invest_3ed.pdf

# Many statisticians now view the plus four or adjusted

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Many statisticians now view the Plus Four or Adjusted Wald method as the best in terms of coverage rate and width (Binomial intervals, because of the discreteness, are often wider than they “need” to be ), as well as simplicity. Whatever procedure is used to determine the confidence interval, you interpret a (valid) interval the same way as the interval of plausible values for the parameter. For example, we are 95% confident that the underlying probability of death within 30 days of a heart transplant operation at St. George’s Hospital is between 0.16 and 0. 24; where by “95% confident , we mean that if we were to use this

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Chance/Rossman, 2015 ISCAM III Investigation 1.11 93 procedure to construct intervals from thousands of representative samples, roughly 95% of those intervals will succeed in capturing the actual (but unknown) value of the parameter of interest. Keep in Mind: The main things you should focus on in your study of confidence intervals are: x What parameter is the interval estimating (in context)? x What are the effects of sample size, confidence level, and p ˆ on the width and midpoint interval? x What do we me an by “confidence”? x What are the effects (if any!) of sample size, confidence level, and p ˆ on the coverage rate of the method? x Why might one confidence interval method be preferred over another? Also note how to use R, Minitab, or the Theory-Based Inference applet to perform these calculations. You should NOT use the Simulating Confidence Intervals applet to construct a confidence interval for a particular sample of data. Practice Problem 1.11 Return to your exploration using the Simulating Confidence Intervals applet. Assume that S = 0.667 is the probability that a kissing couple leans to the right. (a) From the third pull-down menu select the Wilson interval method. Evaluate the performance of this interval method, clearly explaining your steps. (b) Starting with ˆ (1 ) / p z n S S S ± ± , use the quadratic formula to solve for S and verify the formula for the Score interval. (c) Show that with 95% confidence the Wilson formula simplifies to approximately the Plus Four method.
Chance/Rossman, 2015 ISCAM III Investigation 1.12 94 SECTION 3: SAMPLING FROM A FINITE POPULATION So far, we have treated each set of data that we have investigated as a sample from an ongoing process with an underlying process probability. It is more common to consider sample data as coming from a larger, but finite, population (e.g., a sample of adults from among all Americans aged 18 years and older). So what we now want to consider are (i) how to select a sample from the population to allow us to generalize our sample observations back to that larger population and (ii) what statistical techniques we need to make such inferences. You will see that though many of the analysis procedures are the same, there are a few more issues to consider as well.

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