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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".

Definitions:

Value

The importance, worth, or usefulness of something to someone, often measured in terms of monetary, social, or strategic significance.

S.A.V.E. Framework

A marketing concept that focuses on Solution, Access, Value, and Education to better meet customer needs and differentiate from competition.

Marketing Mix

The combination of product, price, promotion, and placement of what a company is offering.

Designer Cookware

High-end kitchenware products that are not only functional but also have a distinctive style or design appeal.

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