{"id":6754,"date":"2020-11-15T10:41:31","date_gmt":"2020-11-15T10:41:31","guid":{"rendered":"http:\/\/onlineclassesguru.com\/?p=6754"},"modified":"2020-11-15T10:41:31","modified_gmt":"2020-11-15T10:41:31","slug":"read-problem-6-in-chapter-6-of-your-textbook","status":"publish","type":"post","link":"https:\/\/onlineclassesguru.com\/index.php\/2020\/11\/15\/read-problem-6-in-chapter-6-of-your-textbook\/","title":{"rendered":"Read Problem 6 in Chapter 6 of your textbook"},"content":{"rendered":"<style type=\"text\/css\"><\/style><p>Respond to\u2026<\/p>\n<p>Read Problem 6 in Chapter 6 of your textbook. Calculate and answer parts a through d. Include all calculations and spreadsheets in your post. Explain why the moving average method was used instead of another forecasting method. What might be another forecasting method that could prove to be just as useful?<br \/>\nThe figures below indicate the number of mergers that took place in the savings and loan industry over a 12-year period.<br \/>\nYear<br \/>\nMergers<br \/>\n2000<br \/>\n46<br \/>\n2001<br \/>\n46<br \/>\n2002<br \/>\n62<br \/>\n2003<br \/>\n45<br \/>\n2004<br \/>\n64<br \/>\n2005<br \/>\n61<br \/>\n2006<br \/>\n83<br \/>\n2007<br \/>\n123<br \/>\n2008<br \/>\n97<br \/>\n2009<br \/>\n186<br \/>\n2010<br \/>\n225<br \/>\n2011<br \/>\n240<br \/>\nCalculate a 5-year moving average to forecast the number of mergers for 2012.<br \/>\nf_2012= ((123+97+186+225+240))\/5=174.2<br \/>\nThis method is used to help offset years where there may have been unusual circumstances leading to an increase or decrease in total mergers (Vonderembse &amp; White, 2013).<br \/>\nUse the moving average technique to determine the forecast for 2005 to 2011. Calculate measurement error using MSE and MAD.<br \/>\nMean Squared Error (MSE)<br \/>\nYear<br \/>\nMergers<br \/>\nForecast<br \/>\nError<br \/>\nSq Error<br \/>\n2005<br \/>\n61<br \/>\n52.6<br \/>\n8.4<br \/>\n70.56<br \/>\n2006<br \/>\n83<br \/>\n55.6<br \/>\n27.4<br \/>\n750.76<br \/>\n2007<br \/>\n123<br \/>\n63<br \/>\n60<br \/>\n3600<br \/>\n2008<br \/>\n97<br \/>\n75.2<br \/>\n21.8<br \/>\n475.24<br \/>\n2009<br \/>\n186<br \/>\n85.6<br \/>\n100.4<br \/>\n10080.16<br \/>\n2010<br \/>\n225<br \/>\n110<br \/>\n115<br \/>\n13225<br \/>\n2011<br \/>\n240<br \/>\n142.8<br \/>\n97.2<br \/>\n9447.84<br \/>\nTotal<br \/>\n37649.56<br \/>\nAverage<br \/>\n5378.5<br \/>\nMean Absolute Dev (MAD)<br \/>\nYear<br \/>\nMergers<br \/>\nForecast<br \/>\nError<br \/>\nSigns Removed<br \/>\n2005<br \/>\n61<br \/>\n52.6<br \/>\n8.4<br \/>\n8.4<br \/>\n2006<br \/>\n83<br \/>\n55.6<br \/>\n27.4<br \/>\n27.4<br \/>\n2007<br \/>\n123<br \/>\n63<br \/>\n60<br \/>\n60<br \/>\n2008<br \/>\n97<br \/>\n75.2<br \/>\n21.8<br \/>\n21.8<br \/>\n2009<br \/>\n186<br \/>\n85.6<br \/>\n100.4<br \/>\n100.4<br \/>\n2010<br \/>\n225<br \/>\n110<br \/>\n115<br \/>\n115<br \/>\n2011<br \/>\n240<br \/>\n142.8<br \/>\n97.2<br \/>\n97.2<br \/>\nTotal<br \/>\n430.2<br \/>\nAverage<br \/>\n61.5<br \/>\nMergers minus forecast equals total error. Total error squared is simply that. For the MAD calculation, all figures were positive, so no need to change signs.<\/p>\n<p>Calculate a 5-year weighted moving average to forecast the number of mergers for 2012. Use weights of 0.10, 0.15, 0.20, 0.25, and 0.30, with the most recent year weighted being the largest.<\/p>\n<p>Year<br \/>\nMergers<br \/>\nWeight<br \/>\n2007<br \/>\n123<br \/>\n0.10<br \/>\n12.30<br \/>\n2008<br \/>\n97<br \/>\n0.15<br \/>\n14.55<br \/>\n2009<br \/>\n186<br \/>\n0.20<br \/>\n37.20<br \/>\n2010<br \/>\n225<br \/>\n0.25<br \/>\n56.25<br \/>\n2011<br \/>\n240<br \/>\n0.30<br \/>\n72.00<br \/>\nAVG<br \/>\n192.30<br \/>\nThis method is used to show the most recent year with the highest weighting.<\/p>\n<p>Use regression analysis to forecast the number of mergers in 2012.<\/p>\n<p>X<br \/>\nY<br \/>\nXY<br \/>\nX^2<br \/>\nY^2<br \/>\n1<br \/>\n123<br \/>\n123<br \/>\n1<br \/>\n15129<br \/>\n2<br \/>\n97<br \/>\n194<br \/>\n4<br \/>\n37636<br \/>\n3<br \/>\n186<br \/>\n558<br \/>\n9<br \/>\n311364<br \/>\n4<br \/>\n225<br \/>\n900<br \/>\n16<br \/>\n810000<br \/>\n5<br \/>\n240<br \/>\n1200<br \/>\n25<br \/>\n1440000<br \/>\n15<br \/>\n871<br \/>\n2975<br \/>\n55<br \/>\n2614129<br \/>\nb=<br \/>\n1810<br \/>\n160<br \/>\n11.3<br \/>\na=<br \/>\n4.5<br \/>\nY\u0082\u0091=<br \/>\n72.4<br \/>\nb=\u201d\u0089n\u0088\u0091\u201d\u008b\u201d\u008bXY\u0088\u0092\u0088\u0091\u201d\u008bX\/\u0088\u0091\u201d\u008b\u201d\u008bYn\u0088\u0091\u201d\u008b\u201d\u008bX2\u2033\u0089\u0088\u0092(\u0088\u0091\u201d\u008bX)2<br \/>\na=\u0088\u0091\u201d\u008b\u201d\u008bY\/n\u0088\u0092b\u0088\u0091\u201d\u008b\u201d\u008bX\/n<br \/>\nYe=a+bX<br \/>\nReference<br \/>\nVonderembse, M. A., &amp; White, G. P. (2013). Operations management [Electronic version]. Retrieved from https:\/\/content.ashford.edu\/<\/p>\n<p>Respond to\u2026<\/p>\n<p>The figures below indicate the number of mergers that took place in the savings and loan industry over a 12-year period.<br \/>\nYear<br \/>\nMergers<br \/>\n2000<br \/>\n46<br \/>\n2001<br \/>\n46<br \/>\n2002<br \/>\n62<br \/>\n2003<br \/>\n45<br \/>\n2004<br \/>\n64<br \/>\n2005<br \/>\n61<br \/>\n2006<br \/>\n83<br \/>\n2007<br \/>\n123<br \/>\n2008<br \/>\n97<br \/>\n2009<br \/>\n186<br \/>\n2010<br \/>\n225<br \/>\n2011<br \/>\n240<br \/>\nA) Calculate a 5-year moving average to forecast the number of mergers for 2012.<br \/>\n(123+97+186+225+240)\/5 = 174.2<br \/>\nB) Use the moving average technique to determine the forecast for 2005 to 2011. Calculate measurement error using MSE and MAD.<br \/>\nMSE\/MAD<br \/>\nYear<br \/>\nActual Mergers<br \/>\nForecasted Mergers<br \/>\nError<br \/>\nSquared Error<br \/>\n2005<br \/>\n61<br \/>\n(46 +46+62+45+64)\/5 = 53<br \/>\n8<br \/>\n(8*8) = 64<br \/>\n2006<br \/>\n83<br \/>\n(46 +62+45+64+61)\/5 = 56<br \/>\n27<br \/>\n(27*27) = 729<br \/>\n2007<br \/>\n123<br \/>\n(62+45+64+61+83)\/5 = 63<br \/>\n60<br \/>\n(60*60) = 3600<br \/>\n2008<br \/>\n97<br \/>\n(45+64+61+83+123)\/5 = 75<br \/>\n22<br \/>\n(22*22) = 484<br \/>\n2009<br \/>\n186<br \/>\n(64+61+83+123+97)\/5 = 86<br \/>\n100<br \/>\n(100*100) = 10000<br \/>\n2010<br \/>\n225<br \/>\n(61+83+123+97+186)\/5 = 110<br \/>\n115<br \/>\n(115*115) = 13225<br \/>\n2011<br \/>\n240<br \/>\n(83+123+97+186+225)\/5 = 143<br \/>\n97<br \/>\n(97*97) = 9409<br \/>\nTotal<br \/>\n429<br \/>\n53597<br \/>\nMSE (53597\/7) = 7656.7<br \/>\nMAD (429\/7) = 61.3<br \/>\nC) Calculate a 5-year weighted moving average to forecast the number of mergers for 2012. Use weights of 0.10, 0.15, 0.20, 0.25, and 0.30, with the most recent year weighted being the largest.<br \/>\n(0.30 * 240) + (0.25 * 225) + (0.20 * 186) + (0.15 * 97) + (0.10 * 123) = 192<br \/>\nD) Use regression analysis to forecast the number of mergers in 2012.<br \/>\nYear<br \/>\nX<br \/>\nMergers<br \/>\nXY<br \/>\nX^2<br \/>\nY^2<br \/>\n2000<br \/>\n1<br \/>\n46<br \/>\n(1*46) = 46<br \/>\n(1^2) = 1<br \/>\n(46^2) = 2116<br \/>\n2001<br \/>\n2<br \/>\n46<br \/>\n(2*46) = 92<br \/>\n(4^2) = 4<br \/>\n(46^2) = 2116<br \/>\n2002<br \/>\n3<br \/>\n62<br \/>\n(3*62) = 186<br \/>\n(3^2) = 9<br \/>\n(62^2) = 3844<br \/>\n2003<br \/>\n4<br \/>\n45<br \/>\n(4*45) = 180<br \/>\n(4^2) = 16<br \/>\n(45^2) = 2025<br \/>\n2004<br \/>\n5<br \/>\n64<br \/>\n(5*64) = 320<br \/>\n(5^2) = 25<br \/>\n(64^2) = 4096<br \/>\n2005<br \/>\n6<br \/>\n61<br \/>\n(6*61) = 366<br \/>\n(6^2) = 36<br \/>\n(61^2) = 3721<br \/>\n2006<br \/>\n7<br \/>\n83<br \/>\n(7*83) = 581<br \/>\n(7^2) = 49<br \/>\n(83^2) = 6889<br \/>\n2007<br \/>\n8<br \/>\n123<br \/>\n(8*123) = 984<br \/>\n(8^2) = 64<br \/>\n(123^2) = 15129<br \/>\n2008<br \/>\n9<br \/>\n97<br \/>\n(9*97) = 873<br \/>\n(9^2) = 81<br \/>\n(97^2) = 9409<br \/>\n2009<br \/>\n10<br \/>\n186<br \/>\n(10*186) = 1860<br \/>\n(10^2) = 100<br \/>\n(186^2) = 34596<br \/>\n2010<br \/>\n11<br \/>\n225<br \/>\n(11*225) = 2475<br \/>\n(11^2) = 121<br \/>\n(225^2) = 50625<br \/>\n2011<br \/>\n12<br \/>\n240<br \/>\n(12*240) = 2880<br \/>\n(12^2) = 144<br \/>\n(240^2) = 57600<br \/>\nTotals<br \/>\n78<br \/>\n1278<br \/>\n10843<br \/>\n650<br \/>\n192166<br \/>\nb = 12 * 10,843 \u2013 78 * 1278 \/ 12 * 650 \u2013 78^2 = 13116 \u2013 99,684 \/ 7800 \u2013 6084 = 30,432 \/ 1716 = 17.73<br \/>\na = 1278 \/ 12 \u2013 17.7 * 78 \/ 12 = 106.5 \u2013 115.245 = -8.75<br \/>\nr = 12 * 10,843 \u2013 78 * 1,278 \/ \u0088\u009a 12 * 650 -78^2 12 * 198,514 \u2013 1,278^2 = 130,16 -99,684 \/ \u0088\u009a 7,800 \u2013 6084 2,382,168 \u2013 1,633,284 = 1,258,084,944 = 30,432 \/ 35848.08 = .84892<br \/>\nExplain why the moving average method was used instead of another forecasting method. What might be another forecasting method that could prove to be just as useful?<br \/>\nThe moving average was used instead of another forecasting method because it provides the most reasoned prediction according to Vonderembse and White (2013). This type of forecasting method also helps with smoothing out the peaks and valleys with all the fluctuations within the data. With utilizing forecasting testing, utilizing models such as exponential smoothing could be considered useful in considering qualitative factors. Exponential smoothing is a procedure for continualy revising an estimate to include more recent data, and is based upon averaging past values (Vonderembse &amp; White, 2013). Altogether forecasting by the use of testing, is a great way to help predict the future.<br \/>\nReferences<br \/>\nVonderembse, M. A., &amp; White, G. P. (2013). Operations management[Electronic version]. Retrieved from https:\/\/content.ashford.edu\/<\/p>\n<p><center><a href=\"http:\/\/onlineclassesguru.com\/orders\/ordernow\"><img decoding=\"async\" src=\"https:\/\/encrypted-tbn0.gstatic.com\/images?q=tbn:ANd9GcTyj99p60XCLyLk1htB7-1neRt8-2QdnenNlQ&usqp=CAU\"target=\"_http:\/\/onlineclassesguru.com\/orders\/ordernow\"\/><\/center><p>","protected":false},"excerpt":{"rendered":"<p>Respond to\u2026 Read Problem 6 in Chapter 6 of your textbook. Calculate and answer parts a through d. Include all calculations and spreadsheets in your post. Explain why the moving average method was used instead of another forecasting method. What might be another forecasting method that could prove to be just as useful? 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