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Q:
41+ The point of calculating effect size measures is to
a) decide if something is statistically significant.
b) convey useful information to the reader about what you found.
c) reject the null hypothesis.
d) prove causality.
Q:
40+ Cohen's is an example of
a) a measure of correlation.
b) an r-family measure.
c) a d-family measure.
d) a correlational measure.
Q:
39 When are we most likely to expect larger differences between group means?
a) when there is considerable variability within groups
b) when there is very little variability within groups
c) when we have large samples
d) when we have a lot of power
Q:
38 At test is most often used to
a) compare two means.
b) compare the standard deviations of two samples.
c) compare many means.
d) none of the above
Q:
37 Which of the following statements is true?
a) Confidence intervals are the boundaries of confidence limits.
b) Confidence intervals always enclose the population mean.
c) Sample size does not affect the calculation of t.
d) Confidence limits are the boundaries of confidence intervals.
Q:
36 A one-sample t test was used to see if a college ski team skied faster than the population of skiers at a popular ski resort. The resulting statistic was t.05(23) = -7.13, p < .05. What should we conclude?
a) The sample mean of the college skiers was significantly different from the population mean.
b) The sample mean of the college skiers was not significantly different from the population mean.
c) The null hypothesis was true.
d) The sample mean was greater than the population mean.
Q:
35+ All of the following increase the magnitude of the t statistic and/or the likelihood of rejecting H0 EXCEPTa) a greater difference between the sample mean and the population mean.b) an increase in sample size.c) a decrease in sample variance.d) a smaller significance level ().
Q:
34+ Which of the following statistics comparing a sample mean to a population mean is most likely to be significant if you used a two-tailed test?
a) t = 10.6
b) t = 0.9
c) t = -10.6
d) both a and c
Q:
33 If we fail to reject the null hypothesis in a t test we can conclude
a) that the null hypothesis is false.
b) that the null hypothesis is true.
c) that the alternative hypothesis is false.
d) that we don"t have enough evidence to reject the null hypothesis.
Q:
32 The two-tailed p value that a statistical program produces refers to
a) the value of t.
b) the probability of getting at least that large a value of t if the null hypothesis is false.
c) the probability of getting at least that large an absolute value of t if the null hypothesis is true.
d) the probability that the null hypothesis is true.
Q:
31+ If we have calculated a confidence interval and we find that it does NOT include the population mean,
a) we must have done something wrong in collecting data.
b) our interval was too wide.
c) we made a mistake in calculation.
d) this will happen a fixed percentage of the time.
Q:
30 A 95% confidence interval is going to be _______ a 99% confidence interval.
a) narrower than
b) wider than
c) the same width as
d) more accurate than
Q:
29 When we take a single sample mean as an estimate of the value of a population mean, we have
a) a point estimate.
b) an interval estimate.
c) a population estimate.
d) a parameter.
Q:
28 If we compute 95% confidence limits on the mean as 112.5 - 118.4, we can conclude thata) the probability is .95 that the sample mean lies between 112.5 and 118.4.b) the probability is .05 that the population mean lies between 112.5 and 118.4.c) an interval computed in this way has a probability of .95 of bracketing the population mean.d) the population mean is not less than 112.5.
Q:
27 Which of the following does NOT directly affect the magnitude of t?a) The actual obtained difference .b) The magnitude of the sample variance (s2).c) The sample size (N).d) The population variance (2).
Q:
26+ If we have run a t test with 35 observations and have found at of 3.60, which is significant at the .05 level, we would write
a) t(35) = 3.60, p <.05.
b) t(34) = 3.60, p >.05.
c) t(34) = 3.60, p <.05.
d) t(35) = 3.60, p <05.
Q:
25+ With a one-sample t test, the value of t is
a) always positive.
b) positive if the sample mean is too small.
c) negative whenever the sample standard deviation is negative.
d) positive if the sample mean is larger than the hypothesized population mean.
Q:
24+ For a t test with one sample we
a) lose one degree of freedom because we have a sample.
b) lose one degree of freedom because we estimate the population mean.
c) lose two degrees of freedom because of the mean and the standard deviation.
d) have N degrees of freedom.
Q:
23 The sampling distribution of the variance is
a) positively skewed.
b) negatively skewed.
c) normal.
d) rectangular.
Q:
22+ The variance of an individual sample is more likely than not to be
a) larger than the corresponding population variance.
b) smaller than the corresponding population variance.
c) the same as the population variance.
d) less than the population mean.
Q:
21 The reason why we need to solve for t instead of z in some situations relates to
a) the sampling distribution of the mean.
b) the sampling distribution of the sample size.
c) the sampling distribution of the variance.
d) the size of our sample mean.
Q:
20 The importance of the underlying assumption of normality behind a one-sample means test
a) depends on how fussy you are.
b) depends on the sample size.
c) depends on whether you are solving for t or z.
d) doesn"t depend on anything.
Q:
19 An assumption behind the use of a one-sample t test is that
a) the population is normally distributed.
b) the sample is normally distributed.
c) the population variance is normally distributed.
d) the population variance is known.
Q:
18 In using a z test for testing a sample mean against a hypothesized population mean, the formula for z is
a) b) c) d) none of the above
Q:
17 When you are using a one-sample t test, the degrees of freedom area) N.b) N - 1.c) N + 1.d) N - 2.
Q:
16 Many textbooks (though not this one) advocate testing the mean of a sample against a hypothesized population mean by using z even if the population standard deviation is not known, so long as the sample size exceeds 30. Those books recommend this because
a) they don"t know any better.
b) there are not tables for t for more than 30 degrees of freedom.
c) the difference between t and z is small for that many cases.
d) t and z are exactly the same for that many cases.
Q:
15+ If the standard deviation of the population is 15 and we repeatedly draw samples of 25 observations each, the resulting sample means will have a standard error of
a) 2
b) 3
c) 15
d) 0.60
Q:
14+ Suppose that we know that the sample mean is 18 and the population standard deviation is 3. We want to test the null hypothesis that the population mean is 20. In this situation we woulda) reject the null hypothesis at = .05.b) reject the null hypothesis at = .01c) retain the null hypothesis.d) We cannot solve this problem without knowing the sample size.
Q:
13 It makes a difference whether or not we know the population variance because
a) we cannot deal with situations in which the population variance is not known.
b) we have to call the result t if the population variance is used.
c) we have to call the result z if the population variance is not used.
d) we have to call the result t if the sample variance is used.
Q:
12 If the population from which we draw samples is "rectangular," then the sampling distribution of the mean will be
a) rectangular.
b) normal.
c) bimodal.
d) more normal than the population.
Q:
11 The standard error of the mean is a function of
a) the number of samples.
b) the size of the samples.
c) the standard deviation of the population.
d) both b and c
Q:
10 The standard error of the mean is
a) equal to the standard deviation of the population.
b) larger than the standard deviation of the population.
c) the standard deviation of the sampling distribution of the mean.
d) none of the above
Q:
9+ If we knew the population mean and variance, we would expect
a) the sample mean would closely approximate the population mean.
b) the sample mean would differ from the population mean by no less than 1.96 standard deviations only 5% of the time.
c) the sample mean would differ from the population mean by no more than 1.64 standard deviations only 5% of the time.
d) the sample mean would differ from the population mean by more than 1.96 standard errors only 5% of the time.
Q:
8 With large samples and a small population variance, the sample means usually
a) will be close to the population mean.
b) will slightly underestimate the population mean.
c) will slightly overestimate the population mean.
d) will equal the population mean.
Q:
7 If the population from which we sample is normal, the sampling distribution of the mean
a) will approach normal for large sample sizes.
b) will be slightly positively skewed.
c) will be normal.
d) will be normal only for small samples.
Q:
6+ Which of the following is NOT part of the Central Limit Theorem?
a) The mean of the sampling distribution approaches the population mean.
b) The variance of the sampling distribution approaches the population variance divided by the sample size.
c) The sampling distribution will approach a normal distribution as the sample size increases.
d) All of the above are part of the Central Limit Theorem.
Q:
5 The sampling distribution of the mean is
a) the population mean.
b) the distribution of the population mean over many populations.
c) the distribution of sample means over repeated samples.
d) the mean of the distribution of the sample.
Q:
4 When we are using a two-tailed hypothesis test, the alternative hypothesis is of the form
Q:
3+ When we are using a two-tailed hypothesis test, the null hypothesis is of the form
Q:
2 I want to test the hypothesis that children who experience daycare before the age of 3 do better in school than those who do not experience daycare. I have just described the
a) alternative hypothesis.
b) research hypothesis.
c) experimental hypothesis.
d) all of the above
Q:
1+ In one-sample tests of means we
a) compare one sample mean with another.
b) compare one sample mean against a population mean.
c) compare two sample means with each other.
d) compare a set of population means.
Q:
58 Based on the previous regression equation you just created, estimate cancer anxiety given the following values.
a) social support = 100; general anxiety = 50
b) social support = 25; general anxiety = 7
Q:
57 Given the information in the following table, create the corresponding regression equation.
Q:
56 If you wanted to identify mothers who needed a parenting intervention to enhance sensitivity and could only collect two pieces of information from each family due to time and costs, which of the measures in the previous example would you select? Why?
Q:
55 How do the regression results vary from the simple correlations presented below? Explain why this may be the case.
Q:
54 How much variability in maternal sensitivity is accounted for by the set of predictors?
Q:
53 Which individual predictors are significantly associated with maternal sensitivity?
Q:
52 Are the set of predictors significantly associated with maternal sensitivity?
Q:
51 Write a sentence explaining the analysis presented in the following table (i.e., what are the predictor variables, what is the criterion variable).
Q:
50 Based on the same formula (= .75 X -.40 Z + 5), calculate the missing predictor variables based on the following information.
a) = 100; X = 0
b) = 0; Z = -20
Q:
49 Estimate Y based on the equation = .75 X -.40 Z + 5 using the following values.
a) X = 10; Z = 0
b) X = 0; Z = 0
c) X = 20; Z = 100
Q:
48 Individual predictors cannot be individually associated with the criterion variable if R is not different from 0 (i.e., if the entire model is not significant).
Q:
47 R2 can range from -1 to 1.
Q:
46 Multiple regression examines the degree of association between any predictor and the criterion variable controlling for other predictors in the equation.
Q:
45 Stepwise regression procedures capitalize on chance.
Q:
44 Any association that was significant as a simple correlation will be significant in a multiple regression equation predicting the same criterion variable.
Q:
43 Multiple regression allows you to examine the degree of association between individual independent variables and the criterion variable AND the degree of association between the set of independent variables and the criterion variable.
Q:
42 In multiple regression, the criterion variable is predicted by more than one independent variable.
Q:
41 In a regression predicting adolescent delinquent behavior from gender, the number of delinquent peers in the social network, and parental under control, R2 = .60. This means each of the variables accounted for 36% of the variability in delinquent behavior.
Q:
40 Multiple regression means there is more than one criterion variable.
Q:
39 Multicollinearity occurs when the predictor variables are highly correlated with one another.
Q:
38 A table in which each variable is correlated with every other variable is called
a) a multivariate table
b) an intercorrelation matrix
c) a contingency table
d) a pattern matrix
Q:
37 We want to predict a person's happiness from the following variables: degree of optimism, success in school, and number of close friends. What type of statistical test can tell us whether these variables predict a person's happiness?
a) factorial ANOVA
b) multiple comparison
c) regression
d) multiple regression
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:
35+ The following regression equation was found for a sample of college students.
predicted happiness = 32.8 GPA + 17.3 pocket money + 7.4
Which of the following can be concluded?
a) The correlation between pocket money and happiness is larger than the correlation between GPA and happiness.
b) GPA is less useful than pocket money in predicting happiness.
c) For students with no pocket money, a one-unit increase in GPA will increase the value of predicted happiness by 32.8 units.
d) The r-squared value for GPA must be greater than the r-squared value for pocket money.
Q:
34 A multiple regression analysis was used to test the values of visual acuity, swing power, and cost of clubs for predicting golf scores. The regression analysis showed that visual acuity and swing power predicted significant amounts of the variability in golf scores, but cost of clubs did not. What can be concluded from these results?
a) Cost of clubs and golf scores are not correlated.
b) Cost of clubs adds predictive value above and beyond the predictive value of visual acuity and swing power.
c) The regression coefficient of cost of clubs is equal to zero.
d) Removing cost of clubs from the overall model will not reduce the model's R2 value significantly.
Q:
33 The example in the text predicting distress in cancer patients used distress at an earlier time as one of the predictors. This was done
a) because the authors wanted to be able to report a large correlation.
b) because the authors wanted to see what effect earlier distress had.
c) because the authors wanted to look at the effects of self-blame after controlling for initial differences in distress.
d) because the authors didn"t care about self-blame, but wanted to control for it.
Q:
32 The example in Chapter 11 of predicting weight from height and sex showed that
a) adding sex as a predictor accounted for an important source of variability.
b) there is a much stronger relationship between height and weight in males than in females.
c) sex is not a useful predictor in this situation.
d) we cannot predict very well, even with two predictors.
Q:
31+ The text generally recommended against formal procedures for finding an optimal regression procedure because
a) those procedures don"t work.
b) those procedures pay too much attention to chance differences.
c) the statistical software won"t handle those procedures.
d) all of the above
Q:
30 Many of the procedures for finding an optimal regression equation (whatever that means) are known as
a) hunting procedures.
b) trial and error procedures.
c) trialwise procedures.
d) stepwise procedures.
Q:
29+ If you drop a predictor from the regression equation
a) the correlation could increase.
b) the correlation will probably go down.
c) the correlation could stay the same.
d) both b and c
Q:
28 If the overall analysis of variance is NOT significant
a) we need to look particularly closely at the tests on the individual variables.
b) it probably doesn"t make much sense to look at the individual variables.
c) the multiple correlation is too large to worry about.
d) none of the above
Q:
27 The Analysis of Variance section in computer results for multiple regression
a) compares the means of several variables.
b) tests the overall significance of the regression.
c) tests the significance of each predictor.
d) compares the variances of the variables.
Q:
26 In an example in Chapter 10 we found that the relationship between how a student evaluated a course, and that student's expected grade was significant. In this chapter Grade was not a significant predictor. The difference is
a) we had a new set of data.
b) grade did not predict significantly once the other predictors were taken into account.
c) the other predictors were correlated with grade.
d) both b and c
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:
24 The statistical tests on regression coefficients are usually
a) t tests.
b) z tests.
c) F tests.
d) r tests.
Q:
23+ When testing null hypotheses about multiple regression we
a) only look at the significance test on the overall multiple correlation.
b) have a separate significance test for each predictor and for overall significance.
c) don"t have to worry about significance testing.
d) know that if one predictor is significant, the others won"t be.
Q:
22 If we predict anxiety from stress and intrusive thoughts, and if the multiple regression is significant, that means that
a) the regression coefficient for stress will be significant.
b) the regression coefficient for intrusive thoughts will be significant.
c) both variables will be significant predictors.
d) We can"t tell.
Q:
21+ If the multiple correlation is high, we would expect to have _______ residuals than if the multiple correlation is low.
a) smaller
b) larger
c) the same as
d) We can"t tell.
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.