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Copyright 2014 Institute for Marketing Productivity - All rights reserved Limitations:Common Measurement Confounds.

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Presentation on theme: "Copyright 2014 Institute for Marketing Productivity - All rights reserved Limitations:Common Measurement Confounds."— Presentation transcript:

1 Copyright 2014 Institute for Marketing Productivity - All rights reserved Limitations:Common Measurement Confounds

2 The Gist Pie = (Price – Production Cost) * Sales – Marketing Cost Optimality Implies, That the Marginal Revenue of Marketing Equal the Marginal Cost. 2

3 Overarching Motivation Where Managerial Decisions are supported by models and metrics, biased measurements lead to sub- optimal decisions 3

4 Roadmap Recitation: Econometrics Experiment One: Co-linearity Experiment Two: Omitted Variables 4

5 Recitation: Econometrics The application of mathematical models, economic theory, and statistical methods and inference to recover empirical relationships from data. 5

6 Experiment One: Observed variable correlation 6

7 Causes of Multi-co-linearity Managerial Strategy Directly Causal Indirectly Causal Customer Behavior Directly Causal Indirectly Causal 7

8 Co-linearity Bias: Managerial Example 8 Price Advertising Cost

9 Co-linearity Bias: Behavioral Example 9 Go to Workbook

10 Experiment Two: Omitted Variable Bias 10

11 Causes of Omitted Variable Bias Managerial Strategy Customer Behavior Forces of the Universe 11

12 Omitted Variable Bias: General Example 12

13 An Quick Detour Common Measurement Model Functional Forms: Lineary = x*slope + intercept Log-Linln(y) = x*slope + intercept Lin-Logy = ln(x)*slope + intercept Log-log ln(y) = ln(x)*slope + intercept 13

14 Omitted Variable Bias: Behavioral Simultaneity Example 14

15 Omitted Variable Bias: Managerial Simultaneity Example 15 Go to Workbook

16 Experiment with Parameters Open the Section Three Workbook Change highlighted parameters and run regressions of Log Sales on Logged Mix Variables to see how it effects estimates Summarize Findings Report to Class 16

17 SUMMARY Motivated concern about measurement confounds Demonstrated co-linearity bias Demonstrated omitted variables bias Experiment with the two confounds


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