Quantitative Analysis – Regression Analysis

Quantitative Analysis – Regression Analysis

 Recommend solutions to business problems using quantitative analysis.

Minimum submission requirements for this Competency Assessment:

  • Completion of Part 1 (the Certify sections in the lessons associated with the bold criteria in the rubric below).
  • Completion of Part 2 (Dropbox Assignment).

If work submitted for this Competency Assessment does not meet the minimum submission requirements, it will be returned without being scored.

Part 1: 

  • You will complete the Certify section in Certify Labs in the Hawkes learning software associated with bold criteria in the rubric.
  • For all lessons, a score of 80% or higher in the Certify section will complete your certification!
  • It is suggested (not required) that you email your instructor when Part 1 is complete.

Begin Part 1 of the Competency Assessment by clicking on the Hawkes Learning link located on the left navigation menu.

Part 2:

  • Show your work and explain your process for determining the solution for each of these problems on a word document with the solution given below the problem.
  • If Excel was used, please indicate that as well on the word document.
  • A word document and/or the Excel Workbook (if used) should be submitted to the Dropbox with labels on the worksheets to indicate which problem is being evaluated.
  • All answers should be clearly indicated.
  • Written explanation, reasoning, and rationale should use complete sentences.

A time series model is a forecasting technique that attempts to predict the future values of a variable by using only historical data on that one variable. Here are some examples of variables you can use to forecast. You may use a different source other than the ones listed (be sure to reference the website). There are many other variables you can use, as long as you have values that are recorded at successive intervals of time.

 

See example and starter video in LiveBinder.

Once you have historical data, address the following:

  1. Apply quantitative forecasting methods in time-series modeling.
    1. State the variable you are forecasting.
    2. Collect data for any time horizon (daily, monthly, yearly). Select at least 8 data values.
    3. Compute moving average and weighted moving average in a time-series model.
      1. Use the Excel Workbooks for this module to forecast the next period’s value using moving average, and weighted moving average (see video in LiveBinder).
      2. Copy/paste the results of each method into your word document.
  • Be sure to state the number of periods used in the moving average method and the weights used in the weighted moving average.  Clearly state the “next period” prediction for each method.
  1. Determine which of the two forecasts should be chosen and give the rationale for the decision.
  1. Identify variables for a regression model.
    1. Determine which variable from the time series forecast would be an appropriate dependent variable (Y) and tell why.
    2. Determine which variable from the time series forecast would be an appropriate independent variable (X) and tell why.
  2. Develop a simple linear regression model.
    1. Use the regression function found in Data Analysis located in Microsoft Excel to determine the linear regression model.
    2. Based upon the values given, what is the valid dependent variable range?

Submitting Course Assessment Part 2:

When you are ready to submit your Course Assessment Part 2, click on the Course Assessment Dropbox and complete the steps below:

  • Click the link that says Add Attachments.
  • Click on the Upload
  • Click the Add Attachments
  • Locate your Course Assessment and click on Open.
  • To view your graded work, come back to the Dropbox or go to the Gradebook after your instructor has evaluated it. Click the Dropbox to access it.
  • Make sure that you save a copy of your submitted Assignment.

If work submitted for this Competency Assessment does not meet the minimum submission requirements, it will be returned without being scored.

 

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