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Exhibit 18

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Exhibit 18.7.The following table shows the annual revenues (in millions of dollars)of a pharmaceutical company over the period 1990-2011. Exhibit 18.7.The following table shows the annual revenues (in millions of dollars)of a pharmaceutical company over the period 1990-2011. The autoregressive models of order 1 and 2, and ,were applied on the time series to make revenue forecasts.The relevant parts of Excel regression outputs are given below. Model AR(1): Model AR(2): Refer to Exhibit 18.7.(Use Regression in Data Analysis of Excel. )Compare the autoregressive models, and ,through the use of MSE and MAD.Hint.You may compute the errors by clicking Residuals ;to analyze instead of ,it suffices to click Constant is Zero . The autoregressive models of order 1 and 2, Exhibit 18.7.The following table shows the annual revenues (in millions of dollars)of a pharmaceutical company over the period 1990-2011. The autoregressive models of order 1 and 2, and ,were applied on the time series to make revenue forecasts.The relevant parts of Excel regression outputs are given below. Model AR(1): Model AR(2): Refer to Exhibit 18.7.(Use Regression in Data Analysis of Excel. )Compare the autoregressive models, and ,through the use of MSE and MAD.Hint.You may compute the errors by clicking Residuals ;to analyze instead of ,it suffices to click Constant is Zero . and Exhibit 18.7.The following table shows the annual revenues (in millions of dollars)of a pharmaceutical company over the period 1990-2011. The autoregressive models of order 1 and 2, and ,were applied on the time series to make revenue forecasts.The relevant parts of Excel regression outputs are given below. Model AR(1): Model AR(2): Refer to Exhibit 18.7.(Use Regression in Data Analysis of Excel. )Compare the autoregressive models, and ,through the use of MSE and MAD.Hint.You may compute the errors by clicking Residuals ;to analyze instead of ,it suffices to click Constant is Zero . ,were applied on the time series to make revenue forecasts.The relevant parts of Excel regression outputs are given below.
Model AR(1): Exhibit 18.7.The following table shows the annual revenues (in millions of dollars)of a pharmaceutical company over the period 1990-2011. The autoregressive models of order 1 and 2, and ,were applied on the time series to make revenue forecasts.The relevant parts of Excel regression outputs are given below. Model AR(1): Model AR(2): Refer to Exhibit 18.7.(Use Regression in Data Analysis of Excel. )Compare the autoregressive models, and ,through the use of MSE and MAD.Hint.You may compute the errors by clicking Residuals ;to analyze instead of ,it suffices to click Constant is Zero . Model AR(2): Exhibit 18.7.The following table shows the annual revenues (in millions of dollars)of a pharmaceutical company over the period 1990-2011. The autoregressive models of order 1 and 2, and ,were applied on the time series to make revenue forecasts.The relevant parts of Excel regression outputs are given below. Model AR(1): Model AR(2): Refer to Exhibit 18.7.(Use Regression in Data Analysis of Excel. )Compare the autoregressive models, and ,through the use of MSE and MAD.Hint.You may compute the errors by clicking Residuals ;to analyze instead of ,it suffices to click Constant is Zero . Refer to Exhibit 18.7.(Use Regression in Data Analysis of Excel. )Compare the autoregressive models, Exhibit 18.7.The following table shows the annual revenues (in millions of dollars)of a pharmaceutical company over the period 1990-2011. The autoregressive models of order 1 and 2, and ,were applied on the time series to make revenue forecasts.The relevant parts of Excel regression outputs are given below. Model AR(1): Model AR(2): Refer to Exhibit 18.7.(Use Regression in Data Analysis of Excel. )Compare the autoregressive models, and ,through the use of MSE and MAD.Hint.You may compute the errors by clicking Residuals ;to analyze instead of ,it suffices to click Constant is Zero . and Exhibit 18.7.The following table shows the annual revenues (in millions of dollars)of a pharmaceutical company over the period 1990-2011. The autoregressive models of order 1 and 2, and ,were applied on the time series to make revenue forecasts.The relevant parts of Excel regression outputs are given below. Model AR(1): Model AR(2): Refer to Exhibit 18.7.(Use Regression in Data Analysis of Excel. )Compare the autoregressive models, and ,through the use of MSE and MAD.Hint.You may compute the errors by clicking Residuals ;to analyze instead of ,it suffices to click Constant is Zero . ,through the use of MSE and MAD.Hint.You may compute the errors by clicking "Residuals";to analyze Exhibit 18.7.The following table shows the annual revenues (in millions of dollars)of a pharmaceutical company over the period 1990-2011. The autoregressive models of order 1 and 2, and ,were applied on the time series to make revenue forecasts.The relevant parts of Excel regression outputs are given below. Model AR(1): Model AR(2): Refer to Exhibit 18.7.(Use Regression in Data Analysis of Excel. )Compare the autoregressive models, and ,through the use of MSE and MAD.Hint.You may compute the errors by clicking Residuals ;to analyze instead of ,it suffices to click Constant is Zero . instead of Exhibit 18.7.The following table shows the annual revenues (in millions of dollars)of a pharmaceutical company over the period 1990-2011. The autoregressive models of order 1 and 2, and ,were applied on the time series to make revenue forecasts.The relevant parts of Excel regression outputs are given below. Model AR(1): Model AR(2): Refer to Exhibit 18.7.(Use Regression in Data Analysis of Excel. )Compare the autoregressive models, and ,through the use of MSE and MAD.Hint.You may compute the errors by clicking Residuals ;to analyze instead of ,it suffices to click Constant is Zero . ,it suffices to click "Constant is Zero".

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