i need explanation how to get question 4 and 5 AND enrichment question. i need graphs and how to create them. thank you!

That's question 3

4. Develop multiplicative seasonal indices for the linear trend model developed in question 3. Use these indices to adjust predictions from the linear trend model from question 3 above for seasonal effects. Plot the predictions from this model on the graph with the original data. How well does this technique fit the data? Make forecasts for the next 12 months of 2007 using this technique. S. Which forecasting method of those that you tried do you have the most confidence for making accurate forecasts for 2007? Use MAPE (mean absolute percent error) as your criterion to Justify your decision Enrichment (5 pts): Use Optimization (and Solver in Excel) to find the optimal smoothing constantin problem 2 above (by minimizing the Mean Squared Error or MSE). - Case Study2fa20- Forecasti. Lupe Chavez File GO 1 Home Insert Design Layout References Mailings Review View Help Tell me 8 11 Paste Calibri (Body) BIU-abx x x A. : AA Font A AAD. Styles Editing lipboard Paragraph 2AL24 5 Styles 1997 MONTH IAN FEB MAR 1998 101 2001 104 100 99 2000 132 109 101 96 1999 88 110 129 113 114 169 82 84 125 118 121 140 APR MAY JUN 88 89 108 85 124 141 2002 111 123 121 139 119 156 154 136 105 132 123 164 2003 127 129 132 108 115 149 155 129 117 166 152 173 JUL 2004 2005 2006 119 147 145 147 146 149 164 133 148 135 148 148 124 148 168 191 201 159 184 137 156 166 149 151 159 138 166 175 175 170 195 188 194 111 140 179 145 140 120 129 118 134 109 131 139 141 201 152 138 137 138 144 148 178 109 101 106 102 78 111 AUG SEP OCT 119 121 111 101 112 120 115 116 128 NOV DEC 139 From the time series given in the above table, you will make a forecast for the 12 months of the next year, 2007 Managerial Report is due on ... Wednesday, 17 Feb 21 (40 pts) 1. Produce a time series plot of the data. From this graph, do you see a pattern? Can you see any Seasonality in the data? You may have to perform time series plots by years. 2. Use exponential smoothing to fit the data. Select an appropriate constant based on the variation you see in the data. Comment on the appropriateness of exponential smoothing on this data set. Plot the predictions from this model on the graph with the original data. How well does this technique it the data? Make forecasts for each month in 2007 Remember, for smoothing methods, you can only provide the forecast for the next period If you use ALL 10 years of data, that means you are providing a forecast for only lan 2007 I. I still need the forecast for the other 11 months. And you cannot do a forecast using these forecasts You will have to figure out how to use all 10 years of data to determine 12 Individual, monthly forecasts 3. Use regression to build a lineartrend model. Comment on the goodness of fit of the model to the data for how well does explain the variance in the data. Pot the prediction from the model on the graph with the original data . To me what you want to do A A A EST AaBbCD AaBbcc AaBI ** * * A. A. - - - Normal No Space 1 Heading Font Paragraph Styles &2 3. Use regression to build a linear trend model. Comment on the goodness of fit of this model to the data (or how well does explain the variance is the data). Plot the predictions from this model on the graph with the original data. Regression as a lineartrend model. Linear regression is a data plot that graphs the linear relationship Ed between an independent and a dependent variable. It is typically used to visually show the strength of the relationship and the dispersion of results-all for the purpose of explaining the behavior of the dependent variable Running HendCase Study 2-Forecasting Ad: 03953673 Regression V. OR bestellen met w De Engin united States 4. Develop multiplicative seasonal indices for the linear trend model developed in question 3. Use these indices to adjust predictions from the linear trend model from question 3 above for seasonal effects. Plot the predictions from this model on the graph with the original data. How well does this technique fit the data? Make forecasts for the next 12 months of 2007 using this technique. S. Which forecasting method of those that you tried do you have the most confidence for making accurate forecasts for 2007? Use MAPE (mean absolute percent error) as your criterion to Justify your decision Enrichment (5 pts): Use Optimization (and Solver in Excel) to find the optimal smoothing constantin problem 2 above (by minimizing the Mean Squared Error or MSE). - Case Study2fa20- Forecasti. Lupe Chavez File GO 1 Home Insert Design Layout References Mailings Review View Help Tell me 8 11 Paste Calibri (Body) BIU-abx x x A. : AA Font A AAD. Styles Editing lipboard Paragraph 2AL24 5 Styles 1997 MONTH IAN FEB MAR 1998 101 2001 104 100 99 2000 132 109 101 96 1999 88 110 129 113 114 169 82 84 125 118 121 140 APR MAY JUN 88 89 108 85 124 141 2002 111 123 121 139 119 156 154 136 105 132 123 164 2003 127 129 132 108 115 149 155 129 117 166 152 173 JUL 2004 2005 2006 119 147 145 147 146 149 164 133 148 135 148 148 124 148 168 191 201 159 184 137 156 166 149 151 159 138 166 175 175 170 195 188 194 111 140 179 145 140 120 129 118 134 109 131 139 141 201 152 138 137 138 144 148 178 109 101 106 102 78 111 AUG SEP OCT 119 121 111 101 112 120 115 116 128 NOV DEC 139 From the time series given in the above table, you will make a forecast for the 12 months of the next year, 2007 Managerial Report is due on ... Wednesday, 17 Feb 21 (40 pts) 1. Produce a time series plot of the data. From this graph, do you see a pattern? Can you see any Seasonality in the data? You may have to perform time series plots by years. 2. Use exponential smoothing to fit the data. Select an appropriate constant based on the variation you see in the data. Comment on the appropriateness of exponential smoothing on this data set. Plot the predictions from this model on the graph with the original data. How well does this technique it the data? Make forecasts for each month in 2007 Remember, for smoothing methods, you can only provide the forecast for the next period If you use ALL 10 years of data, that means you are providing a forecast for only lan 2007 I. I still need the forecast for the other 11 months. And you cannot do a forecast using these forecasts You will have to figure out how to use all 10 years of data to determine 12 Individual, monthly forecasts 3. Use regression to build a lineartrend model. Comment on the goodness of fit of the model to the data for how well does explain the variance in the data. Pot the prediction from the model on the graph with the original data . To me what you want to do A A A EST AaBbCD AaBbcc AaBI ** * * A. A. - - - Normal No Space 1 Heading Font Paragraph Styles &2 3. Use regression to build a linear trend model. Comment on the goodness of fit of this model to the data (or how well does explain the variance is the data). Plot the predictions from this model on the graph with the original data. Regression as a lineartrend model. Linear regression is a data plot that graphs the linear relationship Ed between an independent and a dependent variable. It is typically used to visually show the strength of the relationship and the dispersion of results-all for the purpose of explaining the behavior of the dependent variable Running HendCase Study 2-Forecasting Ad: 03953673 Regression V. OR bestellen met w De Engin united States