Lecture30 - ECO220Y Lecture 30 Single Population Estimation...

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ECO220Y Lecture 30 CO220 ectu e 30 Single Population Estimation & Hypothesis Testing (2) Migiwa Tanaka Reading: 12.3 1
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utline Outline Statistical Inference of single population Inference of μ when σ is known Estimation Lecture 21, 22 Hypothesis Testing Inference of μ when σ is unknown Lecture 24, 25,26, 27,28 Estimation Hypothesis Testing Lecture 23 Lecture 29 Inference of population proportion Estimation t 3 0 Hypothesis Testing Lecture 30 2
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opulation Proportion Population Proportion Data– Nominal or Categorical Data Possible values: Binary: Yes/No, Defective/Not Defective, Success/Failure Categorical: Red/Blue/Yellow, High/Middle/Low Percentage of population that choose one of the categories Poll Results: Support for a particular candidate/bills Marketing Research Results: Prefer healthy alternative. 3
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xample: Taste for Automobile Features Example: Taste for Automobile Features Your company is considering introduction of a new car model, at highly fuel fficient that is highly fuel-efficient. The marketing department wants to know if this feature is preciated enough among potential buyers. f ore than 0 appreciated enough among potential buyers. (If more than 30 percent of buyers consider fuel efficiency is the most important feature in deciding their car.) It surveyed consumers who plan to buy a car within next 12 months. uestion: Which f the following features do you find the most Question: Which of the following features do you find the most important when you make car purchase decision? Horse Power Color/Design Fuel Efficiency Size 4
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Results of Survey: stimating Population proportion Estimating Population proportion Car Features # of respondents % of the respondents Horse power 15 Fuel Efficiency 65
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This note was uploaded on 03/03/2011 for the course ECON 220 taught by Professor Tanaka during the Spring '11 term at University of Toronto- Toronto.

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Lecture30 - ECO220Y Lecture 30 Single Population Estimation...

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