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Linear Statistical Models

Linear Statistical Models. Answering the questions you have asked. Are these ratings really different? What is the influence of a policy variable (price, advertising, and etc.) on a market outcome (market shares, sales, overall satisfaction)?

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Linear Statistical Models

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  1. Linear Statistical Models

  2. Answering the questions you have asked. • Are these ratings really different? • What is the influence of a policy variable (price, advertising, and etc.) on a market outcome (market shares, sales, overall satisfaction)? • What is the part worth of a particular feature in a whole bundle that makes a product?

  3. Are these ratings really different? • Comparing one mean to a null hypothesis • One Sample t-test • Comparing two samples means to each other • Two Sample t-test • What about comparing six mean ratings • Why not 6*5/2 = 15 paired t-tests?

  4. Degrees of freedom • We are asking if a series of 6 mean ratings are really all the same (the variations around the mean are just noise). • We shouldn’t run 15 paired t-tests with only 5 independent pieces of information. A complex pattern of redundancy will make the results deceptive.

  5. Testing Hypotheses about What Facilities are Most Needed • Null hypothesis: • The different facilities are all equally needed • Alternative hypothesis: • Some kind are rated as more needed than others • Test statistic • F =Variance Between Groups/Variance Within • = Signal / Noise • Decision Rule: Set Alpha at .05

  6. Once you have found a significant difference overall • Post Hoc Comparisons • You can do as many paired t-tests as you want without worrying about redundancy. • You can use advanced test (Range Tests) to see which ratings stand out.

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