statistics exam 3

Suppose that an alien lands on earth, notices that there are two different sexes of the human species, and sets out to estimate the proportion of human beings who are female. Suppose that the alien chooses the members of the 1998 U.S. House of Representatives as its sample of human beings. In 1998, there were 53 representatives who were female and 382 representatives who were male. Create a 95% confidence interval for the population proportion of human beings who are female. Note: Your sample size is the total number of members in the 1998 US House of Representatives. .09 to .15 .104 to .136 52.97 to 53.03 52.88 to 53.12

.09 to .15

Suppose a 95% confidence interval for the average amount of weight loss on a diet program for males is between 13.4 and 18.3 pounds. These results were based on a sample of 42 male participants who were deemed to be overweight at the start of the 4-month study. What is the standard error of the sample mean? 15.85 1.225 4.9 2.45

1.225

Weight is a measure that tends to be normally distributed. Suppose the mean weight of all women at a large university is 135 pounds, with a standard deviation of 12 pounds. If you took a random sample of 36 university women, there would be a 68% chance that the sample mean (x-bar) weight would be between: 133 and 137 pounds 123 pounds and 147 pounds 131 and 139 pounds 125 and 145 pounds 119 and 1511 pounds

133 and 137 pounds

The height of American adult women is distributed almost exactly as a normal distribution. The mean height of adult American women is 63.5 inches with a standard deviation of 2.5 inches. Imagine that all possible random samples of size 25(n = 25) are taken from the population of American adult women's heights. The means from each sample would then be graphed to form the sampling distribution of sample means. The mean of this sampling distribution is _____ and the standard deviation of this sampling distribution is ________. 63.5; 2.5 25.4; 2.5 63.5; .1 63.5; .5 25.4; .1

63.5; .5

A meteorologist predicts a 40% chance of rain in London and a 80% chance of rain in Chicago. What is the percent chance that it will rain in either London or Chicago. 32% 88% 60% 120% 94%

88%

Which of the following is a correct interpretation of a 90% confidence interval? Once a specific sample has been selected, the probability its resulting confidence interval contains the true population value is 90%. All of the above statements are true. 90% of the random samples you could select would result in intervals that contain the true population value. 90% of the population values should be close to our sample results.

90% of the random samples you could select would result in intervals that contain the true population value.

Which of the following statements is not true regarding a 95% confidence interval for the mean of a population? In 95% of all samples, the true population mean will be within 2 standard errors of the sample mean. If you add and subtract two standard errors to and from the sample mean, in 95% of all cases, you will have captured the true population mean. 95% of the population values will lie within 2 standard errors of the sample mean. In 95% of all samples, the sample mean will fall within 2 standard errors of the true population mean.

95% of the population values will lie within 2 standard errors of the sample mean.

A meteorologist predicts a 40% chance of rain in London and a 80% chance of rain in Chicago. What is the most likely outcome? It rains in at least one city. It rains only in London. It rains only in Chicago. it rains in London and Chicago.

It rains in at least one city

Sampling distributions for the mean follow many rules. Which of the rules listed below is false? Sampling distributions are centered over the true population parameter. Sampling distributions are composed of statistics from many, many samples. Two of the options listed are false. Three of the options listed are false. Sampling distributions get wider (I.e. have greater variability) with increasing sample size All of the options listed are false. Sampling distributions of size n > 30 are typically bell-shaped.

Sampling distributions get wider (I.e. have greater variability) with increasing sample size

Which of the following is an example of the conjunction fallacy? -The probability of two events occurring together is thought to be higher than the probability of either event occurring alone. -A rare event is perceived to have a higher chance of happening to someone if the event is good (say winning the Powerball Lottery) vs. a rare event that is not good (say getting struck by lightening). -None of the above. -Two events happening in conjunction is thought to be impossible if they are mutually exclusive.

The probability of two events occurring together is thought to be higher than the probability of either event occurring alone.

Which of the following describes the "specificity" of a test for a certain disease? The proportion of people who correctly test negative when they don't have the disease. All of the above. The probability that you are likely to have a specific disease, without any knowledge of your test results. The proportion of people who correctly test positive when they actually have the disease.

The proportion of people who correctly test negative when they don't have the disease.

If numerous large random samples or repetitions of the same size are taken from a population, the proportions from the various samples will have what approximate mean? The true population proportion. 1 The true population mean 95% because most of them will be within 2 standard deviations of the true population value.

The true population proportion.

Imagine that we have a population that is negatively (left)skewed. This population has a mean of 112 and a standard deviation of 16. Using a simulation program, Jake simulated drawing 1000 samples of size 2 from the population. He then plotted the means for each of the samples that he drew. Alex simulated drawing 1000 samples of size 30, and he also plotted the means for each of the samples that he drew. Since neither Jake nor Alex took all possible samples of a given size from the skewed population, the distributions they created are not true sampling distributions, though they are big enough that you can assume that are rules for sampling distributions of the mean apply. Would you expect the shape of Jake's sampling distribution of the mean to differ from the shape of Alex's sampling distribution of the means? No, both Jake and Alex would create sampling distributions that are negatively skewed. Yes, Alex's sampling distribution would be negatively skewed, but Jake's would be normal in shape. Yes, Jake's sampling distribution would be negatively skewed, but Alex's would be normal in shape. No, both Jake and Alex would create sampling distributions that are normal in shape.

Yes, Jake's sampling distribution would be negatively skewed, but Alex's would be normal in shape.

specificity

ability to detect the absense of the disease in people who dont have it

Sensitivity

ability to detect the disease in people who actually have the disease

Which of the following lottery combinations (6 numbers from 1-30, no repeats allowed) is the least likely to come up as a winning ticket? 1, 2, 3, 4, 5, 6 2, 10, 23, 27, 30, 11 All of the above are equally unlikely. 11, 19, 14, 12, 17, 15

all

A 2018 sample of 130 college students randomly selected from a university indicated that 91 were sexually active.The researcher calculated a 95% confidence interval for the data to be .62 to .78. If the researcher had created a 99.7% confidence interval instead, the interval would have been: Narrower The same width Wider No answer text provided.

wider