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    will need toight if possible – 2 years ago **files (2)** – ## project_description.docx – ## BUSA511_individual_project_template1.docx **## project_description.docx** **Project Assignment BUSA 511, Business Analytics for Managers** **The case** Considering monthly demands for the ABC Corporation…

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    will need toight if  possible

    – 2 years ago

    **files (2)**

    – ## project_description.docx

    – ## BUSA511_individual_project_template1.docx

    **## project_description.docx**

    **Project Assignment BUSA 511, Business Analytics for Managers**

    **The case**

    Considering monthly demands for the ABC Corporation as shown below.

    ![](https://www.sweetstudy.com/questions/business-ana-20892301)

    **Question**

    Forecast the monthly demands for Year 6 using
    **moving average**,
    **simple exponential smoothing**,
    **Holts' model**, and
    **Winter's model**. In each case, evaluate the bias, TS, MAD, MAPE, and MSE.

    **Project Assignment Guidance**

    1. To answer the question above, firstly based on the monthly sales data of Year 1, 2, 3, 4 and 5, you should forecast the 12 monthly demands of Year 6 from January to December using "
    **_moving average"_**, "
    **_simple exponential smoothing"_**, "
    **_Holts' model_**", and "
    **_Winter's model”, respectively_**. Secondly, you should calculate the forecast-error indicators of bias, TS, MAD, MAPE, and MSE regarding these four models. Thirdly, based on your findings, you should identify the best forecasting methods and provide your justifications.

    2. Two documents should be submitted for this assignment.
    **The first** is the Excel file in which the formula, the calculation processes and solutions of the four forecasting methods, and the related forecast-error estimates of bias, TS, MAD, MAPE, and MSE should be given.
    **The second** is the word file template as report by answering questions asked in the report template. This word file template will be posted on D2L.

    **_(_**
    **_Note: Sample of the work in excel and Video recording of the tutorial is posted on D2L)_**

    3. The mark weighting of the analysis of the above four forecasting methods will be listed in the report template posted on D2L.

    4. The submission deadline is stated on D2L page for the course.

    5. You should submit e-copy of your assignment (word-format report with Excel file(s), through the submission portal on D2L (no hard copy needed)

    2

    **image1.emf**

    **## BUSA511_individual_project_template1.docx**

    ![page1image20678064](https://www.sweetstudy.com/questions/business-ana-20892301)

    **BUSA 511: Project Assignment cover page**

    Assignment for Course:      BUSA 511: Business Analytics for Managers

    Submitted to:                 Dr. Syed A. Raza

    Submitted by:

    | | |
    | — | — |
    | **Student Name** | **CWID** |
    | | |

    :

    Title of Assignment:
    **Time Series Forecasting Group Project**

    CERTIFICATION OF AUTHORSHIP: I certify that I am the author of this paper and that any assistance received in its preparation is fully acknowledge and disclosed in the paper. I have also cited any sources from which we used data, ideas of words, whether quoted directly or paraphrased.  I also certify that this paper was prepared by me specifically for this course

    \*\*\*****************************************

    **Time Series Forecasting Project Instructions**

    **_Instructions_**

    This is an individual assignment and therefore must be completed by an individual student without outside assistance of any type. Follow the instructions below in order to complete the assignment:

    ·
    **Step 1**: Download and read the project statement for the
    _on the Forecasting problem_ stated in the Microsoft Word file “
    **project_description.docx**”. Read carefully this document

    ·
    **Step 2**: Download the Excel file “
    **project_data.xlsx**”. Use the data in the file to perform timeseries forecasting analyses.

    ·
    **Step 3**: Based on your analytics performed and use of the models in Step 2, answer the questions and provide a short (
    _not exceeding 500 words_) recommendation/explanation

    **_Submittals_**

    1. Submit your answers to this assignment using this Microsoft Word document and post to the assignment drop box before the required deadline. Be sure to complete the above first page cover sheet. Enter your answers in the pages below to include all of your answers and results of your interpretations of calculations for the assignment questions. Your answers must be entered directly into this Word document below each question. Use as much space as needed.

    2. Submit your Excel spreadsheet(s) with calculations/ visualizations to the assignment drop box before the required deadline. Your Excel model calculations will be used to substantiate your answers to the assignment questions herein.

    **_Grading_**

    This is an individual assignment. A total of 100 percentage points is possible for this assignment. This includes the point values which are assigned to each question – point values are noted next to each question below. Use APA 7 writing style as a guide for answers that require a written explanation. Up to 10 points will be deducted from the combination of written explanation answers that are poorly written. The percentage points earned on this assignment will be 20% weight obtain the final assignment grade.

    Please answer following Question with reference to the project file: “
    **Project_description.docx**”

    **_Question 1_** (6 points): Define a problem statement which reflects the challenge faced in this prediction problem (
    _Do not exceed 500 words_)

    **Answer**:

    **_Question 2_** (6 points): Develop a 3-period moving average forecasting model. Report the forecasts for year 6 from months January through December inclusive. Discuss briefly these forecasts (
    _Discussion NOT to exceed 500 words_)

    **Answer**:

    **_Question 3_** (6 points): Compute for the model developed in Question 2, compute the error parameters MAD, MSE, MAPE, and TS. Explain these error computations (
    _Explanation NOT to exceed 500 words_)

    **Answer**:

    **_Question 4_** (6 points): Develop a simple exponential smoothing forecasting model, assume, =0.2. Report the forecasts for year 6 from months January through December inclusive. Discuss briefly these forecasts (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 5_** (6 points): For the model developed in Question 4, compute the error parameters MAD, MSE, MAPE, and TS.

    **Answer**:

    **_Question 6_** (6 points): For the model developed in Question 4, using Excel Solver optimize the value of with and objective to minimize MSE. Report your results provide a discussion how optimization improved the forecasting error (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 7_** (6 points): Develop a Holt’s model for forecasting, assume, =0.3, and =0.1. Report the forecasts for year 6 from months January through December inclusive. Discuss briefly these forecasts (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 8_** (6 points): For the model developed in Question 7, compute the error parameters MAD, MSE, MAPE, and TS.

    **Answer**:

    **_Question 9_** (6 points): For the model developed in Question 7, using Excel Solver optimize the values of and with an objective to minimize MAD. Report your results provide a Discussion how optimization improved the forecasting error (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 10_** (6 points): Develop a Winter’s model for forecasting, assume, =0.2, =0.3, and =0.1. Report the forecasts for year 6 from months January through December inclusive. Discuss briefly these forecasts (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 11_** (6 points): For the model developed in Question 10, compute the error parameters MAD, MSE, MAPE, and TS.

    **Answer**:

    **_Question 12_** (6 points): For the model developed in Question 10, using Excel Solver optimize the values of , , and with an objective to minimize MAPE. Report your results provide a Discussion how optimization improved the forecasting error (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 13_** (6 points): Discuss the model developed in Question 10 is how different from model developed in Question 7, (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 14_** (22 points): Which model you will select and Why and how does the optimization improve the forecasting performance of the methods? (
    _1 to 2-page discussion recommend_)

    1

    **image1.png**

    **## BUSA511_individual_project_template1.docx**

    ![page1image20678064](https://www.sweetstudy.com/questions/business-ana-20892301)

    **BUSA 511: Project Assignment cover page**

    Assignment for Course:      BUSA 511: Business Analytics for Managers

    Submitted to:                 Dr. Syed A. Raza

    Submitted by:

    | | |
    | — | — |
    | **Student Name** | **CWID** |
    | | |

    :

    Title of Assignment:
    **Time Series Forecasting Group Project**

    CERTIFICATION OF AUTHORSHIP: I certify that I am the author of this paper and that any assistance received in its preparation is fully acknowledge and disclosed in the paper. I have also cited any sources from which we used data, ideas of words, whether quoted directly or paraphrased.  I also certify that this paper was prepared by me specifically for this course

    \*\*\*****************************************

    **Time Series Forecasting Project Instructions**

    **_Instructions_**

    This is an individual assignment and therefore must be completed by an individual student without outside assistance of any type. Follow the instructions below in order to complete the assignment:

    ·
    **Step 1**: Download and read the project statement for the
    _on the Forecasting problem_ stated in the Microsoft Word file “
    **project_description.docx**”. Read carefully this document

    ·
    **Step 2**: Download the Excel file “
    **project_data.xlsx**”. Use the data in the file to perform timeseries forecasting analyses.

    ·
    **Step 3**: Based on your analytics performed and use of the models in Step 2, answer the questions and provide a short (
    _not exceeding 500 words_) recommendation/explanation

    **_Submittals_**

    1. Submit your answers to this assignment using this Microsoft Word document and post to the assignment drop box before the required deadline. Be sure to complete the above first page cover sheet. Enter your answers in the pages below to include all of your answers and results of your interpretations of calculations for the assignment questions. Your answers must be entered directly into this Word document below each question. Use as much space as needed.

    2. Submit your Excel spreadsheet(s) with calculations/ visualizations to the assignment drop box before the required deadline. Your Excel model calculations will be used to substantiate your answers to the assignment questions herein.

    **_Grading_**

    This is an individual assignment. A total of 100 percentage points is possible for this assignment. This includes the point values which are assigned to each question – point values are noted next to each question below. Use APA 7 writing style as a guide for answers that require a written explanation. Up to 10 points will be deducted from the combination of written explanation answers that are poorly written. The percentage points earned on this assignment will be 20% weight obtain the final assignment grade.

    Please answer following Question with reference to the project file: “
    **Project_description.docx**”

    **_Question 1_** (6 points): Define a problem statement which reflects the challenge faced in this prediction problem (
    _Do not exceed 500 words_)

    **Answer**:

    **_Question 2_** (6 points): Develop a 3-period moving average forecasting model. Report the forecasts for year 6 from months January through December inclusive. Discuss briefly these forecasts (
    _Discussion NOT to exceed 500 words_)

    **Answer**:

    **_Question 3_** (6 points): Compute for the model developed in Question 2, compute the error parameters MAD, MSE, MAPE, and TS. Explain these error computations (
    _Explanation NOT to exceed 500 words_)

    **Answer**:

    **_Question 4_** (6 points): Develop a simple exponential smoothing forecasting model, assume, =0.2. Report the forecasts for year 6 from months January through December inclusive. Discuss briefly these forecasts (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 5_** (6 points): For the model developed in Question 4, compute the error parameters MAD, MSE, MAPE, and TS.

    **Answer**:

    **_Question 6_** (6 points): For the model developed in Question 4, using Excel Solver optimize the value of with and objective to minimize MSE. Report your results provide a discussion how optimization improved the forecasting error (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 7_** (6 points): Develop a Holt’s model for forecasting, assume, =0.3, and =0.1. Report the forecasts for year 6 from months January through December inclusive. Discuss briefly these forecasts (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 8_** (6 points): For the model developed in Question 7, compute the error parameters MAD, MSE, MAPE, and TS.

    **Answer**:

    **_Question 9_** (6 points): For the model developed in Question 7, using Excel Solver optimize the values of and with an objective to minimize MAD. Report your results provide a Discussion how optimization improved the forecasting error (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 10_** (6 points): Develop a Winter’s model for forecasting, assume, =0.2, =0.3, and =0.1. Report the forecasts for year 6 from months January through December inclusive. Discuss briefly these forecasts (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 11_** (6 points): For the model developed in Question 10, compute the error parameters MAD, MSE, MAPE, and TS.

    **Answer**:

    **_Question 12_** (6 points): For the model developed in Question 10, using Excel Solver optimize the values of , , and with an objective to minimize MAPE. Report your results provide a Discussion how optimization improved the forecasting error (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 13_** (6 points): Discuss the model developed in Question 10 is how different from model developed in Question 7, (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 14_** (22 points): Which model you will select and Why and how does the optimization improve the forecasting performance of the methods? (
    _1 to 2-page discussion recommend_)

    1

    **image1.png**

    **## project_description.docx**

    **Project Assignment BUSA 511, Business Analytics for Managers**

    **The case**

    Considering monthly demands for the ABC Corporation as shown below.

    ![](https://www.sweetstudy.com/questions/business-ana-20892301)

    **Question**

    Forecast the monthly demands for Year 6 using
    **moving average**,
    **simple exponential smoothing**,
    **Holts' model**, and
    **Winter's model**. In each case, evaluate the bias, TS, MAD, MAPE, and MSE.

    **Project Assignment Guidance**

    1. To answer the question above, firstly based on the monthly sales data of Year 1, 2, 3, 4 and 5, you should forecast the 12 monthly demands of Year 6 from January to December using "
    **_moving average"_**, "
    **_simple exponential smoothing"_**, "
    **_Holts' model_**", and "
    **_Winter's model”, respectively_**. Secondly, you should calculate the forecast-error indicators of bias, TS, MAD, MAPE, and MSE regarding these four models. Thirdly, based on your findings, you should identify the best forecasting methods and provide your justifications.

    2. Two documents should be submitted for this assignment.
    **The first** is the Excel file in which the formula, the calculation processes and solutions of the four forecasting methods, and the related forecast-error estimates of bias, TS, MAD, MAPE, and MSE should be given.
    **The second** is the word file template as report by answering questions asked in the report template. This word file template will be posted on D2L.

    **_(_**
    **_Note: Sample of the work in excel and Video recording of the tutorial is posted on D2L)_**

    3. The mark weighting of the analysis of the above four forecasting methods will be listed in the report template posted on D2L.

    4. The submission deadline is stated on D2L page for the course.

    5. You should submit e-copy of your assignment (word-format report with Excel file(s), through the submission portal on D2L (no hard copy needed)

    2

    **image1.emf**

    **## BUSA511_individual_project_template1.docx**

    ![page1image20678064](https://www.sweetstudy.com/questions/business-ana-20892301)

    **BUSA 511: Project Assignment cover page**

    Assignment for Course:      BUSA 511: Business Analytics for Managers

    Submitted to:                 Dr. Syed A. Raza

    Submitted by:

    | | |
    | — | — |
    | **Student Name** | **CWID** |
    | | |

    :

    Title of Assignment:
    **Time Series Forecasting Group Project**

    CERTIFICATION OF AUTHORSHIP: I certify that I am the author of this paper and that any assistance received in its preparation is fully acknowledge and disclosed in the paper. I have also cited any sources from which we used data, ideas of words, whether quoted directly or paraphrased.  I also certify that this paper was prepared by me specifically for this course

    \*\*\*****************************************

    **Time Series Forecasting Project Instructions**

    **_Instructions_**

    This is an individual assignment and therefore must be completed by an individual student without outside assistance of any type. Follow the instructions below in order to complete the assignment:

    ·
    **Step 1**: Download and read the project statement for the
    _on the Forecasting problem_ stated in the Microsoft Word file “
    **project_description.docx**”. Read carefully this document

    ·
    **Step 2**: Download the Excel file “
    **project_data.xlsx**”. Use the data in the file to perform timeseries forecasting analyses.

    ·
    **Step 3**: Based on your analytics performed and use of the models in Step 2, answer the questions and provide a short (
    _not exceeding 500 words_) recommendation/explanation

    **_Submittals_**

    1. Submit your answers to this assignment using this Microsoft Word document and post to the assignment drop box before the required deadline. Be sure to complete the above first page cover sheet. Enter your answers in the pages below to include all of your answers and results of your interpretations of calculations for the assignment questions. Your answers must be entered directly into this Word document below each question. Use as much space as needed.

    2. Submit your Excel spreadsheet(s) with calculations/ visualizations to the assignment drop box before the required deadline. Your Excel model calculations will be used to substantiate your answers to the assignment questions herein.

    **_Grading_**

    This is an individual assignment. A total of 100 percentage points is possible for this assignment. This includes the point values which are assigned to each question – point values are noted next to each question below. Use APA 7 writing style as a guide for answers that require a written explanation. Up to 10 points will be deducted from the combination of written explanation answers that are poorly written. The percentage points earned on this assignment will be 20% weight obtain the final assignment grade.

    Please answer following Question with reference to the project file: “
    **Project_description.docx**”

    **_Question 1_** (6 points): Define a problem statement which reflects the challenge faced in this prediction problem (
    _Do not exceed 500 words_)

    **Answer**:

    **_Question 2_** (6 points): Develop a 3-period moving average forecasting model. Report the forecasts for year 6 from months January through December inclusive. Discuss briefly these forecasts (
    _Discussion NOT to exceed 500 words_)

    **Answer**:

    **_Question 3_** (6 points): Compute for the model developed in Question 2, compute the error parameters MAD, MSE, MAPE, and TS. Explain these error computations (
    _Explanation NOT to exceed 500 words_)

    **Answer**:

    **_Question 4_** (6 points): Develop a simple exponential smoothing forecasting model, assume, =0.2. Report the forecasts for year 6 from months January through December inclusive. Discuss briefly these forecasts (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 5_** (6 points): For the model developed in Question 4, compute the error parameters MAD, MSE, MAPE, and TS.

    **Answer**:

    **_Question 6_** (6 points): For the model developed in Question 4, using Excel Solver optimize the value of with and objective to minimize MSE. Report your results provide a discussion how optimization improved the forecasting error (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 7_** (6 points): Develop a Holt’s model for forecasting, assume, =0.3, and =0.1. Report the forecasts for year 6 from months January through December inclusive. Discuss briefly these forecasts (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 8_** (6 points): For the model developed in Question 7, compute the error parameters MAD, MSE, MAPE, and TS.

    **Answer**:

    **_Question 9_** (6 points): For the model developed in Question 7, using Excel Solver optimize the values of and with an objective to minimize MAD. Report your results provide a Discussion how optimization improved the forecasting error (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 10_** (6 points): Develop a Winter’s model for forecasting, assume, =0.2, =0.3, and =0.1. Report the forecasts for year 6 from months January through December inclusive. Discuss briefly these forecasts (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 11_** (6 points): For the model developed in Question 10, compute the error parameters MAD, MSE, MAPE, and TS.

    **Answer**:

    **_Question 12_** (6 points): For the model developed in Question 10, using Excel Solver optimize the values of , , and with an objective to minimize MAPE. Report your results provide a Discussion how optimization improved the forecasting error (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 13_** (6 points): Discuss the model developed in Question 10 is how different from model developed in Question 7, (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 14_** (22 points): Which model you will select and Why and how does the optimization improve the forecasting performance of the methods? (
    _1 to 2-page discussion recommend_)

    1

    **image1.png**

    **## project_description.docx**

    **Project Assignment BUSA 511, Business Analytics for Managers**

    **The case**

    Considering monthly demands for the ABC Corporation as shown below.

    ![](https://www.sweetstudy.com/questions/business-ana-20892301)

    **Question**

    Forecast the monthly demands for Year 6 using
    **moving average**,
    **simple exponential smoothing**,
    **Holts' model**, and
    **Winter's model**. In each case, evaluate the bias, TS, MAD, MAPE, and MSE.

    **Project Assignment Guidance**

    1. To answer the question above, firstly based on the monthly sales data of Year 1, 2, 3, 4 and 5, you should forecast the 12 monthly demands of Year 6 from January to December using "
    **_moving average"_**, "
    **_simple exponential smoothing"_**, "
    **_Holts' model_**", and "
    **_Winter's model”, respectively_**. Secondly, you should calculate the forecast-error indicators of bias, TS, MAD, MAPE, and MSE regarding these four models. Thirdly, based on your findings, you should identify the best forecasting methods and provide your justifications.

    2. Two documents should be submitted for this assignment.
    **The first** is the Excel file in which the formula, the calculation processes and solutions of the four forecasting methods, and the related forecast-error estimates of bias, TS, MAD, MAPE, and MSE should be given.
    **The second** is the word file template as report by answering questions asked in the report template. This word file template will be posted on D2L.

    **_(_**
    **_Note: Sample of the work in excel and Video recording of the tutorial is posted on D2L)_**

    3. The mark weighting of the analysis of the above four forecasting methods will be listed in the report template posted on D2L.

    4. The submission deadline is stated on D2L page for the course.

    5. You should submit e-copy of your assignment (word-format report with Excel file(s), through the submission portal on D2L (no hard copy needed)

    2

    **image1.emf**

    **## BUSA511_individual_project_template1.docx**

    ![page1image20678064](https://www.sweetstudy.com/questions/business-ana-20892301)

    **BUSA 511: Project Assignment cover page**

    Assignment for Course:      BUSA 511: Business Analytics for Managers

    Submitted to:                 Dr. Syed A. Raza

    Submitted by:

    | | |
    | — | — |
    | **Student Name** | **CWID** |
    | | |

    :

    Title of Assignment:
    **Time Series Forecasting Group Project**

    CERTIFICATION OF AUTHORSHIP: I certify that I am the author of this paper and that any assistance received in its preparation is fully acknowledge and disclosed in the paper. I have also cited any sources from which we used data, ideas of words, whether quoted directly or paraphrased.  I also certify that this paper was prepared by me specifically for this course

    \*\*\*****************************************

    **Time Series Forecasting Project Instructions**

    **_Instructions_**

    This is an individual assignment and therefore must be completed by an individual student without outside assistance of any type. Follow the instructions below in order to complete the assignment:

    ·
    **Step 1**: Download and read the project statement for the
    _on the Forecasting problem_ stated in the Microsoft Word file “
    **project_description.docx**”. Read carefully this document

    ·
    **Step 2**: Download the Excel file “
    **project_data.xlsx**”. Use the data in the file to perform timeseries forecasting analyses.

    ·
    **Step 3**: Based on your analytics performed and use of the models in Step 2, answer the questions and provide a short (
    _not exceeding 500 words_) recommendation/explanation

    **_Submittals_**

    1. Submit your answers to this assignment using this Microsoft Word document and post to the assignment drop box before the required deadline. Be sure to complete the above first page cover sheet. Enter your answers in the pages below to include all of your answers and results of your interpretations of calculations for the assignment questions. Your answers must be entered directly into this Word document below each question. Use as much space as needed.

    2. Submit your Excel spreadsheet(s) with calculations/ visualizations to the assignment drop box before the required deadline. Your Excel model calculations will be used to substantiate your answers to the assignment questions herein.

    **_Grading_**

    This is an individual assignment. A total of 100 percentage points is possible for this assignment. This includes the point values which are assigned to each question – point values are noted next to each question below. Use APA 7 writing style as a guide for answers that require a written explanation. Up to 10 points will be deducted from the combination of written explanation answers that are poorly written. The percentage points earned on this assignment will be 20% weight obtain the final assignment grade.

    Please answer following Question with reference to the project file: “
    **Project_description.docx**”

    **_Question 1_** (6 points): Define a problem statement which reflects the challenge faced in this prediction problem (
    _Do not exceed 500 words_)

    **Answer**:

    **_Question 2_** (6 points): Develop a 3-period moving average forecasting model. Report the forecasts for year 6 from months January through December inclusive. Discuss briefly these forecasts (
    _Discussion NOT to exceed 500 words_)

    **Answer**:

    **_Question 3_** (6 points): Compute for the model developed in Question 2, compute the error parameters MAD, MSE, MAPE, and TS. Explain these error computations (
    _Explanation NOT to exceed 500 words_)

    **Answer**:

    **_Question 4_** (6 points): Develop a simple exponential smoothing forecasting model, assume, =0.2. Report the forecasts for year 6 from months January through December inclusive. Discuss briefly these forecasts (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 5_** (6 points): For the model developed in Question 4, compute the error parameters MAD, MSE, MAPE, and TS.

    **Answer**:

    **_Question 6_** (6 points): For the model developed in Question 4, using Excel Solver optimize the value of with and objective to minimize MSE. Report your results provide a discussion how optimization improved the forecasting error (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 7_** (6 points): Develop a Holt’s model for forecasting, assume, =0.3, and =0.1. Report the forecasts for year 6 from months January through December inclusive. Discuss briefly these forecasts (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 8_** (6 points): For the model developed in Question 7, compute the error parameters MAD, MSE, MAPE, and TS.

    **Answer**:

    **_Question 9_** (6 points): For the model developed in Question 7, using Excel Solver optimize the values of and with an objective to minimize MAD. Report your results provide a Discussion how optimization improved the forecasting error (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 10_** (6 points): Develop a Winter’s model for forecasting, assume, =0.2, =0.3, and =0.1. Report the forecasts for year 6 from months January through December inclusive. Discuss briefly these forecasts (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 11_** (6 points): For the model developed in Question 10, compute the error parameters MAD, MSE, MAPE, and TS.

    **Answer**:

    **_Question 12_** (6 points): For the model developed in Question 10, using Excel Solver optimize the values of , , and with an objective to minimize MAPE. Report your results provide a Discussion how optimization improved the forecasting error (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 13_** (6 points): Discuss the model developed in Question 10 is how different from model developed in Question 7, (
    _Discussion NOT to exceed 500 words)_

    **Answer**:

    **_Question 14_** (22 points): Which model you will select and Why and how does the optimization improve the forecasting performance of the methods? (
    _1 to 2-page discussion recommend_)

    1

    **image1.png**

  • [Solved] Reflection

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    1. A key theme of our MMW course is the tension between the realities of modernity—such as gender, class, and racial inequality—and the ideals of modernity, as expressed in the French Revolutionary motto "liberty, equality, fraternity.” Elaborate on this tension by providing examples from two assign…

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    1. A key theme of our MMW course is the tension between the realities of modernity—such as gender, class, and racial inequality—and the ideals of modernity, as expressed in the French Revolutionary motto "liberty, equality, fraternity.” Elaborate on this tension by providing examples from two assigned plays, the _Course Reade_ r, and at least one lecture. Your examples should be substantial, demonstrate full engagement with the course material, and include proper MLA citations. Your response must draw from more than one week of course content **.** Finally, do not forget to include a Works Cited page at the end of your assignment.

    2. At least four of our assigned plays (e.g., _A Doll’s House, The Seagull, M. Butterfly, Rhinoceros, A Raisin in the Sun_) use animals (e.g., a seagull) or objects (e.g., a raisin) as symbolic expressions of human life in the modern condition. Discuss the differences and similarities between two assigned plays that extensively use animals or objects as symbolic mediums. Use the _Course Reader_ and at least one lecture as references in your answer. Your selected plays should be different from those used in the previous question. Your examples must be substantial, demonstrate full engagement with the course material, and include proper MLA citations. Your response should incorporate material from more than one week of course content. Finally, do not forget to include a Works Cited page at the end of your assignment.

    – [\
    \
    ReflectionMMW.pdf](https://www.sweetstudy.com/files/reflectionmmw-pdf "ReflectionMMW.pdf")
    – [\
    \
    CopyofMMW122ReflectionGradingSpecificationsFormWi25.pdf](https://www.sweetstudy.com/files/copyofmmw122reflectiongradingspecificationsformwi25-pdf "CopyofMMW122ReflectionGradingSpecificationsFormWi25.pdf")

    – 2 years ago

  • [Solved] English Week 11

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    **Unit 11 Discussion** Review the PowerPoint on Critical Thinking (it is attached below for your convenience). In your initial post address the following: – What role does critical thinking play in your writing process? Use specifics from the PowerPoint to help support your points. (USLO 11.1) – Dis…

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    Assignment Prompt & Instructions

    **Unit 11 Discussion**

    Review the PowerPoint on Critical Thinking (it is attached below for your convenience).

    In your initial post address the following:

    – What role does critical thinking play in your writing process? Use specifics from the PowerPoint to help support your points. (USLO 11.1)
    – Discuss your p-writeing experiences in this class. What has been most helpful to you in learning how to plan and construct an esesay? (USLO 11.2)

    Your initial response should have your informed input and should be at least **100 words**. Your input should be in your own words, demonstrating your understanding and comprehension of the topic. When referring to the Critical Thinking PowerPoint, remember to include an in-text citation and reference in APA 7th-edition format. See examples below.

    – [\
    \
    CriticalThinkingFundamentalsPowerPoint-1.pptx](https://www.sweetstudy.com/files/criticalthinkingfundamentalspowerpoint-1-pptx "CriticalThinkingFundamentalsPowerPoint-1.pptx")

    – 2 years ago

  • [Solved] English Homework

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    In-depth study guidance and analytical solution covering English Homework.

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    Assignment Prompt & Instructions

    Full assignment prompt and step-by-step guidance are included in the downloadable solution file.

  • [Solved] Abstract/Reference

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    Critical Analysis Document on Climate Change Only the Abstract /References document is needed this week 19 March, 2025 For this assignment, you will write a 2000-word research using proper APA format and in argumentative mode. The abstract should be 100-250 words and the body ( excluding the cover p…

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    Critical Analysis Document on Climate Change

    Only the Abstract /References document is needed this week 19 March, 2025

    For this assignment, you will write a 2000-word research using proper APA format and in argumentative mode. The abstract should be 100-250 words and the body ( excluding the cover page and reference page) of the should be 1900 words. Be sure to use the template for a research p that you created in Week One, and provide direct support from your sources. At least eight sources are required.

    The Critical Analysis P options address material from your textbook. For your main response, in a Word document, create a reference page for the three sources from the research you've already conducted that you will use for the Critical Analysis P. Remember that margins and indentions are important. See the following example and be sure to alphabetize your entries. When you submit the reference page, be sure to attach it as a document. In the body of the text box, provide an indication as to how easy the references were to create, and identify any challenges or questions you may have about creating reference entries. (150-200 words required)

    – 2 years ago

  • [Solved] Donne, poems & meditations

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    Topic: Donne, poems & meditations (pp. 258-263, 441-446, 573-589) Instructions: a 1-page, single-spaced thesis on the topic of a text of your choosing. The assignment should follow standard organization, with a heading in the top left corner, paragraphs, topic sentences, transitions, and quotations …

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    Topic: Donne, poems & meditations (pp. 258-263, 441-446, 573-589)

    Instructions: a 1-page, single-spaced thesis on the topic of a text of your choosing. The assignment should follow standard organization, with a heading in the top left corner, paragraphs, topic sentences, transitions, and quotations from the text. you need to make an original argument and reinforce that argument with close readings from the text. To be eligible for an “A,” your assignment needs to have the essentials of a thesis-driven argument, solid organization, concise and lucid writing, and zero plot summary. Make sure the thesis statement is in the first paragraph in the beginning.

    – 2 years ago

  • [Solved] manager

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    – 2 years ago **files (3)** – ## Assignment2.pdf – hprice1.xlsx – RandomNumbers.xlsx **## Assignment2.pdf** Assignment 2 ECO 562 Due: Sunday 3/23 11:59pm on D2L [5 Points] 1. Suppose that you are interested in determining the effect of adding a bedroom on the price of your house with sales data (…

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    – 2 years ago

    **files (3)**

    – ## Assignment2.pdf

    – hprice1.xlsx
    – RandomNumbers.xlsx

    **## Assignment2.pdf**

    Assignment 2

    ECO 562

    Due: Sunday 3/23 11:59pm on D2L

    [5 Points]

    1. Suppose that you are interested in determining the effect of adding a bedroom on the
    price of your house with sales data (Hprice.xlsx). Using GPT 4o/4.5, es mate the naïve
    regression [provide the link to full chat below]:

    price = 𝑚 ∗ bedrooms + 𝑏 + �̃� (1)

    Link:

    a. [0.5 Points] Using GPT 4o/4.5, test for heteroscedas city and linearity of the
    regression (1). If there are viola ons to either assump on, fix it in the chat provided
    above. If there are no issues, proceed to part b.

    b. [0.5 Points] What is the interpreta on of the es mated values of 𝑚 and 𝑏, including
    the p-values? [No AI allowed]

    c. [0.5 Points] Why did we test for viola ons of OLS before interpre ng the es mated
    coefficients 𝑚 and 𝑏? [No AI allowed]

    d. [0.5 Points] What does it mean for 𝑚 to be unbiased? [No AI allowed]

    e. [0.5 Points] Why might the square footage of the house be an omi ed variable from
    the regression (1)? In other words, if the only objec ve is to determine the effect of
    adding one more bedroom on the price of the house, why would the square footage
    need to be included in the regression for the es mates to be unbiased? [No AI
    allowed]

    Using GPT 4o/4.5, es mate the regression below [es mate should be in the same chat as the
    link above]:

    price = 𝑚 ∗ bedrooms + 𝑚 ∗ square footage + 𝑏 + 𝑒 (2)

    f. [0.5 Points] Using GPT 4o/4.5, test for heteroscedas city and linearity of the
    regression (2). If there are viola ons to either assump on, fix it in the chat link
    provided above. If there are no issues, proceed to part g.

    g. [0.5 Points] What is the interpreta on of the es mates 𝑚 and 𝑚 , including the p-
    values? [No AI Allowed]

    h. [0.5 Points] How does 𝑚 and 𝑚 compare? Why does including square footage in the

    regression affect the marginal effect of the number of bedrooms
    posi vely/nega vely? [No AI Allowed]

    2. To demonstrate the weakness of measure 𝑅 as a measure of goodness-of-fit in the
    context of regression analysis, consider Random Numbers.xlsx.

    ChatGPT Link:

    a. [0.25 Points] Using GPT 4o/4.5, create a correla on table and matrix of Y and X1,
    X2,…, X50. What is the intrinsic rela onship between Y and the 50 X’s? [Hint: the
    spreadsheet is called ‘Random Numbers’]

    b. [0.25 Points] Ask GPT 4o/4.5 to es mate the regressions (copy and paste the
    quoted below into the chat):

    “Es mate the regressions

    Y=m1*X1+b+e

    Y=m1*X1+ m2*X2+b+e

    Y=m1*X1+ m2*X2+ m3*X3+b+e

    …

    Y=m1*X1+ m2*X2+ m3*X3+…+ m50*X50+b+e

    For each regression, collected the r-squared. Plot the es mated r-square as a func on of
    the number of uncorrelated independent variables.”

    Describe the plot created with this prompt.

    c. [0.5 Points] How does this exercise relate to the concept of a Kitchen Sink
    Regression?

    **hprice1.xlsx**

    This file is too large to display. [View in new window](https://www.sweetstudy.com/files/hprice1-xlsx)

    **RandomNumbers.xlsx**

    This file is too large to display. [View in new window](https://www.sweetstudy.com/files/randomnumbers-xlsx)

    **RandomNumbers.xlsx**

    This file is too large to display. [View in new window](https://www.sweetstudy.com/files/randomnumbers-xlsx)

    **## Assignment2.pdf**

    Assignment 2

    ECO 562

    Due: Sunday 3/23 11:59pm on D2L

    [5 Points]

    1. Suppose that you are interested in determining the effect of adding a bedroom on the
    price of your house with sales data (Hprice.xlsx). Using GPT 4o/4.5, es mate the naïve
    regression [provide the link to full chat below]:

    price = 𝑚 ∗ bedrooms + 𝑏 + �̃� (1)

    Link:

    a. [0.5 Points] Using GPT 4o/4.5, test for heteroscedas city and linearity of the
    regression (1). If there are viola ons to either assump on, fix it in the chat provided
    above. If there are no issues, proceed to part b.

    b. [0.5 Points] What is the interpreta on of the es mated values of 𝑚 and 𝑏, including
    the p-values? [No AI allowed]

    c. [0.5 Points] Why did we test for viola ons of OLS before interpre ng the es mated
    coefficients 𝑚 and 𝑏? [No AI allowed]

    d. [0.5 Points] What does it mean for 𝑚 to be unbiased? [No AI allowed]

    e. [0.5 Points] Why might the square footage of the house be an omi ed variable from
    the regression (1)? In other words, if the only objec ve is to determine the effect of
    adding one more bedroom on the price of the house, why would the square footage
    need to be included in the regression for the es mates to be unbiased? [No AI
    allowed]

    Using GPT 4o/4.5, es mate the regression below [es mate should be in the same chat as the
    link above]:

    price = 𝑚 ∗ bedrooms + 𝑚 ∗ square footage + 𝑏 + 𝑒 (2)

    f. [0.5 Points] Using GPT 4o/4.5, test for heteroscedas city and linearity of the
    regression (2). If there are viola ons to either assump on, fix it in the chat link
    provided above. If there are no issues, proceed to part g.

    g. [0.5 Points] What is the interpreta on of the es mates 𝑚 and 𝑚 , including the p-
    values? [No AI Allowed]

    h. [0.5 Points] How does 𝑚 and 𝑚 compare? Why does including square footage in the

    regression affect the marginal effect of the number of bedrooms
    posi vely/nega vely? [No AI Allowed]

    2. To demonstrate the weakness of measure 𝑅 as a measure of goodness-of-fit in the
    context of regression analysis, consider Random Numbers.xlsx.

    ChatGPT Link:

    a. [0.25 Points] Using GPT 4o/4.5, create a correla on table and matrix of Y and X1,
    X2,…, X50. What is the intrinsic rela onship between Y and the 50 X’s? [Hint: the
    spreadsheet is called ‘Random Numbers’]

    b. [0.25 Points] Ask GPT 4o/4.5 to es mate the regressions (copy and paste the
    quoted below into the chat):

    “Es mate the regressions

    Y=m1*X1+b+e

    Y=m1*X1+ m2*X2+b+e

    Y=m1*X1+ m2*X2+ m3*X3+b+e

    …

    Y=m1*X1+ m2*X2+ m3*X3+…+ m50*X50+b+e

    For each regression, collected the r-squared. Plot the es mated r-square as a func on of
    the number of uncorrelated independent variables.”

    Describe the plot created with this prompt.

    c. [0.5 Points] How does this exercise relate to the concept of a Kitchen Sink
    Regression?

    **hprice1.xlsx**

    This file is too large to display. [View in new window](https://www.sweetstudy.com/files/hprice1-xlsx)

    **RandomNumbers.xlsx**

    This file is too large to display. [View in new window](https://www.sweetstudy.com/files/randomnumbers-xlsx)

    **## Assignment2.pdf**

    Assignment 2

    ECO 562

    Due: Sunday 3/23 11:59pm on D2L

    [5 Points]

    1. Suppose that you are interested in determining the effect of adding a bedroom on the
    price of your house with sales data (Hprice.xlsx). Using GPT 4o/4.5, es mate the naïve
    regression [provide the link to full chat below]:

    price = 𝑚 ∗ bedrooms + 𝑏 + �̃� (1)

    Link:

    a. [0.5 Points] Using GPT 4o/4.5, test for heteroscedas city and linearity of the
    regression (1). If there are viola ons to either assump on, fix it in the chat provided
    above. If there are no issues, proceed to part b.

    b. [0.5 Points] What is the interpreta on of the es mated values of 𝑚 and 𝑏, including
    the p-values? [No AI allowed]

    c. [0.5 Points] Why did we test for viola ons of OLS before interpre ng the es mated
    coefficients 𝑚 and 𝑏? [No AI allowed]

    d. [0.5 Points] What does it mean for 𝑚 to be unbiased? [No AI allowed]

    e. [0.5 Points] Why might the square footage of the house be an omi ed variable from
    the regression (1)? In other words, if the only objec ve is to determine the effect of
    adding one more bedroom on the price of the house, why would the square footage
    need to be included in the regression for the es mates to be unbiased? [No AI
    allowed]

    Using GPT 4o/4.5, es mate the regression below [es mate should be in the same chat as the
    link above]:

    price = 𝑚 ∗ bedrooms + 𝑚 ∗ square footage + 𝑏 + 𝑒 (2)

    f. [0.5 Points] Using GPT 4o/4.5, test for heteroscedas city and linearity of the
    regression (2). If there are viola ons to either assump on, fix it in the chat link
    provided above. If there are no issues, proceed to part g.

    g. [0.5 Points] What is the interpreta on of the es mates 𝑚 and 𝑚 , including the p-
    values? [No AI Allowed]

    h. [0.5 Points] How does 𝑚 and 𝑚 compare? Why does including square footage in the

    regression affect the marginal effect of the number of bedrooms
    posi vely/nega vely? [No AI Allowed]

    2. To demonstrate the weakness of measure 𝑅 as a measure of goodness-of-fit in the
    context of regression analysis, consider Random Numbers.xlsx.

    ChatGPT Link:

    a. [0.25 Points] Using GPT 4o/4.5, create a correla on table and matrix of Y and X1,
    X2,…, X50. What is the intrinsic rela onship between Y and the 50 X’s? [Hint: the
    spreadsheet is called ‘Random Numbers’]

    b. [0.25 Points] Ask GPT 4o/4.5 to es mate the regressions (copy and paste the
    quoted below into the chat):

    “Es mate the regressions

    Y=m1*X1+b+e

    Y=m1*X1+ m2*X2+b+e

    Y=m1*X1+ m2*X2+ m3*X3+b+e

    …

    Y=m1*X1+ m2*X2+ m3*X3+…+ m50*X50+b+e

    For each regression, collected the r-squared. Plot the es mated r-square as a func on of
    the number of uncorrelated independent variables.”

    Describe the plot created with this prompt.

    c. [0.5 Points] How does this exercise relate to the concept of a Kitchen Sink
    Regression?

    **hprice1.xlsx**

    This file is too large to display. [View in new window](https://www.sweetstudy.com/files/hprice1-xlsx)

    **RandomNumbers.xlsx**

    This file is too large to display. [View in new window](https://www.sweetstudy.com/files/randomnumbers-xlsx)