3/28/18. Estimation. Focus Points. Focus Points. Estimating p in the Binomial Distribution. Estimating p in the Binomial Distribution. Section 7.

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Which side of a cheetah has the most spots? Estimation The outside. 7 Section 7.3 Estimating p in the Binomial Distribution Boy, I m tired. I ve been up since the quack of dawn.

Focus Points Compute the sample size to be used for estimating a proportion p when we have no estimate for p. Interpret poll results.

1 Which side of a cheetah has the most spots? Estimation The outside. 7 Section 7.3 Estimating p in the Binomial Distribution Boy, I m tired. I ve been up since the quack of dawn. Focus Points Compute the maximal length of error for proportions using a given level of confidence. Compute confidence intervals for p and interpret the results. Focus Points Compute the sample size to be used for estimating a proportion p when we have no estimate for p. Interpret poll results. Compute the sample size to be used for estimating a proportion p when we have an estimate for p. The binomial distribution is completely determined by the number of trials n and the probability p of success on a single trial. For most experiments, the number of trials is chosen in advance. Then the distribution is completely determined by p. In this section, we will consider the problem of estimating p under the assumption that n has already been selected. We are employing what are called large-sample methods. We will assume that the normal curve is a good approximation to the binomial distribution, and when necessary, we will use sample estimates for the standard deviation. Empirical studies have shown that these methods are quite good, provided both np > 5 and nq > 5, where q = 1 p 1

Let r be the number of successes out of n trials in a binomial experiment. We will take the sample proportion of successes " = $ as % our point estimate for p, the population proportion of successes.

It turns out that, for large samples, the distribution of values is well approximated by a normal curve with Then, in terms of the language of probability, mean!

Recall that z c is the number such that an area equal to c under the standard normal curve falls between z c and z c.

2 Let r be the number of successes out of n trials in a binomial experiment. We will take the sample proportion of successes " = $ as % our point estimate for p, the population proportion of successes. To compute the bounds for the margin of error, we need some information about the distribution of " = $ % values for different samples of the same size n. It turns out that, for large samples, the distribution of values is well approximated by a normal curve with Then, in terms of the language of probability, mean! = # and standard error $ = #%/' (17) Since the distribution of # = ) is approximately normal, * we use features of the standard normal distribution to find the bounds for the difference # #. Recall that z c is the number such that an area equal to c under the standard normal curve falls between z c and z c. Equation (17) says that the chance is c that the numerical difference between " and " is between With the c confidence level, our estimate " differs from p by no more than To find a c confidence interval for p, we will use E in place of the expression in Equation (17). Then we get P( " < % % < ") = c (19) Some algebraic manipulation produces the mathematically equivalent statement P( % " < % < % + ") = c (20) Therefore, the interval from " $ to " + $ is the c confidence interval for p that we wanted to find. There is one technical difficulty in computing the c confidence interval for p. The expression requires that we know the values of p and q. In most situations, we will not know the actual values of p or q, so we will use our point estimates " " and ' = 1 " 1 " to estimate E. 2

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All 800 students were exposed to the flu, and 600 of them did not get the flu. Let p represent the probability that the shot will be successful for any single student selected at random from the entire population of 20,000. Let q be the probability that the shot is not successful. What is the number of trials n? What is the value of r? Since each of the 800 students receiving the shot may be thought of as a trial, then n = 800, and r = 600 is the number of successful trials. What are the point estimates for p and q? We estimate p by the sample point estimate We estimate q by = 1 = = 0.25 Example 6(c) Confidence Interval for p Check Requirements Would it seem that the number of trials is large enough to justify a normal approximation to the binomial? Since n = 800, p» 0.75, and q» 0.25, then np» (800)(0.75) = 600 > 5 and np» (800)(0.25) = 200 > 5 A normal approximation is certainly justified. Example 6(d) Confidence Interval for p Find a 99% confidence interval for p. 1-PropZInt x : 600 n : 800 C-Level : 0.99 Calculate ( , ) Interpretation We are 99% confident that the probability a flu shot will be effective for a student selected at random is between and

Interpreting Results from a Poll Suppose you want to specify the maximal margin of error in advance for a confidence interval for p at a given confidence level c. What sample size do you need?

4 Interpreting Results from a Poll Newspapers frequently report the results of an opinion poll. In articles that give more information, a statement about the margin of error accompanies the poll results. Interpreting Results from a Poll Suppose you want to specify the maximal margin of error in advance for a confidence interval for p at a given confidence level c. What sample size do you need? The answer depends on whether or not you have a preliminary estimate for the population probability of success p in a binomial distribution. A company is in the business of selling wholesale popcorn to grocery stores. The company buys directly from farmers. A buyer for the company is examining a large amount of corn from a certain farmer. Before the purchase is made, the buyer wants to estimate p, the probability that a kernel will pop. Suppose a random sample of n kernels is taken and r of these kernels pop. 4

5 The buyer wants to be 95% sure that the point estimate " = $ for p will be in error either way by less than % In this case, we use Equation (22) with z 0.95 = 1.96 (see Table 7-2) and E = a. If no preliminary study is made to estimate p, how large a sample should the buyer use? Some Levels of Confidence and Their Corresponding Critical Values Table 7-2 (b) A preliminary study showed that p was approximately If the buyer uses the results of the preliminary study, how large a sample should he use? In this case, we use Equation (21) with p» Again, from Table 7-2, z 0.95 = 1.96, and from the problem, E = The buyer would need a sample of n = 9604 kernels. The sample size should be at least n = This sample is less than half the sample size necessary without the preliminary study. 5

Estimation. Focus Points 10/11/2011. Estimating p in the Binomial Distribution. Section 7.3

Estimation. Focus Points 10/11/2011. Estimating p in the Binomial Distribution. Section 7.3 Estimation 7 Copyright Cengage Learning. All rights reserved. Section 7.3 Estimating p in the Binomial Distribution Copyright Cengage Learning. All rights reserved. Focus Points Compute the maximal length