Business Ethics

Q: Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is monthly household expenditures on groceries (in $'s), and her independent variable is annual household income (in $1,000's). Regression analysis of the data yielded the following tables. Coefficients Standard Error t Statistic p-value Intercept 39.14942 22.30182 1.755436 0.109712 x 1.792312 0.407507 4.398234 0.001339 Source df SS MS F Se = 29.51443 Regression 1 16850.99 16850.99 19.34446 r2 = 0.682478 Residual 9 7839.915 871.1017 Total 10 24690.91 Abby's sample size (n) is __________. a) 8 b) 10 c) 11 d) 20 e) 12

Q: Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is monthly household expenditures on groceries (in $'s), and her independent variable is annual household income (in $1,000's). Regression analysis of the data yielded the following tables. Coefficients Standard Error t Statistic p-value Intercept 39.14942 22.30182 1.755436 0.109712 x 1.792312 0.407507 4.398234 0.001339 Source df SS MS F Se = 29.51443 Regression 1 16850.99 16850.99 19.34446 r2 = 0.682478 Residual 9 7839.915 871.1017 Total 10 24690.91 The correlation coefficient between the two variables in this regression is __________. a) 0.682478 b) -0.83 c) 0.83 d) -0.68 e) 1.0008

Q: Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is monthly household expenditures on groceries (in $'s), and her independent variable is annual household income (in $1,000's). Regression analysis of the data yielded the following tables. Coefficients Standard Error t Statistic p-value Intercept 39.14942 22.30182 1.755436 0.109712 x 1.792312 0.407507 4.398234 0.001339 Source df SS MS F Se = 29.51443 Regression 1 16850.99 16850.99 19.34446 r2 = 0.682478 Residual 9 7839.915 871.1017 Total 10 24690.91 Abby's regression model is __________. a) y = 39.15 + 2.79x b) y = 39.15 - 1.79x c) y = 1.79 + 39.15x d) y = -1.79 + 39.15x e) y = 39.15 + 1.79x

Q: Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in $100's). Regression analysis of the data yielded the following tables. Coefficients Standard Error t Statistic p-value Intercept 3.996 1.161268 3.441065 0.004885 x 0.358 0.102397 3.496205 0.004413 Source df SS MS F Se = 0.898 Regression 1 9.858769 9.858769 12.22345 r2 = 0.526341 Residual 11 8.872 0.806545 Total 12 18.73077 For a lot size of 10,000 handsets, Louis' model predicts total cost will be _____. a) $4,031.80 b) $757.60 c) $3,960.20 d) $354.01 e) $1873.077

Q: Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in $100's). Regression analysis of the data yielded the following tables. Coefficients Standard Error t Statistic p-value Intercept 3.996 1.161268 3.441065 0.004885 x 0.358 0.102397 3.496205 0.004413 Source df SS MS F Se = 0.898 Regression 1 9.858769 9.858769 12.22345 r2 = 0.526341 Residual 11 8.872 0.806545 Total 12 18.73077 Using a = 0.05, Louis should ________________. a) increase the sample size b) suspend judgment c) not reject H0: b1 = 0 d) reject H0: b1 = 0 e) do not reject H0: b0 = 0

Q: Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in $100's). Regression analysis of the data yielded the following tables. Coefficients Standard Error t Statistic p-value Intercept 3.996 1.161268 3.441065 0.004885 x 0.358 0.102397 3.496205 0.004413 Source df SS MS F Se = 0.898 Regression 1 9.858769 9.858769 12.22345 r2 = 0.526341 Residual 11 8.872 0.806545 Total 12 18.73077 Louis's sample size (n) is ________________. a) 13 b) 14 c) 12 d) 24 e) 1

Q: Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in $100's). Regression analysis of the data yielded the following tables. Coefficients Standard Error t Statistic p-value Intercept 3.996 1.161268 3.441065 0.004885 x 0.358 0.102397 3.496205 0.004413 Source df SS MS F Se = 0.898 Regression 1 9.858769 9.858769 12.22345 r2 = 0.526341 Residual 11 8.872 0.806545 Total 12 18.73077 The correlation coefficient between Louis's variables is ________________. a) -0.73 b) 0.73 c) 0.28 d) -0.28 e) 0.00

Q: Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in $100's). Regression analysis of the data yielded the following tables.CoefficientsStandard Errort Statisticp-valueIntercept3.9961.1612683.4410650.004885x0.3580.1023973.4962050.004413SourcedfSSMSFSe = 0.898Regression19.8587699.85876912.22345r2 = 0.526341Residual118.8720.806545Total1218.73077Louis's regression model is ________________.a) y = -0.358 + 3.996xb) y = 0.358 + 3.996xc) y = -3.996 + 0.358xd) y = 3.996 - 0.358xe) y = 3.996 + 0.358x

Q: The equation of the trend line for the data based on sales (in $1000) of a local restaurant over the years 2005-2010 is Sales= -265575+132.571 year. The equation of the trend line when using 1 to 6 for 2005-2010 is ________ a) -265575+132.571x b) 132,571x c) 97.284+132.571x d) -263571+98x e) 2004+37.2x

Q: The equation of the trend line for the data based on sales (in $1000) of a local restaurant over the years 2005-2010 is Sales= -265575+132.571 year. Using the trend line, the forecast sales for the year 2012 is ________ a) $1157.85 b) $1157850 c) $132571 d) $2673304 e) $1000327

Q: A manager wants to predict the cost (y) of travel for salespeople based on the number of days (x) spent on each sales trip. The following model has been developed: y = $400 + 120x. If a trip took 3 days, the predicted cost of the trip is _____________. a) 760 b) 360 c) 523 d) 1560 e) 1080

Q: A manager wants to predict the cost (y) of travel for salespeople based on the number of days (x) spent on each sales trip. The following model has been developed: y = $400 + 120x. If a trip took 4 days, the predicted cost of the trip is _____________. a) 480 b) 880 c) 524 d) 2080 e) 1080

Q: A manager wishes to predict the annual cost (y) of an automobile based on the number of miles (x) driven. The following model was developed: y = 2,000 + 0.42x. If a car is driven 20,000 miles, the predicted cost is ____________. a) 10,400 b) 20,000 c) 2,840 d) 6,200 e) 6,750

Q: A manager wishes to predict the annual cost (y) of an automobile based on the number of miles (x) driven. The following model was developed: y = 2,000 + 0.42x.If a car is driven 30,000 miles, the predicted cost is _____________. a) 10,400 b) 14,600 c) 2,000 d) 32,000 e) 10,250

Q: A manager wishes to predict the annual cost (y) of an automobile based on the number of miles (x) driven. The following model was developed: y = 2,000 + 0.42x. If a car is driven 15,000 miles, the predicted cost is ____________. a) 2,090 b) 17,000 c) 8,400 d) 8,300 e) 6,300

Q: In the regression equation, y=2.164+1.3657x n=6, the mean of x is 8.667, Sxx=89.333 and Se=3.44. A 95% prediction interval for y when x=8 is _________ a) (9.13, 17.05) b) (2.75, 23.43) c) (10.31, 15.86) d) (3.56, 22.62) e) (12.09, 14.09)

Q: In the regression equation, y = 2.164 + 1.3657x, n = 6, the mean of x is 8.667, Sxx= 89.333 and Se= 3.44. A 95% confidence interval for the average of y when x=8 is _________ a) (9.13, 17.05) b) (2.75, 23.43) c) (10.31, 15.86) d) (3.56, 22.62) e) (12.09, 14.09)

Q: A researcher has developed a regression model from fifteen pairs of data points. He wants to test if the slope is significantly different from zero. He uses a two"‘tailed test and a = 0.10. The critical table t value is _______. a) 1.771 b) 1.350 c) 1.761 d) 2.145 e) 2.068

Q: A researcher has developed a regression model from fourteen pairs of data points. He wants to test if the slope is significantly different from zero. He uses a two"‘ tailed test and a = 0.01. The critical table t value is _______. a) 2.650 b) 3.012 c) 3.055 d) 2.718 e) 2.168

Q: In a regression analysis if SST = 150 and SSR = 100, r 2 = _________. a) 0.82 b) 1.22 c) 1.50 d) 0.67 e) -1.00

Q: In a regression analysis if SST = 200 and SSR = 200, r 2 = _________. a) 0.25 b) 0.75 c) 0.00 d) 1.00 e) -1.00

Q: If x and y in a regression model are totally unrelated, _______. a) the correlation coefficient would be -1 b) the coefficient of determination would be 0 c) the coefficient of determination would be 1 d) the SSE would be 0 e) the MSE would be 0s

Q: The proportion of variability of the dependent variable accounted for or explained by the independent variable is called the _______. a) sum of squares error b) coefficient of correlation c) coefficient of determination d) covariance e) regression sum of squares

Q: The numerical value of the coefficient of determination must be _______. a) between -1 and +1 b) between -1 and 0 c) between 0 and 1 d) equal to SSE/(n-2) e) between -100 and +100

Q: In regression analysis, R-squared is also called the _______. a) residual b) coefficient of determination c) coefficient of correlation d) standard error of the estimate e) sum of squares of regression

Q: A simple regression model developed for ten pairs of data resulted in a sum of squares of error, SSE = 125. The standard error of the estimate is _______. a) 12.5 b) 3.5 c) 15.6 d) 3.95 e) 25

Q: A simple regression model developed for 12 pairs of data resulted in a sum of squares of error, SSE = 246. The standard error of the estimate is _______. a) 24.6 b) 4.96 c) 20.5 d) 4.53 e) 12.3

Q: A standard deviation of the error of the regression model is called the _______. a) coefficient of determination b) sum of squares of error c) standard error of the estimate d) R-squared e) coefficient of correlation

Q: The total of the squared residuals is called the _______. a) coefficient of determination b) sum of squares of error c) standard error of the estimate d) R-squared e) coefficient of correlation

Q: The following residuals plot indicates _______________. a) a nonlinear relation b) a nonconstant error variance c) the simple regression assumptions are met d) the sample is biased e) a random sample

Q: The assumption of constant error variance in regression analysis is called _______. a) heteroscedasticity b) homoscedasticity c) residuals d) linearity e) nonnormality

Q: The assumptions underlying simple regression analysis include ______________. a) the error terms are exponentially distributed b) the error terms have unequal variances c) the model is nonlinear d) the error terms are dependent e) the error terms are independent

Q: One of the assumptions made in simple regression is that ______________. a) the error terms are exponentially distributed b) the error terms have unequal variances c) the model is linear d) the error terms are dependent e) the model is nonlinear

Q: One of the assumptions made in simple regression is that ______________. a) the error terms are normally distributed b) the error terms have unequal variances c) the model is nonlinear d) the error terms are dependent e) the error terms are all equal

Q: For the following scatter plot and regression line, at x = 35 the residual is _______.a) positiveb) zeroc) negatived) imaginarye) unknown

Q: Consider the following scatter plot and regression line. At x = 50, the residual (error term) is _______.a) positiveb) zeroc) negatived) imaginarye) unknown

Q: The following data is to be used to construct a regression model:X35748109y54547108The regression equation is _______________.a) y = 16.49 + 1.43xb) y = 1.19 + 0.91xc) y = 1.19 + 0.75xd) y = 0.75 + 0.18xe) y = 0.91 + 4.06x

Q: The following data is to be used to construct a regression model:X35748109y54547108The value of the slope is ____________.a) 16.49b) 1.19c) 1.43d) 0.75e) 1.30

Q: The following data is to be used to construct a regression model:X35748109y54547108The value of the intercept is ________.a) 16.49b) 1.19c) 1.43d) 0.75e) 1.30

Q: The coefficient of correlation in a simple regression analysis is = - 0.6. The coefficient of determination for this regression would be _______. a) 0.6 b) - 0.6 or + 0.6 c) 0.13 d) - 0.36 e) 0.36

Q: For a certain data set the regression equation is y = 37 + 13x. The correlation coefficient between y and x in this data set _______. a) must be 0 b) is negative c) must be 1 d) is positive e) must be 3

Q: For a certain data set the regression equation is y = 29 - 5x. The correlation coefficient between y and x in this data set _______. a) must be 0 b) is negative c) must be 1 d) is positive e) must be >1

Q: In the regression equation, y = 54.78 + 1.45x, the intercept is _______. a) 1.45 b) -1.45 c) 54.78 d) -54.78 e) 0.00

Q: In the regression equation, y = 49.56 + 0.97x, the slope is _______.a) 0.97b) 49.56c) 1.00d) 0.00e) -0.97

Q: A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a linear function of batch size (the number of boards produced in one lot or batch). The slope of the accountant's model is ______.a) batch sizeb) unit variable costc) fixed costd) total coste) total variable cost

Q: A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a linear function of batch size (the number of boards produced in one lot or batch). The intercept of this model is the ______. a) batch size b) unit variable cost c) fixed cost d) total cost e) total variable cost

Q: A quality manager is developing a regression model to predict the total number of defects as a function of the day of week the item is produced. Production runs are done 10 hours a day, 7 days a week. The dependent variable is ______. a) day of week b) production run c) percentage of defects d) number of defects e) number of production runs

Q: A quality manager is developing a regression model to predict the total number of defects as a function of the day of week the item is produced. Production runs are done 10 hours a day, 7 days a week. The explanatory variable is ______. a) day of week b) production run c) percentage of defects d) number of defects e) number of production runs

Q: If there is positive correlation between two sets of numbers, then _______. a) r = 0 b) r < 0 c) r > 0 d) SSE=1 e) MSE = 1

Q: If there is perfect negative correlation between two sets of numbers, then _______. a) r = 0 b) r = -1 c) r = +1 d) SSE=1 e) MSE = 1

Q: The numerical value of the coefficient of correlation must be _______. a) between -1 and +1 b) between -1 and 0 c) between 0 and 1 d) equal to SSE/(n-2) e) between 0 and -1

Q: From the following scatter plot, we can say that between y and x there is _______.a) perfect positive correlationb) virtually no correlationc) positive correlationd) negative correlatione) perfect negative correlation

Q: According to the following graphic, X and Y have _________.a) strong negative correlationb) virtually no correlationc) strong positive correlationd) moderate negative correlatione) weak negative correlation

Q: Regression output from Excel software includes an ANOVA table.

Q: Regression output from Minitab software includes an ANOVA table.

Q: Regression output from Excel software directly shows the regression equation.

Q: Regression output from Minitab software directly displays the regression equation.

Q: Regression methods can be pursued to estimate trends that are linear in time.

Q: Prediction intervals get narrower as we extrapolate outside the range of the data.

Q: Given x, a 95% prediction interval for a single value of y is always wider than a 95% confidence interval for the average value of y.

Q: The variability in the estimated slope is smaller when the x-values are more spread out.

Q: The F-value to test the overall significance of a regression model is computed by dividing the sum of squares regression (SSreg) by the sum of squares error (SSerr).

Q: To determine whether the overall regression model is significant, the F-test is used.

Q: A t-test is used to determine whether the coefficients of the regression model are significantly different from zero.

Q: The range of admissible values for the coefficient of determination is -1 to +1.

Q: In the simple regression model, y = 21 - 5x, if the coefficient of determination is 0.81, we can say that the coefficient of correlation between y and x is 0.90.

Q: In a simple regression the coefficient of correlation is the square root of the coefficient of determination.

Q: The coefficient of determination is the proportion of variability of the dependent variable (y) accounted for or explained by the independent variable (x).

Q: The proportion of variability of the dependent variable (y) accounted for or explained by the independent variable (x) is called the coefficient of correlation.

Q: The standard error of the estimate, denoted se, is the square root of the sum of the squares of the vertical distances between the actual Y values and the predicted values of Y.

Q: One of the major uses of residual analysis is to test some of the assumptions underlying regression.

Q: In simple regression analysis the error terms are assumed to be independent and normally distributed with zero mean and constant variance.

Q: One of the assumptions of simple regression analysis is that the error terms are exponentially distributed

Q: Data points that lie apart from the rest of the points are called deviants. .

Q: The difference between the actual y value and the predicted y value found using a regression equation is called the residual.

Q: For the regression line, y = 21 - 5x, 21 is the y-intercept of the line.

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