Q&A Hero

Q: 48 I have a hunch that tomorrow is going to be a bad day. This is an example of the subjective probability.

Q: 47 Predicting the political party of the next president based on previous election patterns is an example of the relative frequency view of probability.

Q: 46 Dealing 5 cards from a standard deck is an example of sampling with replacement.

Q: 45 When observing a continuous variable, you can calculate the probability that an event falls in a certain interval; and when observing a discrete variable, you can calculate the probability of a specific outcome.

Q: 44 The probability of getting heads twice when flipping a coin 2 times is .50.

Q: 43 A bag of 100 hard candies included 30 butterscotch, 40 peppermint, 15 strawberry, 10 orange, and 5 banana. The probability that the first candy pulled out of the bag will be butterscotch or strawberry is .45.

Q: 42 The probability of rolling a 6 on the first roll with a standard die is independent of the probability of rolling a 6 on the second roll.

Q: 41 Two events are mutually exclusive if a) they cannot both happen at the same time. b) they cover all possibilities. c) one of them must happen. d) none of the above

Q: 59 In a normal distribution, indicate what percent of scores fall: a) between the mean and 1 standard deviation above the mean b) between plus and minus 2 standard deviations of the mean. c) 3 standard deviations above or below the mean.

Q: 58 At a neighboring university, the average salary is also $45,000 and the distribution is normal. If $47,000 has a z score of 1.5, what is the standard deviation?

Q: 57 If the salary of assistant professors in this university is normally distributed with a mean of $45,000 and a standard deviation of $1,500, what salary would have a z score of .97?

Q: 56 The basketball team lives in another dorm from those in the previous question. Their heights are normally distributed as well, with a mean height of 71 inches and a standard deviation of 2 inches. a) Draw their distribution on the same graph as students who lived in the first dorm (e.g., draw separate but overlapping distributions). b) What percent of students in the first dorm are at least as tall as the average basketball players? c) What percent of basketball players are taller than the average dorm resident?

Q: 55 Based on the previous data, we could conclude that 90% of the students are likely to fall between what heights?

Q: 54 Based on the height data in the previous question: a) What percent of residents are between 65 inches and 71 inches tall? b) What percent of residents are taller than 72 inches? c) What percent of residents are shorter than 72 inches?

Q: 53 The height of students in a dormitory is normally distributed with a mean of 68 inches and a standard deviation of 3 inches. Draw the distribution.

Q: 52 Using the distribution in the previous question, calculate z scores for: a) X = 11 b) X = 35 c) X = 71

Q: 51 Create a z distribution based on the following data. Explain the process. 10 20 20 30 30 30 40 40 40 40 50 50 50 60 60 70

Q: 50 The birth weight of healthy, full term infants in the United States is nearly normally distributed. The mean weight is 3,500 grams, and the standard deviation is 500 grams. a) What percent of healthy newborns will weigh more than 3,250 grams? b) What weights would 95% of all healthy newborns tend to fall between? c) What is the z score for an infant who weighs 2,750 grams?

Q: 49 In a normal distribution, the majority of scores fall beyond plus or minus one standard deviation from the mean.

Q: 48 The probability that a student will score between plus or minus one standard deviation from the mean on an exam, assuming the scores are normally distributed, is approximately 68%.

Q: 47 Suzie scored in the 95th percentile on the Math portion of the SAT. This means that she scored as high or higher than 95% of the other students who took the test.

Q: 46 Performing a linear transformation can make any distribution normal.

Q: 45 A z score refers to the number of standard deviations above or below the mode.

Q: 44 The area under a particular portion of the normal curve is equivalent to theprobability of falling within that portion of the distribution.

Q: 43 The standard normal distribution is a normal distribution with a mean of 0 and a standard deviation of 1.

Q: 42 Most statistical techniques are based on the assumption that the population of observations is not normal.

Q: 41 The normal distribution is bimodal and symmetric.

Q: 40 The normal distribution is often referred to as the bell curve.

Q: 39 In a normal distribution, about how much of the distribution lies within two (2) standard deviations of the mean? a) 33% of the distribution b) 50% of the distribution c) 66% of the distribution d) 95% of the distribution

Q: 38 A normal distribution a) has more than half of its data points to the left of the median. b) has more than half of its data points to the right of the mean. c) has 95% of its data points within one standard deviation of the mean. d) is symmetrical.

Q: 37 For a normal distribution a) all of the data points lie within one standard deviation from the mean. b) about 2/3 of the distribution lies within one standard deviation from the mean. c) about 95% of the distribution lies within two standard deviations from the mean. d) both b and c

Q: 36 The most common situation in statistical procedures is to assume that a) data are positively skewed. b) data are negatively skewed. c) data are normally distributed. d) it doesn't make any difference what the distribution of the data looks like.

Q: 35 An example of a linear transformation is a) converting heights from feet to meters. b) subtracting the value of the mean from each individual IQ score and dividing by the value of the standard deviation. c) both a and b d) none of the above

Q: 34 The difference between a normal distribution and a standard normal distribution is a) standard normal distributions are more symmetric. b) normal distributions are based on fewer scores. c) standard normal distributions always have a mean of 0 and a standard deviation of 1. d) there is no difference.

Q: 33 The advantage of using T-scores and standard scores isa) those scores provide a common form of reference to everyone using them.b) only negative numbers are used.c) the mean is always 10.d) scores of -1 and +1 are equal distances from the mean.

Q: 32 "Abscissa" is to _______ as "ordinate" is to _______. a) density; frequency b) frequency; density c) horizontal; vertical d) vertical; horizontal

Q: 31+ A test score of 84 was transformed into a standard score of -1.5. If the standard deviation of test scores was 4, what is the mean of the test scores?a) 78b) 80c) 90d) 88

Q: 30+ If the test scores on an art history exam were normally distributed with a mean of 76 and standard deviation of 6, we would expect a) most students scored around 70. b) no one scored 100 on the exam. c) almost equal numbers of students scored a 70 and an 82. d) both a and c

Q: 29+ The difference between a standard score of -1.0 and a standard score of 1.0 isa) the standard score 1.0 is farther from the mean than -1.0.b) the standard score -1.0 is farther from the mean than 1.0.c) the standard score 1.0 is above the mean while -1.0 is below the mean.d) the standard score -1.0 is above the mean while 1.0 is below the mean.

Q: 28 Which of the following is NOT always true of a normal distribution? a) It is symmetric. b) It has a mean of 0. c) It is unimodal. d) both a and b

Q: 27 Transforming a set of data to a new mean and standard deviation using a linear transformation a) alters the shape of the distribution. b) makes the scores harder to work with. c) is rarely permissible. d) is something we do frequently.

Q: 26 Stanine scores a) are badly skewed. b) have a mean of 5 and vary between 1 and 9. c) are always integers. d) both b and c

Q: 25+ When we transform scores to a distribution that has a mean of 50 and a standard deviation of 10, those scores are called a) z scores. b) t scores. c) T scores. d) stanine scores.

Q: 24 The difference between "probable limits" and "confidence limits" is that the probable limits a) focus on estimating where a particular score is likely to lie using a known population mean. b) estimate the kinds of means that we expect. c) try to set limits that have a .95 probability of containing the population mean. d) There is no difference.

Q: 23+ Assume that your class took an exam last week and the mean and standard deviation of the exam were 85 and 5, respectively. Your instructor told you that 30 percent of the students had a score of 90 or above. You would probably a) think that your instructor was out of her mind. b) decide that your score of 80 would probably fall in the failing range. c) conclude that the scores were not normally distributed. d) conclude that such a set of scores could not possibly happen.

Q: 22+ If we have data that have been sampled from a population that is normally distributed with a mean of 50 and a standard deviation of 10, we would expect that 95% of our observations would lie in the interval that is approximately a) 3070. b) 3550. c) 4555. d) 7090.

Q: 21 We are interested in what the text calls "probable limits" because a) we want to know whether a piece of data is unusual. b) we want to have a good idea what kinds of values to expect. c) we might want to know whether values below some specific value are improbable. d) all of the above

Q: 20+ The formula for calculating the 95% probable limits on an observation is a) ( > 1.96s) b) (s + 1.96) c) ( - 1.96s) d) (  1.96s)

Q: 19+ There are a few z scores that we use often that are worth remembering. The upper 50%, and 97.5 percent of a normal distribution are cut off by z scores of a) 1.0, and 1.64. b) 0.0, and 1.96. c) .50, and .975. d) plus and minus 1.96.

Q: 18 The text discussed setting "probable limits" on an observation. These limits are those which have a a) 50% chance of enclosing the value that the observation will have. b) 75% chance of enclosing the value that the observation will have. c) 80% chance of enclosing the value that the observation will have. d) 95% chance of enclosing the value that the observation will have.

Q: 17 If you are interested in identifying children who are highly aggressive, and you have a normally distributed scale that will do so, you will be particularly interested in a) scores on that scale that are substantially above the mean. b) scores on that scale that are substantially far from the mean. c) scores on that scale that are substantially below the mean. d) any extreme score.

Q: 16+ If we know that the probability for z > 1.5 is .067, then we can say that a) the probability of exceeding the mean by more than 1.5 standard deviations is .067. b) the probability of being more than 1.5 standard deviations away from the mean is .134. c) 86.6% of the scores are less than 1.5 standard deviations from the mean. d) all of the above

Q: 15+ The tables of the standard normal distribution contain only positive values of z. This is because a) the distribution is symmetric. b) z can take on only positive values. c) we aren"t interested in negative values of z. d) probabilities can never be negative.

Q: 14 If behavior problem scores are roughly normally distributed in the population, a sample of behavior problem scores will a) be normally distributed with any size sample. b) more closely resemble a normal distribution as the sample size increases. c) have a mean of 0 and a standard deviation of 1. d) be negatively skewed.

Q: 13 Which of the following is a good reason to convert data to z scores? a) We want to be able to estimate probabilities or proportions easily. b) We think that it is easier for people to work with round numbers. c) We want to make a skewed set of data into a normally distributed set of data. d) all of the above

Q: 12+ A linear transformation of data a) multiplies all scores by a constant and/or adds some constant to all scores. b) is illegal. c) drastically changes the shape of a distribution. d) causes the data to form a straight line.

Q: 11+ A z score of 1.25 represents an observation that is a) 1.25 standard deviation below the mean. b) 0.25 standard deviations above the mean of 1. c) 1.25 standard deviations above the mean. d) both b and c

Q: 10 The symbol p is commonly used to refer to a) any value for the observed variable. b) a value from a standard normal distribution. c) the probability for the occurrence of an observation. d) none of the above

Q: 9 The distribution that is normally distributed with a mean of 0 and a standard deviation of 1 is called a) the normal distribution. b) the standard normal distribution. c) the skewed normal distribution. d) the ideal normal distribution.

Q: 8 Knowing that data are normally distributed allows me to a) calculate the probability of obtaining a score greater than some specified value. b) calculate the probability of obtaining a score of exactly 1. c) calculate what range of values are unlikely to occur by chance. d) both a and c

Q: 7 The ordinate of a normal distribution is often labeled a) frequency. b) X. c) density. d) proportion.

Q: 6+ If behavior problem scores are normally distributed, and we want to say something meaningful about what values are likely and what are unlikely, we would have to know a) the mean. b) the standard deviation. c) the sample size. d) both a and b

Q: 5 If a population of behavior problem scores is reasonably approximated by a normal distribution, we would expect that the X axis would a) have values between 0 and 4. b) have values between -1 and +1. c) have only negative values. d) We cannot say what the values on that axis would be.

Q: 4 The difference between the histogram of 175 behavior problem scores and a normal distribution is a) the normal distribution is continuous, while behavior problem scores are discrete. b) the normal distribution is symmetric, while behavior problem scores may not be. c) the ordinate of the normal distribution is density, the ordinate for behavior problems is frequency. d) Each of the previous choices is correct.

Q: 3+ We know that 25% of the class got an A on the last exam, and 30% got a B. What percent got either an A or a B? a) 25% 30% = 7.5% b) 25% + 30 % = 55% c) 45% d) We cannot tell from the information that is presented.

Q: 2 We care a great deal about areas under the normal distribution because a) they translate directly to expected proportions. b) they are additive. c) they allow us to calculate probabilities of categories of outcomes. d) all of the above

Q: 1+ The normal distribution is a) most frequently observed for the distribution of small sample sizes. b) characterized by a high degree of skewness. c) a distribution with a known shape and other properties. d) the distribution that we would expect for the salaries of basketball players.

Q: 62 Construct two small sets of data that have the same mean, but a different standard deviation.

Q: 61 Given the following distribution, which would be the least useful measure of central tendency? Explain your answer.

Q: 60 Compare the distribution of exam scores for students who did and did not read the textbook prior to taking the exam. Discuss measures of variability and of central tendency.

Q: 59 Answer the following questions based on this distribution of exam scores. a) What is the median? b) Are there outliers? c) Does the distribution seem skewed? If so, is it positively, or negatively skewed?

Q: 58 What happens to the standard deviation when a constant is added to each score? Use the following set of data, and a constant of 2 to illustrate your answer. 1 2 3 4

Q: 57 Create two sets of scores with equal ranges, but different variances.

Q: 56 Create a box plot for the above data.

Q: 55 Based on the same data, calculate: a) The median location b) The median c) The hinge location d) The upper hinge e) The lower hinge f) H spread g) Lower fence h) Upper fence i) Lower adjacent value j) Upper adjacent value

Q: 54 A sample of 20 families reported how many children they have. Answer the following questions based on the summary table below. Number of children 0 1 2 3 4 Number of families 3 6 7 3 1 a) What is the range? b) What is the variance? c) What is the standard deviation?

Q: 53 Answer the following questions based on this set of numbers:1 2 2 3 3 3 4 5a) What is the range?b) What is the variance?c) What is the standard deviation?

Q: 52 There no outliers.

Q: 51 The median of this distribution is 16.

Q: 50 The sample variance is a biased statistic.