Mar. 22 Statistic for the day: Percent of Americans 18 or older who believe Martha Stewart’s sentence should include jail time: 53% Assignment: Read Chapter 18 Exercises p329: 1, 2, 5, 6, 8, 10 Source: gallup.com These slides were created by Tom Hettmansperger and in some cases modified by David Hunter

Do you believe Martha Stewart got a fair trial? Do you believe Martha Stewart’s sentence should include jail time? Gallup Poll Sample percentages: categorical variableYesNo No opinion Fair trial 66%27%7% Jail time 53%40%7%

The Gallup Poll was based on 1005 telephone interviews. Based on the sample of 1005 we estimate that 53% of the population of millions believes that Martha Stewart’s sentence should include jail time. If we take a new sample of 1005 we will get a new sample percentage. It will generally not be exactly 53%. If we take lots of samples of 1005 we will get lots of sample percentages. Next we look at the histogram for the percentages.

200 percentages based on 200 samples of 1005 each. Mean = 53% (or.53) Standard deviation = (57% − 50%) / 4 = 1.75% (or.0175)

How do we measure and assess the uncertainty in the sample percentage?

So in our example, if the sample size is 1600, then the MARGIN OF ERROR is: Or 3.2 % And we report 53% + 3.2% We defined the margin of error to be 2 standard deviations. We estimated the standard deviation from the histogram to be This nearly agrees since 2x.0175 =.035. Pretty close!

Summary: Gallup Poll We have a simple random sample from We have a simple random sample from the population of telephone owners. The sample size used was The sample size used was We find the percentage from our sample. We find the percentage from our sample. The MARGIN OF ERROR is 1 divided by The MARGIN OF ERROR is 1 divided by the square root of the sample size. For 1005 the MARGIN OF ERROR is.032. For 1005 the MARGIN OF ERROR is.032.  Hence we report: PERCENTAGE The margin of error does not depend on the The margin of error does not depend on the population size, only on the sample size!

To refine the idea of standard deviation (for later use in a refined margin of error). We also want to relate this to the normal curve. In the past we: 1.used a sample to get a sample proportion 2.used a formula to get the margin of error 3.reported the sample proportion + the margin of error Now we want a formula for the standard deviation. Then we will use the new standard deviation formula to calculate a new margin of error. Goals

Formula for estimating the standard deviation of a sample proportion (don’t need histogram): If we happen to know the true population proportion we use it instead of the sample proportion.

Summary: 1.We take a sample of 1005 phone interviews 2.We estimate the percent of the American public that thinks that Martha Stewart should go to jail: 53% 3.To assess the uncertainty in the 53% sample figure, we think of a normal curve of percentages centered at.530 with standard deviation of So the normal curve has 95% of its distribution between.530 – 2x.016 and x.016 or Estimate 53% (.53) with 50% to 56% (.50 to.56) the reasonable interval of values.

What to expect from sample proportions Facts: fingerprints may be influenced by prenatal hormones. Most people have more ridges on right hand than left. People who have more on the left hand are said to have leftward asymmetry. Women are more likely to have this trait than men. The proportion of all men who have this trait is about 15%

In a study of 186 heterosexual and 66 homosexual men 26 (14%) heterosexual men showed the trait and 20 (30%) homosexual men showed the trait (Reference: Hall, J. A. Y. and Kimura, D. "Dermatoglyphic Asymmetry and Sexual Orientation in Men", Behavioral Neuroscience, Vol. 108, No. 6, , Dec 94. ) Is it unusual to observe a sample of 66 men and observe a sample proportion of 30%? We now know what the distribution of sample proportions based on a sample of 66 should look like. We will suppose that the true proportion in the population of men is 15%. Standard deviation

The sample proportion for homosexual men (30%) is too large to come from the expected distribution of sample proportions.

Sample means: measurement variables Data from stat 100 survey. Sample size 237. Mean value is pounds. Standard deviation is about (240 – 100)/4 = 35 Suppose we want to estimate the mean weight at PSU

What is the uncertainty in the mean? We need a margin of error for the mean. Suppose we take another sample of 237. What will the mean be? Will it be again? Probably not. Consider what happens if we take 1000 samples each of size 237 and compute 1000 means.

Standard deviation is about (157 – 148)/4 = 9/4 = 2.25

Formula for estimating the standard deviation of the sample mean (don’t need histogram) Just like in the case of proportions, we would like to have a simple formula to find the standard deviation of the mean without having to resample a lot of times. Suppose we have the standard deviation of the original sample. Then the standard deviation of the sample mean is:

So in our example of weights: The standard deviation of the sample is about 35. Hence by our formula: Standard deviation of the mean is 35 divided by the square root of 237: 35/15.4 = 2.3 (Recall we estimated it to be 2.25) So the margin of error of the sample mean is 2x2.3 = 4.6 Report or to 157.1

Example: SAT scores Suppose nationally we know that the SAT has a mean of 425 points and a standard deviation of 120 points. Draw by hand a picture of what you expect the distribution of sample means based on samples of size 100 to look like. Sample means have a normal distribution mean 425 standard deviation 120/10 = 12 So draw a bell shaped curve, centered at 425, with 95% of the bell between 425 – 24 = 401 and = 449

A sample of 100 SATs with a mean of 460 would be very unusual. A sample of 100 with a mean of 440 would not be unusual.