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Lecture11_print

# Lecture11_print - Chapter 8 Inference on Proportions...

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Unformatted text preview: Chapter 8: Inference on Proportions Readings: Sections 8.1-8.2 1 Introduction • There is NO SPSS work in this chapter. We will do everything by hand. • In previous chapters (5, 6, 7) we looked at confidence intervals and hypothesis testing for problems involving means, where we have a quantitative variable. • For problems involving counts and proportions, we have a categorical (“Which?” or “Do you?” or “Yes or no?”) variable. Example 1 : a. Did you vote in the last election? The response would be either a “Yes” or a “No”. The variable is categorical, the response is the value the variable takes on for each unit/person. If I did a survey of this class, I could accumulate the count of “Yes” responses and describe this count as a proportion of the total. b. What academic year are you in at Purdue? The response would be either “Fresh- man”, “Sophomore”, “Junior”, or “Senior”. Again, I could accumulate the count of each and describe each as a proportion of the total. In both cases we are interested in estimating the unknown proportion, p , from a population. The statistic, ˆ p (sample proportion) estimates the population parameter p . Population and Sample proportions • In statistical sampling we often want to estimate the proportion, p , of “successes” in a population. “Success” is when the categorical variable takes on one particular value. We normally call whatever characteristic we are studying a “success.” – Population proportion : p = Count of successes in population Size of population – Sample proportion...
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Lecture11_print - Chapter 8 Inference on Proportions...

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