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Q:
One of the assumptions associated with the Wilcoxon Matched-Pairs Signed Rank test is that the distribution of the population differences is symmetric about their median.
Q:
In order to determine whether the median distance for the X-Special golf ball exceeds the median distance for the best-selling golf ball, six golfers were selected and asked to hit each ball with their driver. The distance was recorded. The following data were observed. GolferX-SpecialBest Seller124023322672703255240423423052502606285270The appropriate null and alternative hypotheses are:H0 : 1 2H0 : 1 > 2
Q:
In order to determine whether the median distance for the X-Special golf ball exceeds the median distance for the best-selling golf ball, six golfers were selected and asked to hit each ball with their driver. The distance was recorded. The following data were observed. Golfer
X-Special
Best Seller 1
240
233 2
267
270 3
255
240 4
234
230 5
250
260 6
285
270 Based on these, the appropriate test is the Kruskal-Wallis one-way analysis of variance.
Q:
The Wilcoxon Matched-Pairs Signed rank test is an alternative to the paired sample t-test when we are unwilling to assume that the populations are normally distributed.
Q:
If a decision maker wishes to test to determine whether the medians are equal for two populations the Mann-Whitney U test is appropriate for either independent or dependent sampling situations.
Q:
A large sample Mann-Whitney U test should be used when the sample sizes exceed 20.
Q:
In conducting a Mann-Whitney U test when the sample size is greater than 20, the U test statistic can be assumed normally distributed.
Q:
Recently, a study was done to determine whether the median speed on a section of highway is the same for cars versus trucks. A sample of 12 cars (n1 = 12) and 15 trucks (n2 = 15) was collected. If the Mann-Whitney U test is to be performed using an alpha = .05 and if the U test statistic is calculated to be 68, the null hypothesis should be rejected.
Q:
Recently, a study was done to determine whether the median speed on a section of highway is the same for cars versus trucks. A sample of 12 cars (n1 = 12) and 15 trucks (n2 = 15) was collected. If the Mann-Whitney U test is to be performed using an alpha = .05, the test critical U value is 49.
Q:
A claim was recently made that stated that the median income for male and female graduates is the same for those graduating with a degree in operations management. The following sample data were collected: Males
Females $42,000
$39,000 $36,000
$39,000 $40,000
$41,000 $32,000
$42,000 $50,000
$44,000 $47,000
$38,000 $47,000
$51,000 In employing the Mann-Whitney U test, the U test statistic is 25.5
Q:
A claim was recently made that stated that the median income for male and female graduates is the same for those graduating with a degree in operations management. The following sample data were collected: Males
Females $42,000
$39,000 $36,000
$39,000 $40,000
$41,000 $32,000
$42,000 $50,000
$44,000 $47,000
$38,000 $47,000
$51,000 In employing the Mann-Whitney U test, the U test statistic for the males is 43.5
Q:
A claim was recently made that stated that the median income for male and female graduates is the same for those graduating with a degree in operations management. The following sample data were collected: Males
Females $42,000
$39,000 $36,000
$39,000 $40,000
$41,000 $32,000
$42,000 $50,000
$44,000 $47,000
$38,000 $47,000
$51,000 In employing the Mann-Whitney U test, the sum of the ranks for the males is 53.5
Q:
The critical value for a one-tailed Mann-Whitney U test with sample sizes of n1 = 10 and n2 = 10 is 23 for a 0.05 level of significance.
Q:
A claim was recently made that stated that the median income for male and female graduates is the same for those graduating with a degree in operations management. In order to test this claim using the Mann-Whitney U test, the same number of males and females must be selected.
Q:
The Mann-Whitney U test is always a one-tailed test.
Q:
In a Mann-Whitney U test, if the sample sizes are large then the test statistic can be approximated by the student's t-distribution.
Q:
In a Mann-Whitney U test, the test statistic will be equal to the sum of the ranks from sample one, or sample two, whichever is larger.
Q:
One of the assumptions of the Mann-Whitney U test is that the population distributions are the same for shape and spread.
Q:
The logic behind the Mann-Whitney U test is that if the hypothesis is true that the populations have equal central locations, then the sum of the ranks from each population will be approximately equal.
Q:
In employing the Mann-Whitney U test, the sample data from the two populations are first combined and the ranks of the data are determined, but we keep track of which population each ranked item came from.
Q:
The Mann-Whitney U test can be used to test whether two independent populations have the same median so long as the data are measured on at least an ordinal scale.
Q:
The Mann-Whitney U test is a nonparametric test that is used to test whether two related populations have the same median.
Q:
The procedure of the Wilcoxon signed rank test is the same for either small or large sample sizes.
Q:
In a large sample test about a single population median, it is appropriate to employ the standard normal distribution so long as the population is also normally distributed.
Q:
In the Wilcoxon signed rank test using either small or large sample sizes, any value that equals the hypothesized median is discarded from the analysis.
Q:
In the Wilcoxon signed rank test for testing about a single population median, if the sample size is large (n > 20), the test statistic can be approximated by the standard normal distribution.
Q:
In conducting the Wilcoxon signed rank test, after collecting the sample data the next step is to find the sample median and subtract this value from each data value to obtain the deviations.
Q:
Managers for a company that produces a weight loss product claim that the median weight lost over six weeks for people who use this product will be at least 20 pounds. The following data were collected from a sample of nine people who used the product. Pounds Lost 6.00 22.00 14.00 5.00 30.00 12.00 7.00 21.00 25.00 Based on these data, the test statistic is W = 12.
Q:
Managers for a company that produces a weight loss product claim that the median weight lost over six weeks for people who use this product will be at least 20 pounds. The following data were collected from a sample of nine people who used the product. Pounds Lost 6.00 22.00 14.00 5.00 30.00 12.00 7.00 21.00 25.00 In order to test the manager's claim, they will need to assume that the population is normally distributed.
Q:
Recently, Major League Baseball officials stated that the median cost for a family of four to attend a baseball game including, parking, tickets, food, and drinks did not exceed $125.00. The following sample data were collected: Dollars Spent 142.00 99.00 134.00 175.00 100.00 225.00 80.00 Assuming that the test of the owners' claim is going to be conducted using an alpha = .05 level, the null hypothesis that the median cost does not exceed $125 should not be rejected.
Q:
Recently, Major League Baseball officials stated that the median cost for a family of four to attend a baseball game including, parking, tickets, food, and drinks did not exceed $125.00. The following sample data were collected: Dollars Spent 142.00 99.00 134.00 175.00 100.00 225.00 80.00 Assuming that the test of the owners' claim is going to be conducted using an alpha = .05 level, the critical value is t = 1.9432.
Q:
The Wilcoxon signed rank test is always a one-tail test with the rejection region in the upper tail.
Q:
Recently, Major League Baseball officials stated that the median cost for a family of four to attend a baseball game including, parking, tickets, food, and drinks did not exceed $125.00. As long as the data are considered interval or ratio level, the t-test can be used to test MLB's claim.
Q:
All Wilcoxon signed rank tests are two-tailed tests since we are testing whether the population median is the exact center of the population distribution.
Q:
The following null and alternative hypotheses are appropriate when using a Wilcoxon signed rank test.
H0 : Population Median equals 14.5
HA : Population Median is not equal to 14.5
Q:
If a decision maker believes that the population is normally distributed and the data are known to be ratio level, then the either the t-test or the Wilcoxon signed rank test can be used to test null hypotheses about a single population mean.
Q:
The Wilcoxon signed rank test should be used in place of the t-test whenever the sample size is less than 20.
Q:
The basic logic of the Wilcoxon signed rank test is that if about half the data values fall above the hypothesized median, and about half fall below, the null hypothesis should not be rejected.
Q:
The Wilcoxon signed rank test is used to test hypotheses about the population median.
Q:
The Wilcoxon signed rank test is used for testing hypotheses about a population median if the data are at nominal level.
Q:
Renton Industries makes replacement parts for the automobile industry. As part of the company's capacity planning, it needs a long-range total demand forecast. The following information was generated based on 10 years of historical data on total number of parts sold each year. Based on this information we can conclude that the linear trend model explains a significant proportion of the variation in the number of parts sold, because the p-value is much smaller than any reasonable α that we might use.
Q:
Renton Industries makes replacement parts for the automobile industry. As part of the company's capacity planning, it needs a long-range total demand forecast. The following information was generated based on 10 years of historical data on total number of parts sold each year. Based on this information, the percent of variation in the number of parts sold that is explained by the linear trend model is approximately 90.9.
Q:
You are given the following linear trend model: Ft = 345.60 - 200.5(t). This model implies that in year 1, the dependent variable had a value of 145.1.
Q:
From an annual time series of a company's sales the linear trend model Ft = 127 + 54(t) has been developed. This means that on average sales have been increasing by 127 per year.
Q:
You are given the following linear trend model: Ft = 345.60 - 200.5(t). The forecast for period 15 is approximately -2,662.
Q:
A time-series graph shows that annual sales data have grown gradually over the past 10 years. Given this, if a linear trend model is used to forecast future years' sales, the sign on the regression slope coefficient will be positive.
Q:
One of the basic tools for creating a trend-based forecasting model is regression analysis.
Q:
Two common unweighted indexes are the Paasche Index and the Laspeyres Index.
Q:
To compare one value measured at one point in time with other values measured at different points in time, index numbers must be used.
Q:
Stock analysts have recently stated in a meeting on Wall Street that over the past 50 years there have been periods of high market prices followed by periods of lower prices but over time prices have moved upwards. Given their statement, stock prices most likely exhibit only trend and cyclical components.
Q:
In order to identify a cyclical component in time-series data, one year of weekly data should be sufficient.
Q:
Some stocks are referred to as cyclical stock because they tend to be in favor for several years and then out of favor for several years. This is a correct use of the term cyclical.
Q:
Harrison Hollow, an upscale eatery in Atlanta, tracks its sales on a daily basis. Recently, the manager stated that sales over the past three weeks have been very cyclical. Given the data she has, this statement is not a reasonable one to make.
Q:
The Gilbert Company chief financial officer has been tracking annual sales for each of the company's three divisions for the past 10 years. At a recent meeting, he pointed to the annual data and indicated that it clearly showed the seasonality associated with its business. Given the data, this statement may have been very appropriate.
Q:
While virtually all time series exhibit a random component, not all time series exhibit other components.
Q:
In a recent meeting, a manager indicated that sales tend to be higher during October, November, and December and lower in the spring. In making this statement, she is indicating that sales for the company are cyclical.
Q:
An annual time series cannot exhibit a seasonal component.
Q:
The time-series component that implies a long-term upward or downward pattern is called the trend component.
Q:
In order for a time series to exhibit a seasonal component, the data must be measured in periods as short or shorter than quarterly.
Q:
A stockbroker at a large brokerage firm recently analyzed the combined annual profits for all firms in the airline industry. One time-series component that may have been present in these annual data was a seasonal component.
Q:
The forecasting interval is the unit of time for which forecasts are made.
Q:
If the historical data on which the model is being built consist of weekly data, the forecasting period would also be weekly.
Q:
If a manager is planning for an expansion of the factory, a forecast model with a long-term planning horizon would probably be used. "¢
Q:
Model specification is the process of determining how well a forecasting model fits the past data.
Q:
Which of the following is not an indication of potential multicollinearity problems?
A) The sign on the standard error of the estimate is positive.
B) A sign on a regression slope coefficient is negative when the sign on the correlation coefficient was positive.
C) The standard error of the estimate increases when a variable enters the model in the presence of other independent variables.
D) An independent variable goes from being statistically significant to being insignificant when a new variable is added to the model.
Q:
A multiple regression is shown for a data set of yachts where the dependent variable is the price in thousands of dollars. Based on this output, which of the independent variables appear to be significantly helping to predict the price of a yacht, using a 0.10 level of significance?
A) Age
B) Age and length
C) Rooms and Nav. Equip.
D) Length and Nav. Equip.
Q:
The editors of a national automotive magazine recently studied 30 different automobiles sold in the United States with the intent of seeing whether they could develop a multiple regression model to explain the variation in highway miles per gallon. A number of different independent variables were collected. The following regression output (with some values missing) was recently presented to the editors by the magazine's analysts: Based on this output and your understanding of multiple regression analysis, which of the independent variables is not considered statistically significant if the test is conducted at the 0.05 level of statistical significance?
A) All the variables in the model are statistically significant.
B) None of the variables in the model is statistically significant.
C) Torque and price as tested
D) Cylinders, torque, and 0 to 60
Q:
The editors of a national automotive magazine recently studied 30 different automobiles sold in the United States with the intent of seeing whether they could develop a multiple regression model to explain the variation in highway miles per gallon. A number of different independent variables were collected. The following regression output (with some values missing) was recently presented to the editors by the magazine's analysts: Based on this output and your understanding of multiple regression analysis, which of the following statements is true?
A) The overall multiple regression model explains a significant portion of the variation in highway mileage when tested at a significance level of 0.05.
B) Only the two independent variables are statistically significant in the presence of the others when a significance level of 0.05 is used to test.
C) The standard error of the estimate is a negative value due to the multicollinearity problems in the model.
D) None of the above is true.
Q:
The editors of a national automotive magazine recently studied 30 different automobiles sold in the United States with the intent of seeing whether they could develop a multiple regression model to explain the variation in highway miles per gallon. A number of different independent variables were collected. The following regression output (with some values missing) was recently presented to the editors by the magazine's analysts: Based on this output and your understanding of multiple regression analysis, what is the critical value for testing the significance of the overall regression model at a 0.05 level of statistical significance?
A) About F = 5.92
B) Nearly F = 3.80
C) Approximately F = 2.50
D) None of the above
Q:
The editors of a national automotive magazine recently studied 30 different automobiles sold in the United States with the intent of seeing whether they could develop a multiple regression model to explain the variation in highway miles per gallon. A number of different independent variables were collected. The following regression output (with some values missing) was recently presented to the editors by the magazine's analysts: Based on this output and your understanding of multiple regression analysis, what is the value of the standard error of the estimate for this model?
A) Approximately 2.02
B) About 5.97
C) Approximately 14.05
D) Nearly 8.0
Q:
The editors of a national automotive magazine recently studied 30 different automobiles sold in the United States with the intent of seeing whether they could develop a multiple regression model to explain the variation in highway miles per gallon. A number of different independent variables were collected. The following regression output (with some values missing) was recently presented to the editors by the magazine's analysts: Based on this output and your understanding of multiple regression analysis, how many degrees of freedom are associated with the Residual in the ANOVA table?
A) 19
B) 22
C) 7
D) 29
Q:
The editors of a national automotive magazine recently studied 30 different automobiles sold in the United States with the intent of seeing whether they could develop a multiple regression model to explain the variation in highway miles per gallon. A number of different independent variables were collected. The following regression output (with some values missing) was recently presented to the editors by the magazine's analysts: Based on this output and your understanding of multiple regression analysis, what is the adjusted R-square value for this model?
A) About 0.82
B) Approximately 0.90
C) Just under 0.48
D) None of the above
Q:
The editors of a national automotive magazine recently studied 30 different automobiles sold in the United States with the intent of seeing whether they could develop a multiple regression model to explain the variation in highway miles per gallon. A number of different independent variables were collected. The following regression output (with some values missing) was recently presented to the editors by the magazine's analysts: Based on this output and your understanding of multiple regression analysis, what percentage of variation in the dependent variable is explained by the regression model?
A) Approximately 82 percent
B) Over 90 percent
C) About 37 percent
D) None of the above
Q:
The editors of a national automotive magazine recently studied 30 different automobiles sold in the United States with the intent of seeing whether they could develop a multiple regression model to explain the variation in highway mileage per gallon. A number of different independent variables were collected. The following correlation matrix was developed: If only one variable were to be brought into the model, which variable should it be if the goal is to explain the highest possible percentage of variation in the dependent variable?
A) 0 to 60 mph
B) Horsepower
C) Curb weight
D) Displacement
Q:
In a multiple regression model, which of the following is true?
A) The coefficient of determination will be equal to the square of the highest correlation in the correlation matrix.
B) Adding variables that have a low correlation with the dependent variable will cause the R-square value to decline.
C) The sum of the residuals computed for the least squares regression equation will be zero.
D) The adjusted R-square might be higher or lower than the value of the R-square.
Q:
Which of the following is not an assumption of the multiple regression model?
A) The mean of the residuals is equal to the variance at all combinations of levels of the independent variables.
B) The regression error terms are normally distributed.
C) The model error terms are independent.
D) The residuals have a constant variance for all combinations of values for the independent variables.
Q:
To check out whether the regression assumption involving normality of the error terms is valid, it is appropriate to construct a normal probability plot. If this plot forms a straight line from the lower left-hand corner diagonally up to the upper right-hand corner, the error terms may be assumed to be normally distributed.
Q:
The following residual plot was constructed based on a simple linear regression model. Based on this plot, there appears to be no basis for concluding that a curvilinear model may be more appropriate than a linear model to explain the variation in the y variable.
Q:
The following residual plot is an output of a regression model. Based on this residual plot, there is evidence to suggest that the underlying relationship between the y variable and the x variable is nonlinear.
Q:
If one or more of the regression assumptions has been violated this means that the current regression model is not the best one for this data set, and another model should be sought.