Posted: January 18th, 2023

**Purpose **

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

**Resources: **__Microsoft Excel® DAT5/65 Week 5 Data File__

**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?

SOLUTION:

- To construct a scatter plot in Excel with FloorArea as the independent variable and AssessmentValue as the dependent variable, you would need to organize the data in the Excel file so that the FloorArea values are in one column, and the AssessmentValue values are in another column. Once you have the data organized, you can create a scatter plot by selecting the two columns of data and using the scatter plot option in Excel’s chart creation tools. The bivariate linear regression equation and r^2 can be added to the graph by using Excel’s built-in regression analysis tool. Observing the scatter plot, if the points are closely following a linear pattern, it indicates a linear relationship exists between the two variables.
- Using Excel’s Analysis ToolPak, you can conduct a regression analysis of FloorArea and AssessmentValue. The output will show the coefficient of determination (r^2) and the p-value for the FloorArea variable. If the p-value is less than 0.05, it means that FloorArea is a significant predictor of AssessmentValue.
- To construct a scatter plot in Excel with Age as the independent variable and AssessmentValue as the dependent variable, you would need to organize the data in the Excel file so that the Age values are in one column, and the AssessmentValue values are in another column. Once you have the data organized, you can create a scatter plot by selecting the two columns of data and using the scatter plot option in Excel’s chart creation tools. The bivariate linear regression equation and r^2 can be added to the graph by using Excel’s built-in regression analysis tool. Observing the scatter plot, if the points are closely foll……….

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