Year 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Period 1 2 3 4 5 6 7 8 9 10 Sales 22456 20012 25800 24887 26100 32900 32203 33838 35488 37723
Year Period
Year
Year Period Sales
Year
Period Sales
Year

45 Year 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Period 1 2 3 4 5 6 7 8 9 10 Sales 22456 20012 25800 24887 26100 32900 32203 33838 35488 37723

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Paper instructions

Sales are on the rise and Jo wants to forecast what to budget for in 2017. To keep the calculations simple and understandable, Jo wants to use a simple linear regression model. The last ten years of data are provided. Year Period Sale ? 2007 1 22456 2008 2 20012 2009 3 25800 2010 4 24887 2011 5 26100 2012 6 32900 2013 7 32203 2014 8 33838 2015 9 35488 2016 10 37723 Refer to the Lecture Notes A. Does the Normal probability plot appear to be linear? B. What was the forecast for year 2017 (period 11)? C. If the data were seasonal, would this method work? What would be some of the difficulties? D. What types of businesses use forecasting? Explain. Import Excel file: Sales_Forecast.xlsx # Excel and not csv Input code: period <- Sales_Forecast $ Period # independent variable, period sales <- Sales_Forecast $ Sales # dependent variable, sales mod <- lm(sales ~ period, Sales_Forecast) # set object mod summary (mod) # show results # show the results plot (period, sales, xlab = "Period", ylab = "Sales") # plot the scatterplot abline(mod) # draws a regression line # calculate forecast - use mod to provide the coefficients # calculation is: Intercept + IV * period to be forecast. mod$coefficients 18651.694 + 1907.083*(11) # forecast for Period = 11 period.lm = lm(sales ~ period, Sales_Forecast) # begins the Normal probability plot period.stdres = rstandard(period.lm) qqnorm(period.stdres, ylab="Standardized Residuals", xlab="Normal Scores", main = "Normal Probability Plot") # Normal probability plot qqline(period.stdres) # draws a trend line through plot ATTACHMENT PREVIEW Download attachment .t { position: absolute; 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format("woff"); } Year Period Sales 2007 1 22456 2008 2 20012 2009 3 25800 2010 4 24887 2011 5 26100 2012 6 32900 2013 7 32203 2014 8 33838 2015 9 35488 2016 10 37723 var isIE = false; function load1(){ }

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