Q&A Hero

Q: It is generally suggested that the sample size in developing a multiple regression model should be at least four times the number of independent variables.

Q: Where there are two independent variables in a multiple regression, the regression equation forms a plane.

Q: The three components of the regression model-building process are model specification, model fitting, and model diagnosis.

Q: Residuals are calculated by e = y - .

Q: In a multiple regression model, each regression slope coefficient measures the average change in the dependent variable for a one-unit change in the independent variable, all other variables held constant.

Q: The multiple coefficient of determination measures the percentage of variation in the dependent variable that is explained by the independent variables in the model.

Q: In multiple regression analysis, the model will be developed with one dependent variable and two or more independent variables.

Q: The multiple coefficient of determination is the average of all the squared correlations of the independent variables.

Q: In a multiple regression model, R-square can be computed by squaring the highest correlation coefficient between the dependent variable and any independent variable.

Q: In multiple regression analysis, the residual is the absolute difference between the actual value of y and the predicted value of y.

Q: The following regression output is the result of a multiple regression application in which we are interested in explaining the variation in retail price of personal computers based on three independent variables, CPU speed, RAM, hard drive capacity, and-monitor included (1=Yes, 0=No). Given this output, what is the variable, Monitor, called? Also, given the other variables in the model, is Monitor significant in explaining the variation in the dependent variable? Test using a .05 level of alpha.

Q: The following multiple regression was conducted to attempt to predict the price of yachts based on the independent variables shown. Given this information and your knowledge of multiple regression, determine which, if any, of the four independent variables are statistically significant in explaining the variation in the dependent variable. Use a 0.05 level of significance and use the p-value method.

Q: The following multiple regression was conducted to attempt to predict the price of yachts based on the independent variables shown. Given this information and your knowledge of multiple regression, conduct the appropriate test to determine whether the overall regression model is statistically significant at the 0.05 level of significance using the critical value method.

Q: The following regression output is the result of a multiple regression application in which we are interested in explaining the variation in retail price of personal computers based on three independent variables, CPU speed, RAM, and hard drive capacity. However, some of the regression output has been omitted. Given this information and your knowledge of multiple regression, what is the value for the standard error of the estimate?

Q: The following regression output is the result of a multiple regression application in which we are interested in explaining the variation in retail price of personal computers based on three independent variables, CPU speed, RAM, and hard drive capacity. However, some of the regression output has been omitted. Given this information and your knowledge of multiple regression, what is the adjusted R-square value?

Q: The following regression output is the result of a multiple regression application in which we are interested in explaining the variation in retail price of personal computers based on three independent variables, CPU speed, RAM, and hard drive capacity. However, some of the regression output has been omitted. Given this information and your knowledge of multiple regression, what percentage of variation in the dependent variable is explained by the three independent variables in the model?

Q: Explain the difference between forward stepwise regression (standard stepwise), forward selection, and all possible subsets regression approaches.

Q: Based on the residual plot below, which of the following is correct? The above residual plot shows: A) linearity and nonconstant variance. B) nonlinearity and constant variance. C) linearity and constant variance. D) nonlinearity and nonconstant variance.

Q: A standardized residual is: A) equal to the sum of the residuals divided by n-1. B) the ratio of each residual divided by an estimate for the standard deviation of the residuals. C) a value that is normally distributed with a mean equal to zero and a standard deviation equal to one. D) None of the above

Q: Consider the following residual plot. Given this plot, what conclusion should be reached? A) There appears to be no basis for concluding that the relationship between the x and y variable is not linear. B) The assumption of constant variance seems to be supported by this plot. C) Both A and B are true. D) Neither A nor B are true.

Q: If the residuals have a constant variance, which of the following should be evident? A) The residuals should have a variance equal to zero for all levels of the independent variable. B) The plot of the residuals against each x variable should show that the spread in the residuals is about the same at all levels of each of the independent variables. C) The average residual should be about zero and the residual standard deviation should be approximately 1. D) None of the above

Q: The assumption that the errors or residuals are independent is best checked by: A) looking at a normal probability plot of the residuals. B) looking a scatter plot of each x versus y. C) looking at a residual plot versus x and checking for curvature. D) looking at a plot of the residuals versus time and checking for trends.

Q: To determine the aptness of the model, which of the following would most likely be performed? A) Check to see whether the residuals have a constant variance B) Determine whether the residuals are normally distributed C) Check to determine whether the regression model meets the assumption of linearity D) All 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 is the result of using a forward selection stepwise regression approach. Which of the following might explain why no other independent variables entered the model? A) No other variable had a correlation with the dependent variable that was close to 1.0. B) None of the remaining variables had a positive correlation with y. C) The remaining variables must be nearly perfectly correlated with the two variables already in the model. D) Given the two variables already in the model, none of the others could add significantly to the percentage of variation in the y variable that would be explained by the model.

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 is the result of using a forward selection stepwise regression approach. Based on the regression output, which of the following statements is true? A) There is a multicollinearity problem since the standard error of the estimate actually increased when the second variable, "Price as Tested," entered the model. B) The R-square value increased when the second variable entered the model. C) Neither variable in the model is statistically significant at the alpha = 0.05 level. D) The reason that only two variables entered the model is due to the small sample size used in this study.

Q: Which of the following is an advantage of using stepwise regression compared to just entering all the independent variables at one time? A) Stepwise will generally produce a model with a larger R-square value. B) The standard error of the estimate for a model constructed with stepwise regression will be larger than the one generated when all variables are entered at the same time. C) The stepwise regression allows the decision maker to observe the effects of multicollinearity more easily than when all the variables are entered at one time. D) There are no advantages of using stepwise regression over entering all variables at one time.

Q: Which of the following is the difference between forward selection and standard stepwise regression? A) In the standard stepwise regression, variables that were added at earlier steps can be removed at later steps, which is not the case with forward selection. B) The standard stepwise approach will generally produce a regression model with a higher R-square value than the forward selection approach. C) Forward selection begins by selecting the variable with the highest correlation with the dependent variable and then proceeds to select subsequent variables in order of their F-to-enter value, while standard stepwise selects the variables in the order specified by the decision maker and then removes them from the model as needed. D) There are no appreciable differences between the two methods, just different names for the same technique.

Q: A decision maker has five potential independent variables with which to build a regression model to explain the variation in the dependent variable. At step 1, variable x3 enters the regression model. Which of the following indicates which of the four remaining independent variables will be next to enter the model? A) The variable that has the next highest correlation with the dependent variable B) The variable that will provide the next largest value for the slope coefficient C) The variable with the highest coefficient of partial determination D) Can't be determined without seeing the correlation matrix.

Q: Standard stepwise regression A) is the same as forward selection. B) involves trying more regressions that the best subsets method. C) always finds the best regression model. D) combines attributes of both forward selection and backward elimination.

Q: Which of the following is not considered to be a stepwise regression technique? A) Forward selection regression B) Optimal variable entry and removal regression C) Backward elimination D) Standard stepwise regression

Q: The following model:A) is a linear model with interaction.B) is a second order polynomial model.C) is a composite model.D) is a convex model.

Q: Second-order polynomial models: A) always curve upward. B) always curve downward. C) can curve upward or downward depending on the data. D) measure interaction between variables.

Q: Interaction exists in a multiple regression model when: A) one independent variable affects the relationship between another independent variable and the dependent variable. B) multicollinearity is present in a regression model. C) the regression model is overall insignificant. D) a polynomial model used.

Q: The following regression output is from a multiple regression model: The variables t, t2, and t3 represent the t, t-squared, and t-cubed respectively where t is the indicator of time from periods t = 1 to t = 20. Which of the following best describes the type of forecasting model that has been developed? A) A complete third-order polynomial model B) A tri-variate smoothed regression model C) A nonlinear trend model D) A qualitative regression model

Q: Assume that a time-series plot takes the form of that shown in the following graph:Given this plot, which of the following models would likely give the best fit?

Q: Which of the following would best describe the situation that a second-degree polynomial regression equation would be used to model? A) An exponential growth trend B) A cosine function C) A parabola D) It depends on the number of independent variables.

Q: A forecasting model of the following form was developed:Which of the following best describes the form of this model?A) Quadratic modelB) 3rd degree polynomial modelC) 3rd level regression modelD) Tri-slope regression model

Q: In a multiple regression, the dependent variable is house value (in '000$) and one of the independent variables is a dummy variable, which is defined as 1 if a house has a garage and 0 if not. The coefficient of the dummy variable is found to be 5.4 but the t-test reveals that it is not significant at the 0.05 level. Which of the following is true? A) A garage increases the house value by $5,400. B) A garage increases the house value by $5,400, holding all other independent variables constant. C) The house value remains the same with or without a garage. D) We need to include other dummy variables.

Q: A multiple regression was conducted to predict the price of yachts in thousands of dollars. A dummy variable was included to indicate whether or not the yacht has a flying bridge, where 0 means "no" and 1 means "yes." Which of the following statements is correct using the 0.10 level of significance? A) Having a flying bridge significantly increases the price of a yacht by an average of $17.7, given the other variables present. B) Having a flying bridge significantly increases the price of a yacht by an average of $17,708, given the other variables present. C) We can tell that 17 out of 20 yachts have a flying bridge. D) Whether or not the yacht has a flying bridge does not significantly affect the price of a yacht, given the other variables present.

Q: A decision maker is considering including two additional variables into a regression model that has as the dependent variable, Total Sales. The first additional variable is the region of the country (North, South, East, or West) in which the company is located. The second variable is the type of business (Manufacturing, Financial, Information Services, or Other). Given this, how many additional variables will be incorporated into the model? A) 2 B) 6 C) 8 D) 9

Q: Golf handicaps are used to allow players of differing abilities to play against one another in a fair match. Recently a sample of golfers was selected in an effort to develop a model for explaining the difference in handicaps. One independent variable of interest is the number of rounds played per year. Another is whether or not the player is using an "original" name brand club or a copy. In recent years, a number of smaller golf club manufacturers have attempted to copy major golf club designs and sell "copies" of original clubs such as the Big Bertha by Calloway. The resulting regression analysis containing both Rounds Played and a Dummy variable for Club Used is shown as follows: Given this information, which of the following statements is not correct? A) The overall regression model is insignificant at the alpha = .05 level. B) The Club Dummy variable is statistically significant in the model meaning that knowing that a player used an original club or copy is of value in knowing the player's handicap. C) The two independent variables do not explain a statistically significant portion of the variation in golf handicap. D) All of the above are not correct.

Q: Golf handicaps are used to allow players of differing abilities to play against one another in a fair match. Recently a sample of golfers was selected in an effort to develop a model for explaining the difference in handicaps. One independent variable of interest is the number of rounds played per year. Another is whether or not the player is using an "original" name brand club or a copy. In recent years, a number of smaller golf club manufacturers have attempted to copy major golf club designs and sell "copies" of original clubs such as the Big Bertha by Calloway. To incorporate the type of club used, which of the following methods could be used? A) Create a dummy variable called "Club Used" and code it "O" for original and "C" for copy. B) Create a dummy variable called "Club Used" and code it 1 for copy and 0 for original. C) Create a dummy variable called Club Used" and code it 1 for original and 0 for copy. D) Either B or C would work.

Q: A study has recently been conducted by a major computer magazine publisher in which the objective was to develop a multiple regression model to explain the variation in price of personal computers. Three quantitative independent variables were used along with one qualitative variable. The qualitative variable was coded 1 if the computer included a monitor, 0 otherwise. The following computer printout shows the final output. Based on this information, and with a 0.05 level of significance, which of the following conclusions can be justified? A) Knowing whether the computer comes with a monitor or not is a significant factor in explaining the variation in price of the computer. B) There are substantial multicollinearity effects in this regression model. C) The only significant variable in the model at the .05 level of significance is Hard Drive Capacity. D) Removing the Monitor dummy variable would reduce the standard error of the estimate considerably.

Q: A regression equation that predicts the price of homes in thousands of dollars is t = 24.6 + 0.055x1 - 3.6x2, where x2 is a dummy variable that represents whether the house in on a busy street or not. Here x2 = 1 means the house is on a busy street and x2 = 0 means it is not. Based on this information, which of the following statements is true? A) On average, homes that are on busy streets are worth $3600 less than homes that are not on busy streets. B) On average, homes that are on busy streets are worth $3.6 less than homes that are not on busy streets. C) On average, homes that are on busy streets are worth $3600 more than homes that are not on busy streets. D) On average, homes that are on busy streets are worth $3.6 more than homes that are not on busy streets.

Q: The following multiple regression output was generated from a study in which two independent variables are included. The first independent variable (X1) is a quantitative variable measured on a continuous scale. The second variable (X2) is qualitative coded 0 if Yes, 1 if No. Based on this information, which of the following statements is true? A) The model explains nearly 63 percent of the variation in the dependent variable B) If tested at the 0.05 significance level, the overall model would be considered statistically significant. C) The variable X1 has a slope coefficient that is significantly different from zero if tested at the 0.05 level of significance. D) All of the above are 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. Included in these were two variables described as follows: Car Type (categorical) Whether Car has ABS Brakes (categorical) 1 = Four door sedan 1 = All wheel ABS 2 = Luxury car 2 = Rear wheel ABS 3 = Compact truck 3 = No ABS 4 = Full size truck 5 = Sports car If these two variables are to be included in a regression model, how many additional variables will be needed? A) The model explains nearly 63 percent of the variation in the dependent variable. B) If tested at the 0.05 significance level, the overall model would be considered statistically significant. C) The variable X1 has a slope coefficient that is significantly different from zero if tested at the 0.05 level of significance. D) All of the above are true.

Q: Which of the following statements is true? A) Dummy variables are used to incorporate categorical variables into a regression model. B) You should use one fewer dummy variables than are categories for the qualitative variable in question. C) It is appropriate to compute a correlation coefficient for the relationship between a dependent variable and a dummy variable. D) All of the above are true.

Q: If a decision maker wishes to develop a regression model in which the University Class Standing is a categorical variable with 5 possible levels of response, then he will need to include how many dummy variables? A) 5 B) 4 C) 1 D) 3

Q: In a multiple regression analysis involving 15 independent variables and 200 observations, SST = 800 and SSE = 240. The adjusted coefficient of determination is A) 0.15 B) 0.50 C) 0.66 D) 0.70

Q: A multiple regression is shown for a data set of yachts where the dependent variable is the price in thousands of dollars. Given this information, which is correct regarding the test of the overall model using the 0.10 level of significance? A) The overall model does not have significant ability to predict the price of a yacht because p-value = .163 is greater than 0.10 B) The overall model has significant ability to predict the price of a yacht because p-value = 0.163 is greater than 0.10 C) The overall model does not have significant ability to predict the price of a yacht because p-value = .001 is less than 0.10 D) The overall model has significant ability to predict the price of a yacht because p-value = .001 is less than 0.10

Q: A multiple regression is shown for a data set of yachts where the dependent variable is the price in thousands of dollars.Given this information, what is the null hypothesis for testing the overall model?

Q: A multiple regression is shown for a data set of yachts where the dependent variable is the price in thousands of dollars. Given this information, which of the following is true regarding the slope coefficient for Age, where Age represents how many years old the yacht is? A) On average the price of the yacht falls by $1.778 per year. B) On average the yacht is 1.778 years older per $1000 price change. C) On average the price of the yacht falls by $1778 per year. D) On average the price of the yacht increases by $1778 per year.

Q: A multiple regression is shown below for a data set of yachts where the dependent variable is the price of the boat in thousands of dollars. Given this information, what percentage of variation in the dependent variable is explained by the regression model? A) Approximately 68 percent B) About 83 percent C) About 37 percent D) About 60 percent

Q: Which of the following statements is true? A) If the confidence interval estimate for the regression slope coefficient, based on the sample information, crosses over zero, the true population regression slope coefficient could be zero. B) R-square will tend to be smaller than the adjusted R-squared values when insignificant independent variables are included in the model. C) The y-intercept will usually be negative in a multiple regression model when the regression slope coefficients are predominately positive. D) None of the above

Q: Which of the following regression output values is used in computing the variance inflation factors? A) The standard error of the estimate B) The regression intercept value C) The F critical value from the F distribution for the appropriate number of degrees of freedom and the appropriate level of significance D) The R-squared value

Q: Under what circumstances does the variance inflation factor signal that multicollinearity may be a problem? A) When the value of VIF exceeds the size of the sample from which the regression model was developed B) When the VIF value is approximately 1.0 C) When the VIF is greater than or equal to 5 D) When the VIF is a negative value

Q: Explain what is meant by the term least squares regression model.

Q: What factors are of importance to an analyst when linear regression analysis is used for descriptive purposes?

Q: The Public Utility Commission in a southern state is interested in describing the relationship between household monthly utility bills and the size of the house. A recent study of 30 randomly selected household resulted in the following regression results:Based on the information provided, indicate what, if any, conclusions can be reached about the relationship between utility bill and the size of the house in square feet.

Q: A national job placement company is interested in developing a model that might be used to explain the variation in starting salaries for college graduates based on the college GPA. The following data were collected through a random sample of the clients with which this company has been associated. GPA Starting Salary 3.20 $35,000 3.40 $29,500 2.90 $30,000 3.60 $36,400 2.80 $31,500 2.50 $29,000 3.00 $33,200 3.60 $37,600 2.90 $32,000 3.50 $36,000 Based on this sample information, determine the least squares regression model, determine what percent of the variation in starting salaries is explained by GPA, and test to determine whether the regression model is statistically significant at the 0.05 level of significance. Also, develop a scatter plot of the data and locate the regression line on the scatter plot.

Q: Explain why it is important to construct scatter plots prior to conducting regression analysis.

Q: Explain what the correlation coefficient measures and some detail of the key issues associated with it. Be sure to also discuss the concept of spurious correlation.

Q: A random sample of two variables, x and y, produced the following observations: x y 19 7 13 9 17 8 9 11 12 9 25 6 20 7 17 8 Test to determine whether the population correlation coefficient is negative. Use a significance level of 0.05 for the hypothesis test. A) Because t = -4.152 < -1.9432, reject the null hypothesis. Because the null hypothesis is rejected, the sample data does support the hypothesis that there is a negative linear relationship between x and y. B) Because t = -4.152 < -1.9432, do not reject the null hypothesis. Because the null hypothesis is not rejected, the sample data support the hypothesis that there is a negative linear relationship between x and y. C) Because t = -9.895 < -1.9432, reject the null hypothesis. Because the null hypothesis is rejected, the sample data does support the hypothesis that there is a negative linear relationship between x and y. D) Because t = -9.895 < -1.9432, do not reject the null hypothesis. Because the null hypothesis is not rejected, the sample data support the hypothesis that there is a negative linear relationship between x and y.

Q: A random sample of two variables, x and y, produced the following observations: x y 19 7 13 9 17 8 9 11 12 9 25 6 20 7 17 8 Compute the correlation coefficient for these sample data. A) -0.9707 B) -0.2141 C) 0.5133 D) 0.8612

Q: The following data for the dependent variable, y, and the independent variable, x, have been collected using simple random sampling: x y 10 120 14 130 16 170 12 150 20 200 18 180 16 190 14 150 16 160 18 200 Compute the correlation coefficient. A) 0.52 B) 0.71 C) 0.62 D) 0.89

Q: An industry study was recently conducted in which the sample correlation between units sold and marketing expenses was 0.57. The sample size for the study included 15 companies. Based on the sample results, test to determine whether there is a significant positive correlation between these two variables. Use an alpha = 0.05 A) Because t = 2.50 > 1.7709, do not reject the null hypothesis. There is not sufficient evidence to conclude there is a positive linear relationship between sales units and marketing expense for companies in this industry. B) Because t = 2.50 > 1.7709, reject the null hypothesis. There is sufficient evidence to conclude there is a positive linear relationship between sales units and marketing expense for companies in this industry. C) Because t = 3.13 > 1.7709, do not reject the null hypothesis. There is not sufficient evidence to conclude there is a positive linear relationship between sales units and marketing expense for companies in this industry. D) Because t = 3.13 > 1.7709, reject the null hypothesis. There is sufficient evidence to conclude there is a positive linear relationship between sales units and marketing expense for companies in this industry.

Q: If a residual plot exhibits a curved pattern in the residuals, this means that: A) the errors are not normally distributed. B) there must be a curvilinear relation between x and y. C) there is no significant relation between x and y. D) there is a problem with constant variance.

Q: Which of the following is a correct interpretation for the regression slope coefficient? A) For a one-unit change in y, we can expect the value of the independent variable to change by b1 units on average. B) For each unit change in x, the dependent variable will change by b1 units. C) The average change in y of a one-unit change in x will be b1 units. D) The average change in x of a one-unit change in y will be b1 units.

Q: Which of the following statements is true? A) When using a simple linear regression analysis model for prediction purposes, the potential error in the forecast will be less when the value of x used to forecast y is closer to . B) The accuracy of the regression forecast is improved if the standard error for the regression slope coefficient is reduced. C) The use of regression analysis as a means of predicting the value for a dependent variable is not impacted by sampling error since the regression model uses all sample data to arrive at the regression model. D) None of the above

Q: If you were going to develop a scatter plot for the purpose of determining whether one of the assumptions of the regression model is being satisfied, which of the following is true? A) The plot should illustrate a bell-shaped distribution to show that the residuals are normally distributed. B) The horizontal axis should show the fitted values for the dependent variable. C) The plot should illustrate a cone shaped look. D) The points should fall in a straight line.

Q: Residual analysis is conducted to check whether regression assumptions are met. Which of the following is not an assumption made in simple linear regression? A) Errors are independent of each other. B) Errors are normally distributed. C) Errors are linearly related to x. D) Errors have constant variance.

Q: In analyzing the residuals to determine whether the simple regression analysis satisfies the regression assumptions, which of the following is true? A) The histogram of the residuals should be approximately bell-shaped. B) The scatter plot of the residuals against the dependent variable should illustrate that the variation in residuals is the same over all levels of . C) Neither A nor B are true. D) Both A and B are true.

Q: A study was recently performed by the Internal Revenue Service to determine how much tip income waiters and waitresses should make based on the size of the bill at each table. A random sample of bills and resulting tips were collected. These data are shown as follows: Total Bill Tip $126 $19 $58 $11 $86 $20 $20 $3 $59 $14 $120 $30 $14 $2 $17 $4 $26 $2 $74 $16 Based upon these data, what is the approximate predicted value for tips if the total bill is $100? A) $15.55 B) $20.61 C) $26.03 D) $12.88

Q: Which of the following statements is true? A) The interval estimate for predicting a particular value of y given a specific x will be narrower than the interval estimate for the average value of y given a particular x. B) The higher the r-square value, the wider will be the prediction interval based on a simple linear regression model. C) The prediction interval generated from a simple linear regression model will be narrowest when the value of x used to generate the predicted y value is close to the mean value of x. D) The prediction interval generated from a simple linear regression model will be widest when the value of x used to generate the predicted y value is close to the mean value of x.

Q: Assume that you have calculated a prediction of = 110 where the specific value for x is equal to the average value of x. Also assume that n = 201 and that the standard error of the estimate is sε = 4.5. Find the approximate 95 percent prediction interval. A) About 101 ----- 119 B) About 109.4 ----- 110.6 C) About 105.5 ----- 104.5 D) About 98.4 ----- 121.6

Q: It is believed that number of people who attend a Mardi Gras parade each year depends on the temperature that day. A regression has been conducted on a sample of years where the temperature ranged from 28 to 64 degrees and the number of people attending ranged from 8400 to 14,600. The regression equation was found to be = 2378 + 191x. Which of the following is true? A) The average change in parade attendance is an additional 2378 people per one-degree increase in temperature. B) The average change in parade attendance is an additional 191 people per one-degree increase in temperature. C) If the temperature is 75 degrees, we can expect that 16,703 people will attend. D) If the temperature is 0 degrees this year, then we should expect 2378 people to attend.

Q: Which of the following statements is true? A) If the confidence interval estimate for the regression slope coefficient crosses over zero, the average change in y for a one-unit change in x may be zero. B) If the regression slope coefficient is very close to zero, this means that the relationship between the x and y variables is statistically insignificant. C) A statistically significant regression slope coefficient indicates that for a one-unit change in y there will be a positive change in x by an amount equal to the regression slope coefficient. D) It is acceptable to make predictions for y using x values that are outside the range of the data that was used in the regression.

Q: The following regression output was generated based on a sample of utility customers. The dependent variable was the dollar amount of the monthly bill and the independent variable was the size of the house in square feet. Based on this regression output, what is the 95 percent confidence interval estimate for the population regression slope coefficient? A) Approximately -0.0003 ----- +0.0103 B) About -0.0082 ----- +0.0188 C) Approximately -32.76 ----- +32.79 D) None of the above

Q: The following regression output was generated based on a sample of utility customers. The dependent variable was the dollar amount of the monthly bill and the independent variable was the size of the house in square feet. Based on this regression output, which of the following statements is not true? A) The number of square feet in the house explains only about 2 percent of the variation in the monthly power bill. B) At an alpha level equal to 0.05, there is no basis for rejecting the hypothesis that the slope coefficient is equal to zero. C) The average increase in the monthly power bill is about 66.4 for each additional square foot of space in the house. D) The correlation of the monthly power bill with the square footage of the house is 0.149

Q: The National Football League has performed a study in which the total yards gained by teams in games was used as an independent variable to explain the variation in total points scored by teams during games. The points scored ranged from 0 to 57 and the yards gained ranged from 187 to 569. The following regression model was determined: = 12.3 + .12x Given this model, which of the following statements is true? A) The average points scored for teams who gain zero yards during a game is -12.3 points. B) The average yards gained will increase by .12 for every additional point scored. C) The average change in points scored for each increase of one yard will be 0.12 D) The average number of points scored per game is 12.3