This

**preview**shows page 1. to view the full**4 pages of the document.**We presented an introduction to game theory by describing how to solve two-

person, zero-sum games. In these games, the two players end up with the sum of

the gain (loss) to one player and the loss (gain) to the other player always equal

to zero. We described the steps that can be used to determine whether a two-

person, zero-sum game results in an optimal pure strategy. If a pure strategy is

optimal, a saddle point determines the value of the game. If an optimal pure

strategy does not exist for a two-person, zero-sum 2 2 game, we showed how to

identify an optimal mixed strategy. With a mixed strategy, each player uses

probability to select a strategy for each play of the game. We showed how

dominance could be used to reduce the size of mixed strategy games. If the

elimination of dominated strategies can reduce a larger game to a 2 2 game, an

algebraic solution procedure can be used to find a solution. If the game cannot be

reduced to a 2 2 game, a linear programming model is needed to determine the

optimal mixed strategy solution.

Chapter 6 Time Series Analysis and Forecasting

an introduction to basic methods of time series analysis and forecasting. We first

showed that the underlying pattern in the time series can often be identified by

constructing a time series plot. Several types of data patterns can be

distinguished, including a horizontal pattern, a trend pattern, and a seasonal

pattern. The forecasting methods we have discussed are based on which of these

patterns are present in the time series.

We also discussed that the accuracy of the method is an important factor in

determining which forecasting method to use. We considered three measures of

forecast accuracy: mean absolute error (MAE), mean squared error (MSE), and

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