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Homework answers / question archive / Assignment Content Purpose This assignment provides an opportunity to develop, evaluate, and apply bivariate and multivariate linear regression models

**Purpose**

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

**Resources:****Microsoft Excel®, DAT565_v3_Wk5_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 would be 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?
**Submit**your assignment.

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 |