Quantitative Models in Marketing Research by Philip Hans Franses

By Philip Hans Franses

This e-book provides an important and virtually appropriate quantitative versions for advertising study. each one version features a demonstration of the mechanics of the version, empirical research, actual global examples, and an interpretation of effects and findings. The reader will how you can observe the ideas, in addition to comprehend the newest methodological advancements within the educational literature. scholars and practitioners with differing numerical abilities are guided throughout the publication, even though a data of simple numerical recommendations is believed.

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This chapter is not at all intended to give a detailed account of econometric methods and econometric analysis. Much more detail can, for example, be found in Greene (2000), Verbeek (2000) and Wooldridge (2000). In fact, this chapter mainly aims to set some notation and to highlight some important topics in econometric modeling. In later chapters we will frequently make use of these concepts. 1 The standard Linear Regression model In empirical marketing research one often aims to correlate a random variable Yt with one (or more) explanatory variables such as xt , where 29 30 Quantitative models in marketing research the index t denotes that these variables are measured over time, that is, t ¼ 1; 2; .

Obviously, the average amount donated by those who did not respond is zero. In chapter 7 we aim to correlate the censored variable with observed characteristics of the individuals concerning their past donating behavior. These variables are usually summarized under the headings Recency, Frequency and 3,000 No response No. 47 Percentage responded mailings No. 36 Notes: a Average values of the explanatory variables in the full sample. b Average values of the explanatory variables for no response and response observations, respectively.

In more than 25% of the weeks, this brand was on display, while in less than 10% of the weeks there was a coupon promotion. In only about 6% of the weeks, these promotions were held simultaneously. In chapter 3, we will examine whether or not these variables have any explanatory power for log sales while using a standard Linear Regression model. 2 A binomial variable Another frequently encountered type of dependent variable in marketing research is a variable that takes only two values. 65 Notes: a Percentage of weeks in which the brand was on display only.

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