Titlebook: Quantitative Methods for Management; A Practical Approach Miguel Ángel Canela,Inés Alegre,Alberto Ibarra Textbook 2019 Springer Nature Swit

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Multiple Regressionhave been skipped, and the mathematics have been simplified to the indispensable. So, if you have already followed a Statistics course, you may find that the attention paid to the different aspects of the regression analysis differs from what you found there.

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Dummy Variablesd in most of the applications of regression analysis, since the samples on which we collect our data are typically partitioned into groups. In the example, we use a dummy variable to code gender, which allows us to include the comparison between genders in the analysis in an easy way.

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Interactionfect of an independent variable to change with the level of another independent variable. In the example, we use an interaction term to account for the effect of some variables on the efficiency of a truck to be different for different types of motor.

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Classification Modelsndent variables (.’s). The difference is that, in classification models, . is a categorical variable. This book only covers binary classification, in which . takes two values. The example of this chapter is a typical application, default prediction.

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Trend and Seasonalityd 2. The example uses monthly sales data of cold weather gear. The analysis is mostly graphical, based on a linear trend with multiplicative seasonals. This provides a simple model for describing the past behavior of the data and for forecasting the values for the next year.

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