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82 Calculate and for the previous problem.

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Related questions

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: 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: 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: 27+ Which of the following is NOT a method of describing data that reduces the role of outliers on the measurement of a data set's variability? a) interquartile range b) boxplot c) range d) trimmed statistics
Q: 18+ People in the stock market refer to a measure called the "standard deviation," although it is calculated somewhat differently from the one discussed here. It is a good guess that this measure refers toa) the riskiness of the stock.b) the value of the stock.c) how much the stock price is likely to fluctuate.d) how much money you are likely to earn from buying that stock.
Q: 15+ Data points at the extremes of the distribution have a) little effect on the variance. b) more effect on the variance than scores at the center of the distribution. c) are undoubtedly incorrect. d) distort the usefulness of the median.
Q: 12 The vertical line in the center of a box plot a) represents the sample mean. b) represents the sample median. c) serves to anchor the box. d) can represent anything you want it to.
Q: 10 What do we mean by an unbiased statistic? a) a statistic that equals the sample mean b) a statistic whose average is very stable from sample to sample c) a statistic used to measure racial diversity d) a statistic whose long range average is equal to the parameter it estimates
Q: 8+ When calculating the standard deviation we divide by N-1 rather than N because the result is a) smaller. b) less biased. c) easier to interpret. d) equal to the population mean.
Q: 44 Algebraically, the mean = (X)2/N.
Q: 35 Which of the following is probably most useful in studies in which extreme scores sometimes occur but have no real practical significance? a) mean b) median c) average d) none of the above
Q: 32+ If a store manager wanted to stock the men's clothing department with shirts fitting the most men, which measure of central tendency of men's shirt sizes should be employed? a) mode b) mean c) median d) average
Q: 28 For the following data set [1, 9, 9, 9, 11, 28], which of the following is false? a) The mode is 9. b) The median is 9. c) The mean is 9. d) The median location is 3.5.
Q: 21 Suppose a sample of children reported how many voices they heard in their heads on a particular day, and that hearing voices was a relatively uncommon experience. In this case the mode a) will show that the most common number of voices heard is zero. b) will tell us nothing about the children who do hear voices. c) both a and b d) none of the above
Q: 18 is the symbol commonly used for the a) mean. b) mode. c) median. d) none of the above
Q: 13 The chief advantage of the median is that a) it represents a score actually occurring in the data set. b) it is best used with nominal scales. c) *it is not disproportionately affected by extreme scores. d) it is the most commonly occurring score.
Q: 11+ Given the numbers 6 7 9 11 15 71 86, how many numbers fall below the median? a) 3 b) 11 c) 6 to 86 d) 6, 7, 9, and 11
Q: 7+ Which of the following is useful with data collected with nominal scales? a) median b) mode c) mean d) none of the above
Q: 10 When we plot a histogram, the values on the X axis are a) the real lower limits and real upper limits b) the midpoints c) the integers closest to the boundaries. d) any of the above, depending on what makes the most sense at the time
Q: 36 Multiple regression analysis yielded the following regression equation: Predicted Happiness = .36 friends - .13 stress + 1.23 Which of the following is true? a) Happiness increases as Friends increase. b) Happiness increases as Stress increases. c) Happiness decreases as Friends and Stress increase. d) none of the above
Q: 25+ If we know that a regression coefficient is statistically significant, we know that a) it is positive. b) it is not 0.0. c) it is not 1.0. d) it is large.
Q: 20 If we find all of the residuals when predicting our obtained values of Y from the regression equation, the sum of squared residuals would be expected to be _______ the sum of the squared residuals for a new set of data. a) less than b) greater than c) the same as d) We can"t tell.
Q: 18+ If our regression equation is = 0.75 age + 0.50 experience - 0.10 grade point average " 2.0, and if our first subject had scores of 16, 4, and 3.0 on those three variables, respectively, then that subject's predicted score would be a) 11.7 b) 10 c) 16 d) -3
Q: 2+ Assume that we generated a prediction just by adding together the number of stressful events you report experiencing over the last month, the number of close friends you have, and your score on a measure assessing how much control you feel you have over events in your life (i.e., prediction = stress + friends + control). The regression coefficient for stressful events would be a) 1.0 b) 4.0 c) 0.0 d) There is no way to know.
Q: 20+ When we think in terms of standardized data, the slope represents a) the change in X for a one unit change in Y. b) the number of standard deviations will differ for a one standard deviation difference in X. c) the height of the regression line. d) 0.
Q: 19 When we have standardized data, the slope will be denoted as
Q: 17 In the equation = 12.6 X + 5 a) a difference of one unit in X will lead to a 5 point difference in the prediction. b) will decrease as X increases. c) the correlation is certain to be significant. d) a difference of one unit in X will lead to a 12.6 point difference in the prediction.
Q: 13 The "best fitting line" is that regression line that a) minimizes the errors of prediction. b) minimizes each squared error of prediction. c) minimizes the sum of squared errors of prediction. d) hits the most points as it goes through the scatterplot.

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