ECON 3050 (Quantitative Methods) – Spring 2020 Final Comprehensive Examination

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On April 29, 2020April 29, 2020By In To Get Answers: WhatsApp/Text +16469781313

ECON 3050 (Quantitative Methods) – Spring 2020 Final Comprehensive Examination Due Date & Time: April 30, 2020, 10:00 PM, CT

Dr. Achintya Ray

Professor of Economics

Department of Economics & Finance

College of Business, Tennessee State University, Nashville, TN, USA

April 20, 2020

Contents

1How to submit this assignment?2
2What Topics You Need to Cover for This Assignment?2
3What Should You Do Before You Work On This Assignment2
4Suggested Videos That You Should Watch3
 4.1Disclaimers  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .………..   3
 4.2Microsoft Excel Resources . . . . . . . . . . . . . . . . . . . . . .………..   3
 4.3Google Sheets Resources: Free and Online . . . . . . . . . . . . .………..   4
5The Data You Need For the Assignment4
 5.1Housing.xlsx  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .………..   4
 5.2Enplanement.xlsx . . . . . . . . . . . . . . . . . . . . . . . . . . .………..   4
6PART 1: Suggested Early Submission Date: April 20, 2020.Accounts for 25%
 of the Final Grade.5
7PART 2: Suggested Early Submission Date: April 25, 2020.Accounts for 25%
 of the Final Grade.5
8PART 3: Suggested Final Submission Date: April 30, 2020. Accounts for 25% of
 the Final Grade.6
    
    

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  1. How to submit this assignment?
  1. Type your answers nicely in a Word document and upload the same in the designated Elearn folder. Any supporting le must be uploaded along with the main submission. All supporting data and calculations must be submitted in an Excel File along with the Word File. You can download a Google Sheet as an Excel File and upload the same in Elearn.
  • If you do not use Word for typing then try to type the answers in a Google Document online using your phone/tablet and download the le as a Word document and then upload that Word document in the designated folder on Elearn. All supporting data and calculations must be submitted in an Excel File along with the Word File. You can download a Google Sheet as an Excel File and upload the same in Elearn.
  • DO NOT VIOLATE the honor code. Your submission MUST BE your OWN WORK. You may earn a failing grade in the assignment if you violate the honor code.
  • No late submission is allowed. Early submission is welcome and strongly encouraged. Make plans to NOT wait until the very last moments to nish and upload your work. You must reserve about 15 20 hours to nish the entire work. Actual time may be more or less depending on your level of preparations.
  • No email submission will be accepted. All submissions MUST BE through Elearn. Otherwise, they will not be graded.
  • What Topics You Need to Cover for This Assignment?

Watch the videos of ANOVA posted in the Elearn. Read the 10 step ANOVA example. Other topics include: Descriptive Statistics, sampling and con dence interval, t-Test, F-Test, 2 – Test. 2 is Pronounced as Chi-Squared, regression analysis, time series and forecasting. All relevant tables are posted in the Elearn.

Numerous video resources are referred below to help you master the essential con-cepts easily.

  • What Should You Do Before You Work On This Assign-ment
  1. Read the whole assignment very very carefully. Make a note of everything that you need to do for this assignment.
  • Read the relevant chapters and the associated lecture slides posted on the Elearn.
  • Consult the additional lecture materials posted on Elearn.
  • Watch the suggested videos mentioned below.
  • The assignment is broken down in many parts. Finish them in order and periodically upload your submissions for di erent parts by the suggested dates mentioned for the individual parts.

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  • Suggested Videos That You Should Watch

4.1.     Disclaimers

The resources are provided so that you can perform your analysis on two largely comparable plat-forms: Microsoft Excel and Google Sheets.

You need to choose between Microsoft Excel or Google Sheets as your preferred ana-lytics platform but it will be entirely ne to learn BOTH.

The videos are referred as learning materials. No product, opinion, advertisement, etc. is en-dorsed. YOU ARE STRONGLY ADVISED TO IGNORE ANY COMMERCIAL MA-

TERIAL THAT IS NOT RELETED TO FORMULAS AND CONCEPTS COVERED IN THE COURSE. Please bring to my attention if you notice any inappropriate material.

4.2.     Microsoft Excel Resources

  1. For Microsoft Windows: How to Install the Data Analysis ToolPak in Microsoft Excel https://www.youtube.com/watch?v= yNxLFagKgw
  1. Excel – One-Way ANOVA Analysis Toolpack https://www.youtube.com/watch?v=nmHFFFpOVZs
  1. F Test in Excel https://www.youtube.com/watch?v=2337cSdINF0
  1. Moving Average Time Series Forecasting with Excel https://www.youtube.com/watch?v=mC1ARrtkObc

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4.3.     Google Sheets Resources: Free and Online

  1. Installing the XLMiner Analysis ToolPak add-on in Google Sheets https://www.youtube.com/watch?v=JHXsKwcRdRw
  1. Using Google Sheets to Calculate Di erences in Proportions (Z Test) https://www.youtube.com/watch?v=1uGvuaCw6t8
  • The Data You Need For the Assignment

Two data sets will be needed to complete this assignment. Both of these data sets are posted in Elearn.

5.1.     Housing.xlsx

This data set contains the following data for 150 homes sold recently in a large city.

Price: In US Dollars

Beds: Number of bedrooms in the house

Baths: Number of bathrooms

Garage: Number of cars that can be parked in a covered space (like a grage)

Sqft-House: Finished Sq-ft for the house

Sqft-Land: Sq-ft of the land on which the house sits

Kitchen: If the kitchen is updated: 0 means not updated & 1 means updated

Roof: Number of years of useful life left for the roof

Age: Age of the house

5.2.     Enplanement.xlsx

Enplanements for U.S. Air Carrier International, Scheduled Passenger Flights, Thousands, Monthly, Seasonally Adjusted.

This is a monthly data starting on January 2000 and ending December, 2019.

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  • 6.        PART 1: Suggested Early Submission Date: April 20, 2020. Accounts for 25% of the Final Grade.

By using the Housing.xlsx data, answer the following questions.

  1. Test the following hypotheses and also state the alternate hypothesis in each case. Write a sentence or two summarizing your conclusion after you have completed each hypothesis testing.

Hypothesis 1 Average price per sq-ft of houses with 3 bedrooms is more than the average price per sq-ft of other houses. (Use the sq-ft of the house only)

Hypothesis 2 Average price per sq-ft of houses with updated kirchen is more than the average price per sq-ft of other houses.

Hypothesis 3 Average price per sq-ft of houses with 15; 000 sq-ft of land is more than the average price per sq-ft of other houses.

Hypothesis 4 Average price per sq-ft of houses with 10 years of usable roof life left is more than the average price per sq-ft of other houses.

Hypothesis 5 Average price per sq-ft of houses with 10 years old houses is more than the average price per sq-ft of other houses.

Hypothesis 6 Variance of the price per sq-ft of houses with 10 years old houses is more than the variance of the price per sq-ft of other houses.

Hypothesis 7 Average price per sq-ft of 3, 4, or, 5 bedroom houses are basiacally equal to each other. (Hint: Think ANOVA)

  • Run the following regressions and present the results nicely. Also interpret the results carefully:
Price per sq-ft of the house =  0 +  1(Sqf tHouse) +(1)
Price per sq-ft of the house =  0 +  1(Sqf tLand) +(2)
Price per sq-ft of the house =  0 +  1(Roof) +(3)
Price per sq-ft of the house =  0 +  1(Age) +(4)
Price per sq-ft of the house =  0 +  1(Beds) +(5)
Price per sq-ft of the house =  0 +  1(Baths) +(6)
Price per sq-ft of the house =  0 +  1(Kitchen) +(7)
  • Write a short essay summarizing your results in the above regressions. Make your presentation in a simple enough format that may be understood by an average home buyer.
  • PART 2: Suggested Early Submission Date: April 25, 2020. Accounts for 25% of the Final Grade.

By using the Housing.xlsx data, answer the following questions.

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1. Run the following regressions and present the results nicely. Also interpret the results carefully:

Price =  0 +  1(Sqf t  House) +  2(Sqf t  Land) +(8)
Price =  0 +  1(Sqf t  House) +  2(Sqf t  Land) +  3(Beds) +  4(Baths) +(9)
Price =  0 +  1(Sqf t  House) +  2(Beds) +  3(Baths) +  4(Age) +(10)
Price =  0 +  1(Sqf tHouse) +  2(Sqf t  Land) +  3(Beds) +  4(Baths) +  5(Kitchen) +
  (11)
Price =  0 + 1(Sqf tHouse) + 2(Garage) + 3(Beds) + 4(Baths) + 5(Kitchen) +  (12)
Price =  0+ 1(Sqf tHouse)+ 2(Garage)+ 3(Beds)+ 4(Baths)+ 5(Kitchen)+ 6(Age)+
  (13)
  • Find out the predicted values of the houses for each of the models above. How do the actual values di er from the predicted values?
  • Based on the regressions that you have estimated above, what do you think will be a good model to esimate the price of a house that you may be considering? You are allowed to run other regression models and nd out if there is another model that performs better than the models above.
  • PART 3: Suggested Final Submission Date: April 30, 2020. Accounts for 25% of the Final Grade.

By using the Enplanement.xlsx data, answer the following questions. Let us assume that Yt denotes the number of passengers who enplaned in month t. By that logic, if Yt represents the data for December, 2018, then Yt 1 represents the data for November, 2018, Yt 2 represents the data for October, 2018, etc.

In the data t represents time that starts at t = 0 or, the beginning of the data.

  1. Present the data in a nice graph and generally describe the changes over time.
  • Derive the 3-month, 6-month, 9-month, and 12-month moving averages of the data and present those series nicely on a graph. Generally describe the changes of the moving averages over time. (Hint: Watch the moving average video avilable at https://tinyurl.com/w5f6e8e and calculate the MAD, MSE, and MAPE to answer this question. It will be pretty straightforward once you watch the video.)
  • Watch the 3-part time series forecasting videos referenced above before you answer this question. Perform the following regressions and explain the resuts in a way that makes good sense.
Yt =  0 +  1(t) +(14)
Yt =  0 +  1(Yt  1) +(15)
Yt =  0 +  1(Yt1) +  2(t) +(16)
Yt =  0 +  1(Yt  1) +  2(Yt  2) +(17)
Yt =  0 +  1(Yt  1) +  2(Yt  2) +  3(t) +(18)
Yt =  0 +  1(Yt  1) +  2(Yt  2) +  3(Yt  3) +(19)
Yt =  0 +  1(Yt  1) +  2(Yt2) +  3(Yt  3) +  4(t) +(20)
Yt =  0 +  1(Yt  1) +  2(Yt  2) +  3(Yt  3) +  4(Yt  4) +  5(t) +(21)

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  • 4.  Which of the above forecasting models estimated above works best for the data that we have? (Hint: First calculate the predicted values from each of the equations. Watch the moving average video avilable at https://tinyurl.com/w5f6e8e and calculate the MAD, MSE, and MAPE (for the di erence between the actual and predicted values) to answer this question. It will be pretty straightforward once you watch the video.)

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