose? This assignment provides an opportunity to develop, evaluate, and apply bivariate and multivariate linear regression models. Resources:?M - Writeedu

## 15 May Purpose? This assignment provides an opportunity to develop, evaluate, and apply bivariate and multivariate linear regression models. Resources:?M

Purpose

This assignment provides an opportunity to develop, evaluate, and apply bivariate and multivariate linear regression models.

Instructions:

The Excel file for this assignment contains a database with information about the tax assessment value assigned to medical office buildings in a city. The following is a list of the variables in the database:

• FloorArea: square feet of floor space
• Offices: number of offices in the building
• Entrances: number of customer entrances
• Age: age of the building (years)
• AssessedValue: tax assessment value (thousands of dollars)

Use the data to construct a model that predicts the tax assessment value assigned to medical office buildings with specific characteristics.

• Construct a scatter plot in Excel with FloorArea as the independent variable and AssessmentValue as the dependent variable. Insert the bivariate linear regression equation and r^2 in your graph. Do you observe a linear relationship between the 2 variables?
• Use Excel’s Analysis ToolPak to conduct a regression analysis of FloorArea and AssessmentValue. Is FloorArea a significant predictor of AssessmentValue?
• Construct a scatter plot in Excel with Age as the independent variable and AssessmentValue as the dependent variable. Insert the bivariate linear regression equation and r^2 in your graph. Do you observe a linear relationship between the 2 variables?
• Use Excel’s Analysis ToolPak to conduct a regression analysis of Age and Assessment Value. Is Age a significant predictor of AssessmentValue?

Construct a multiple regression model.

• Use Excel’s Analysis ToolPak to conduct a regression analysis with AssessmentValue as the dependent variable and FloorArea, Offices, Entrances, and Age as independent variables. What is the overall fit r^2? What is the adjusted r^2?
• Which predictors are considered significant if we work with α=0.05? Which predictors can be eliminated?
• What is the final model if we only use FloorArea and Offices as predictors?
• Suppose our final model is:
• AssessedValue = 115.9 + 0.26 x FloorArea + 78.34 x Offices
• What wouldbe the assessed value of a medical office building with a floor area of 3500 sq. ft., 2 offices, that was built 15 years ago? Is this assessed value consistent with what appears in the database?

## Regression Modeling Data

 FloorArea (Sq.Ft.) Offices Entrances Age AssessedValue (\$'000) 4790 4 2 8 1796 4720 3 2 12 1544 5940 4 2 2 2094 5720 4 2 34 1968 3660 3 2 38 1567 5000 4 2 31 1878 2990 2 1 19 949 2610 2 1 48 910 5650 4 2 42 1774 3570 2 1 4 1187 2930 3 2 15 1113 1280 2 1 31 671 4880 3 2 42 1678 1620 1 2 35 710 1820 2 1 17 678 4530 2 2 5 1585 2570 2 1 13 842 4690 2 2 45 1539 1280 1 1 45 433 4100 3 1 27 1268 3530 2 2 41 1251 3660 2 2 33 1094 1110 1 2 50 638 2670 2 2 39 999 1100 1 1 20 653 5810 4 3 17 1914 2560 2 2 24 772 2340 3 1 5 890 3690 2 2 15 1282 3580 3 2 27 1264 3610 2 1 8 1162 3960 3 2 17 1447

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