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DAT/565: Data Analysis And Business Analytics

Course Grades

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Andreia Smith

Overall Grade

B+

 

Item Name

Due Date

Status

Grade

Feedback

Total

600.1 / 685

Wk 1 Discussion – Data Analytics and Statistics [due Thurs]

First participated on 6/4/20

6/9/20

Graded

40 / 40

The instructor provided comments for this item

Wk 1 – Practice: Ch 1, Overview of Statistics [due Sat]

1 attempt submitted (0 Late)

6/7/20

Graded

5 / 5

Wk 1 – Practice: Ch 2, Data Collection Selections [due Sat]

1 attempt submitted (0 Late)

6/7/20

Graded

5 / 5

Wk 1 – Practice: Ch 3, Describing Data Visually Selections [due Sat]

1 attempt submitted (0 Late)

6/7/20

Graded

5 / 5

Wk 1 – Practice: Wk 1 Knowledge Check [due Sat]

1 attempt submitted (0 Late)

6/7/20

Graded

10 / 10

Wk 1 – Practice: Wk 1 Exercises [due Sat]

1 attempt submitted (0 Late)

6/7/20

Graded

27.29 / 30

Wk 1 – Apply: Statistics Analysis [due Mon]

Attempt 2 started (1 Late)

6/9/20

Draft saved

50 / 50

The instructor provided comments for this item

Wk 1 – Learn: Wk 1 Videos

6/7/20

Unopened

— / 0

Wk 2 Discussion – Metrics and Information Visualization [due Thurs]

First participated on 6/11/20

6/16/20

Graded

40 / 40

The instructor provided comments for this item

Wk 2 – Practice: Ch 2, Data Collection Selections [due Sat]

1 attempt submitted (0 Late)

6/14/20

Graded

5 / 5

Wk 2 – Practice: Ch 3, Describing Data Visually Selections [due Sat]

1 attempt submitted (0 Late)

6/14/20

Graded

5 / 5

Wk 2 – Practice: Ch 4, Descriptive Statistics [due Sat]

1 attempt submitted (0 Late)

6/14/20

Graded

5 / 5

Wk 2 – Practice: Wk 2 Knowledge Check [due Sat]

1 attempt submitted (0 Late)

6/14/20

Graded

10 / 10

Wk 2 – Practice: Wk 2 Exercises [due Sat]

1 attempt submitted (0 Late)

6/14/20

Graded

2 / 30

Wk 2 – Apply: Signature Assignment: Statistical Report [due Mon]

Attempt 2 started (0 Late)

6/16/20

Draft saved

79.2 / 90

The instructor provided comments for this item

Wk 2 – Learn: Wk 2 Videos

12/31/29

Unopened

— / 0

Wk 3 Discussion – Characterizing Uncertainty [due Thurs]

First participated on 6/18/20

6/23/20

Graded

40 / 40

The instructor provided comments for this item

Wk 3 – Practice: Ch 5, Profitability [due Sat]

1 attempt submitted (0 Late)

6/21/20

Graded

5 / 5

Wk 3 – Practice: Ch 6, Discrete Probability Distributions [due Sat]

1 attempt submitted (0 Late)

6/21/20

Graded

5 / 5

Wk 3 – Practice: Ch 7, Continuous Probability Distribution [due Sat]

1 attempt submitted (0 Late)

6/21/20

Graded

5 / 5

Wk 3 – Practice: Ch 8, Sampling Distributions and Estimation [due Sat]

1 attempt submitted (0 Late)

6/21/20

Graded

5 / 5

Wk 3 – Practice: Wk 3 Knowledge Check [due Sat]

1 attempt submitted (0 Late)

6/21/20

Graded

10 / 10

Wk 3 – Practice: Wk 3 Exercise [due Sat]

1 attempt submitted (0 Late)

6/21/20

Graded

21.83 / 30

Wk 3 – Apply: Market Analysis Research [due Mon]

1 attempt submitted (0 Late)

6/23/20

Graded

62 / 70

The instructor provided comments for this item

Wk 4 Discussion – Testing Hypotheses [due Thurs]

First participated on 6/26/20

6/30/20

Graded

40 / 40

The instructor provided comments for this item

Wk 4 – Practice: Ch 9, One-Sample Hypothesis Tests [due Sat]

1 attempt submitted (0 Late)

6/28/20

Graded

5 / 5

Wk 4 – Practice: Ch 10, Two Sample Hypothesis Tests [due Sat]

1 attempt submitted (0 Late)

6/28/20

Graded

5 / 5

Wk 4 – Practice: Ch 11, Analysis of Variance [due Sat]

1 attempt submitted (0 Late)

6/28/20

Graded

5 / 5

Wk 4 – Practice: Wk 4 Knowledge Check [due Sat]

1 attempt submitted (0 Late)

6/28/20

Graded

10 / 10

Wk 4 – Practice: Wk 4 Exercises [due Sat]

1 attempt submitted (0 Late)

6/28/20

Graded

15.38 / 30

Wk 4 – Apply: Signature Assignment: Globalization and Information Research [due Mon]

6/30/20

Graded

77.4 / 90

The instructor provided comments for this item

Wk 5 Discussion – Patterns and Modeling [due Thurs]

First participated on 7/2/20

7/7/20

Submitted

Not graded

Wk 5 – Practice: Ch 12, Simple Regression [due Sat]

7/7/20

Unopened

— / 5

Wk 5 – Practice: Ch, 13 Multiple Regression [due Sat]

7/7/20

Unopened

— / 5

Wk 5 – Practice: Wk 5 Knowledge Check [due Sat]

7/7/20

Unopened

— / 10

Wk 5 – Practice: Wk 5 Exercises [due Sat]

7/7/20

Unopened

— / 30

Wk 5 – Apply: Regression Modeling [due Mon]

7/7/20

Draft saved

Not graded

Wk 6 Discussion – Time Series Modeling [due Thurs]

No participation

7/14/20

Unopened

— / 40

Wk 6 – Practice: Wk 6 Knowledge Check [due Sat]

12/31/29

Unopened

— / 10

Wk 6 – Practice: Ch. 14, Time-Series Analysis [due Sat]

12/31/29

Unopened

— / 5

Wk 6 – Practice: Wk 6 Exercises [due Sat]

12/31/29

Unopened

— / 30

Wk 6 – Apply: Signature Assignment: Smart Parking Space App Presentation [due Mon]

7/14/20

Unopened

— / 90

×

DAT/565: Data Analysis And Business Analytics

Wk 5 – Apply: Regression Modeling [due Mon]

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Assignment Content

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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 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?

 

Submit your assignment.

 

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ANSWER

 

 

 

Presence of linear relationship between the 2 variables?

Yes

 

FloorArea vs AssessmentValue?

Yes.

r²-value = 0.938 i.e. FloorArea predicts AssessmentValue by 93.8% (Knight, 2018; Kutner et al, 2005).

 

Linear relationship between the 2 variables?

No

 

Age vs AssessmentValue?

No.

r²-value = 0.032 which is in-significant, therefore Age can’t predict AssessmentValue.

p-value = 0.327 which is greater than 0.05 (Olive, 2017).

 

AssessmentValue vs. FloorArea, Offices, Entrances, and Age.

Overall fit r²? 95.3%

Adjusted r²? 94.6%

 

Which predictors are considered significant if we work with α=0.05?

FloorArea and Number of Offices in the building (Morrissey & Ruxton, 2018).

 

Which predictors can be eliminated?

Age and Number of entrances

 

Final model?

AssessedValue = (0.244 x FloorArea) + (80.946 x Offices)

 

Assessed value of a medical office building with a floor area of 3500 sq. ft., 2 offices, that was built 15 years ago?

AssessedValue = (0.244 x 3500) + (80.946 x 2) = 1015.88

 

Is this assessed value consistent with what appears in the database?

Yes

 

References

Knight, G. P. (2018). A survey of some important techniques and issues in multiple regression. In New methods in reading comprehension research (pp. 13-30). Routledge.

Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2005). Applied linear statistical models (Vol. 5). New York: McGraw-Hill Irwin.

Morrissey, M. B., & Ruxton, G. D. (2018). Multiple regression is not multiple regressions: the meaning of multiple regression and the non-problem of collinearity.

Olive, D. J. (2017). Linear regression. Heidelberg: Springer.