Forcasting Methods

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.Currency price: XE ( Trading Economics ( home sales: National Association of Realtors ( tuition: National Center for Education Statistics ( temperature or precipitation: ( price: Yahoo Finance ( See example and starter video in LiveBinder.Once you have historical data, address the following:Apply quantitative forecasting methods in time-series modeling. State the variable you are forecasting.Collect data for any time horizon (daily, monthly, yearly). Select at least 8 data values.Compute moving average and weighted moving average in a time-series model.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).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.Determine which of the two forecasts should be chosen and give the rationale for the decision.Identify variables for a regression model.Determine which variable from the time series forecast would be an appropriate dependent variable (Y) and tell why.Determine which variable from the time series forecast would be an appropriate independent variable (X) and tell why.Develop a simple linear regression model.Use the regression function found in Data Analysis located in Microsoft Excel to determine the linear regression model.Based upon the values given, what is the valid dependent variable range? MM305M5 Competency Assessment RubricCriteriaMetNot yet met[Identify variables for a regression model.]Compute the correlation coefficient.[Develop a simple linear regression model.]Develop a multiple regression model.Use the linear regression model.Analyze the meaning of correlation.Identifying cross-sectional and time series data.[Compute moving average and weighted moving average in a time-series model.]Compute exponential smoothing in a time-series model.[Apply quantitative forecasting methods in time-series modeling.]Use linear time trend projections for forecasting.Evaluate forecasting methods.Total    The criterion statements in Bold are the minimum requirement to show competent performance on the course outcome; all Bold criteria must be met to pass this course outcome.   A “Met” mark for Bold criterion is awarded for certification in the associated lesson(s). The criterion statements in [Brackets] represent mastery achievement. A predefined number of mastery criteria must be achieved to earn an A grade, indicating mastery of the course outcome.  A “Met” mark for [Brackets] criterion is awarded by demonstrating competent performance in part 2 of the assessment. For the written portion, students are to submit a word document and/or Excel workbook(s) that clearly demonstrate the student is able to solve each problem appropriately.  All problems and work must be clearly labeled on the word document and/or Excel workbook(s) submitted.  Standard English with appropriate punctuation and grammar should be used. CLA and Grade Criteria ChartCriteriaCLA ScoreGradePoints Meets all bold criterion and at least 2 mastery criterion5A1,000 Meets at all bold criterion and less than 2 mastery criterion4B850 Meets 6 or 7 bold criterion3(not yet competent) F at term end0 Meets 4 or 5 of the bold criterion2(not yet competent) F at term end0 Meets 2 or 3 of the bold criterion 1(not yet competent) F at term end0 Meets fewer than 2 of the bold criterion 0(not yet competent) F at term end0 No work submitted.N/A(not yet competent) F at term end0 

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