1 (1 point) A marketing research firm wanted to determine whether an individual's favored Social Network and Age Group were related. For age they grouped by 8-17, 18-34, 35-55, and 55+ and the social networks they investigated were Facebook, Google+, and Twitter. The table below presents their findings. Identify the correct conclusion using the 0.05 level of significance. Question 1 options: 1) We determine that Social Network and Age Group are dependent on one another, based on a p-value of 1.0000 2) We find that Social Network and Age Group are not related to one another, based on a p-value of 0.0000 3) We conclude that Social Network and Age Group are associated with each other, based on a p-value of 0.0000 4) We decide that Social Network and Age Group are independent of one another, based on a p-value of 1.0000 5) There is not enough information to make a conclusion. Save Question 2 (1 point) You notice that the type of phone someone has and the computer operating system they use seems to be related. Based on a sample, you make the following tabulation. Is there a relationship between phone type and computer operating system choices? What is the appropriate conclusion at the 0.05 level of significance? Question 2 options: 1) We did not find significant evidence that Phone and Computer OS are not related to each other, based on a p-value of 0.9299 2) There are two correct answers. 3) We did not find significant evidence that Phone and Computer OS are related to each other, based on a p-value of 0.0701 4) We determine that Phone and Computer OS are independent of one another, based on a p-value of 0.0701 5) We determine that Phone and Computer OS are dependent on one another, based on a p-value of 0.9299 Save Question 3 (1 point) You notice that the type of phone someone has and the computer operating system they use seems to be related. Based on a sample, you make the following tabulation. To test at the 0.01 level to determine if Phone and Computer OS are dependent, calculate the chi-square test statistic and p-value. Question 3 options: 1) 2.929, the degrees of freedom is 4, and the p-value is 0.7953. 2) None of the other options are fully correct. 3) 5.927, the degrees of freedom is 4, and the p-value is 0.0100. 4) 5.927, the degrees of freedom is 4, and the p-value is 0.2047. 5) 5.927, the degrees of freedom is 9, and the p-value is 0.7472. Save Question 4 (1 point) Suppose the Federal Aviation Administration (FAA) would like to compare the on-time performances of different airlines on domestic, nonstop flights. The following table shows three different airlines and the frequency of flights that arrived early, on-time, and late for each. To test at the 0.1 level to determine if Airline and Status are dependent, calculate the chi-square test statistic and p-value. Question 4 options: 1) 45.307, the degrees of freedom is 9, and the p-value is 0.0000. 2) 45.307, the degrees of freedom is 4, and the p-value is 0.0000. 3) None of the other options are fully correct. 4) 45.307, the degrees of freedom is 4, and the p-value is 0.1000. 5) 28.676, the degrees of freedom is 4, and the p-value is 1.0000. Save Question 5 (1 point) Suppose Best Buy would like to know if the age of a customer affects the brand of digital camera he or she purchases to help design a new promotional campaign. The data will show the frequency with which people, categorized by their age groups, purchased various camera brands. To determine if Camera Brand and Age Group are dependent, what are the appropriate hypotheses? Question 5 options: 1) HO: Camera Brand and Age Group are independent of one another. HA: Camera Brand and Age Group display a positive correlation. 2) HO: Camera Brand and Age Group are independent of one another. HA: Camera Brand and Age Group display a negative correlation. 3) There is not enough information to choose the correct set of hypotheses. 4) HO: Camera Brand and Age Group are not related to one another. HA: Camera Brand and Age Group are related to each other. 5) HO: Camera Brand and Age Group are related to one another. HA: Camera Brand and Age Group are independent of each other. Save Question 6 (1 point) A political poll asked potential voters if they felt the economy was going to get worse, stay the same, or get better during the next 12 months. The party affiliations of the respondents were also noted. To determine if Party Affiliation and Response are dependent, what are the appropriate hypotheses? Question 6 options: 1) HO: Party Affiliation and Response are not related to one another. HA: Party Affiliation and Response display a negative correlation. 2) HO: Party Affiliation and Response are associated with each other. HA: Party Affiliation and Response are independent of each other. 3) HO: Party Affiliation and Response are independent of one another. HA: Party Affiliation and Response are related to one another. 4) HO: Party Affiliation and Response are not related to one another. HA: Party Affiliation and Response display a positive correlation. 5) There is not enough information to choose the correct set of hypotheses. Save Question 7 (1 point) Choose the value of the Pearson's Correlation Coefficient (r) that best describes the two plots. Question 7 options: 1) I: 1.088, II: -0.21. 2) I: 0.088, II: 0.21. 3) I: -0.088, II: -0.21. 4) I: -0.088, II: 1.21. 5) I: -0.21, II: -0.088. Save Question 8 (1 point) Choose the value of the Pearson's Correlation Coefficient (r) that best describes the two plots. Question 8 options: 1) I: 0.289, II: 0.333. 2) I: 0.333, II: 0.711. 3) I: -0.711, II: -0.333. 4) I: 0.711, II: 0.667. 5) I: 0.711, II: 0.333. Save Question 9 (1 point) Composite SAT scores and composite ACT scores are thought to be correlated for students coming into their first year of college. Given the dataset below, calculate the Pearson's correlation coefficient for the dataset below and interpret what that means. Question 9 options: 1) The correlation is -0.883 . There is a strong negative linear association between ACT and SAT . 2) The correlation is -0.883 . There is a strong positive linear association between ACT and SAT . 3) The correlation is 0.883 . There is a strong positive linear association between ACT and SAT . 4) The correlation is -0.883 . There is a weak negative linear association between ACT and SAT . 5) The correlation is 0.883 . There is a strong negative linear association between ACT and SAT . Save Question 1 (1 point) You work for a company in the marketing department. Your manager has tasked you with forecasting sales by month for the next year. You notice that over the past 12 months sales have consistently gone up in a linear fashion, so you decide to run a regression the company's sales history. You find that the regression equation for the data is (sales) = 21.844*(time) + 5.232. Interpret the slope. Question 1 options: 1) When sales increases by 1 unit, time increases by 5.232 months. 2) When time increases by 1 month, sales increases by 21.844 units. 3) We are not given the dataset, so we cannot make an interpretation. 4) When sales increases by 1 unit, time increases by 21.844 months. 5) When time increases by 1 month, sales increases by 5.232 units. Save Question 2 (1 point) Suppose that in a certain neighborhood, the cost of a home is proportional to the size of the home in square feet. If the regression equation quantifying this relationship is found to be (cost) = 88.952*(size) + 678.671, what does the slope indicate? Question 2 options: 1) When cost increases by 1 dollar, size increases by 678.671 square feet. 2) When size increases by 1 square foot, cost increases by 678.671 dollars. 3) When cost increases by 1 dollar, size increases by 88.952 square feet. 4) We are not given the dataset, so we cannot make an interpretation. 5) When size increases by 1 square foot, cost increases by 88.952 dollars. Save Question 3 (1 point) While attempting to measure its risk exposure for the upcoming year, an insurance company notices a trend between the age of a customer and the number of claims per year. It appears that the number of claims keep going up as customers age. If 10 customers are sampled and the regression output is given below, report the regression equation. Question 3 options: 1) (number of claims per year) = 9.06*(age) - 0.004 2) (age) = 9.06*(number of claims per year) - 0.004 3) (number of claims per year) = -0.004*(age) 4) (number of claims per year) = -0.004*(age) + 9.06 5) (age) = -0.004*(number of claims per year) + 9.06 Save Question 4 (1 point) Suppose that for a typical FedEx package delivery, the cost of the shipment is a function of the weight of the package. You find out that the regression equation for this relationship is (cost of delivery) = 3.608*(weight) + 3.227. If a package you want to ship weighs 34.713 ounces, what would you expect to pay for the shipment? Question 4 options: 1) 125.24 2) We do not know the observations in the data set, so we cannot answer that question. 3) 115.63 4) 128.47 5) 8.73 Save Question 5 (1 point) Zagat restaurant guides publish ratings of restaurants for many large cities around the world. The restaurants are rated on a 0 to 30 point scale based on quality of food, decor, service, and cost. Suppose the regression equation that predicts the cost of dinner using the rating of the quality of food for the restaurants in a particular city is (cost of dinner) = 3.045*(food quality) + 2.301. If a restaurant in the city was given a Zagat rating of 13.687 and the average cost of dinner there is $65.796, what is the residual? Question 5 options: 1) -30.291 2) -21.818 3) -52.109 4) 52.109 5) 21.818 Save Question 6 (1 point) You work for a company in the marketing department. Your manager has tasked you with forecasting sales by month for the next year. You notice that over the past 12 months sales have consistently gone up in a linear fashion, so you decide to run a regression the company's sales history. You find that the regression equation for the data is (sales) = 64.079*(time) + 165.424. In April (time = 4) you see the actual sales quantity was 400.074. What is the residual? Question 6 options: 1) -396.074 2) -21.666 3) -417.74 4) 21.666 5) 396.074 Save Question 7 (1 point) While attempting to measure its risk exposure for the upcoming year, an insurance company notices a trend between the age of a customer and the number of claims per year. It appears that the number of claims keep going up as customers age. After performing a regression, they find that the relationship is (claims per year) = 0.395*(age) + 4.283. If a customer is 32.551 years old and they make an average of 8.656 claims per year, the residual is -8.485. Interpret this residual in terms of the problem. Question 7 options: 1) The age is 8.485 years less than than what we would expect. 2) The number of claims per year is 8.485 claims less than than what we would expect. 3) The number of claims per year is 8.485 claims greater than than what we would expect. 4) The age is 8.485 years larger than than what we would expect. 5) The number of claims per year is 8.656 claims less than than what we would expect. Save Question 8 (1 point) Zagat restaurant guides publish ratings of restaurants for many large cities around the world. The restaurants are rated on a 0 to 30 point scale based on quality of food, decor, service, and cost. Suppose the regression equation that predicts the cost of dinner using the rating of the quality of food for the restaurants in a particular city is (cost of dinner) = 4.759*(food quality) + 2.869. If a restaurant in the city was given a Zagat rating of 23.844, and the average cost of dinner there is 187.203, the residual is 70.86. Interpret the residual. Question 8 options: 1) The cost of dinner is 187.203 dollars larger than than what we would expect. 2) The cost of dinner is 70.86 dollars larger than than what we would expect. 3) The Zagat rating is 70.86 points less than than what we would expect. 4) The cost of dinner is 70.86 dollars less than than what we would expect. 5) The Zagat rating is 70.86 points larger than than what we would expect. Save Question 9 (1 point) You work for a company in the marketing department. Your manager has tasked you with forecasting sales by month for the next year. You notice that over the past 12 months sales have consistently gone up in a linear fashion, so you decide to run a regression the company's sales history. If 10 months are sampled and the regression output is given below, what can we conclude about the slope of time? Question 9 options: 1) The slope is 7.79 and therefore differs from 0. 2) The slope is equal to 0. 3) The slope significantly differs from 0. 4) Since we are not given the dataset, we do not have enough information to determine if the slope differs from 0. 5) Not enough evidence was found to conclude the slope differs significantly from 0. Save Question 10 (1 point) Zagat restaurant guides publish ratings of restaurants for many large cities around the world. The restaurants are rated on a 0 to 30 point scale based on quality of food, decor, service, and cost. Suppose that someone wants to predict the cost of dinner at a restaurant in a city based on the Zagat food quality ratings. If 10 restaurants in a city are sampled and the regression output is given below, what can we conclude about the slope of food quality? Question 10 options: 1) The slope is equal to 0. 2) The slope significantly differs from 0. 3) The slope is 1.528 and therefore differs from 0. 4) Not enough evidence was found to conclude the slope differs significantly from 0. 5) Since we are not given the dataset, we do not have enough information to determine if the slope differs from 0. Question 1 (1 point) You work for a company in the marketing department. Your manager has tasked you with forecasting sales by month for the next year. You notice that over the past 12 months sales have consistently gone up in a linear fashion, so you decide to run a regression the company's sales history. You find that the regression equation for the data is (sales) = 21.844*(time) + 5.232. Interpret the slope. Question 1 options: 1) When sales increases by 1 unit, time increases by 5.232 months. 2) When time increases by 1 month, sales increases by 21.844 units. 3) We are not given the dataset, so we cannot make an interpretation. 4) When sales increases by 1 unit, time increases by 21.844 months. 5) When time increases by 1 month, sales increases by 5.232 units. Save Question 2 (1 point) Suppose that in a certain neighborhood, the cost of a home is proportional to the size of the home in square feet. If the regression equation quantifying this relationship is found to be (cost) = 88.952*(size) + 678.671, what does the slope indicate? Question 2 options: 1) When cost increases by 1 dollar, size increases by 678.671 square feet. 2) When size increases by 1 square foot, cost increases by 678.671 dollars. 3) When cost increases by 1 dollar, size increases by 88.952 square feet. 4) We are not given the dataset, so we cannot make an interpretation. 5) When size increases by 1 square foot, cost increases by 88.952 dollars. Save Question 3 (1 point) While attempting to measure its risk exposure for the upcoming year, an insurance company notices a trend between the age of a customer and the number of claims per year. It appears that the number of claims keep going up as customers age. If 10 customers are sampled and the regression output is given below, report the regression equation. Question 3 options: 1) (number of claims per year) = 9.06*(age) - 0.004 2) (age) = 9.06*(number of claims per year) - 0.004 3) (number of claims per year) = -0.004*(age) 4) (number of claims per year) = -0.004*(age) + 9.06 5) (age) = -0.004*(number of claims per year) + 9.06 Save Question 4 (1 point) Suppose that for a typical FedEx package delivery, the cost of the shipment is a function of the weight of the package. You find out that the regression equation for this relationship is (cost of delivery) = 3.608*(weight) + 3.227. If a package you want to ship weighs 34.713 ounces, what would you expect to pay for the shipment? Question 4 options: 1) 125.24 2) We do not know the observations in the data set, so we cannot answer that question. 3) 115.63 4) 128.47 5) 8.73 Save Question 5 (1 point) Zagat restaurant guides publish ratings of restaurants for many large cities around the world. The restaurants are rated on a 0 to 30 point scale based on quality of food, decor, service, and cost. Suppose the regression equation that predicts the cost of dinner using the rating of the quality of food for the restaurants in a particular city is (cost of dinner) = 3.045*(food quality) + 2.301. If a restaurant in the city was given a Zagat rating of 13.687 and the average cost of dinner there is $65.796, what is the residual? Question 5 options: 1) -30.291 2) -21.818 3) -52.109 4) 52.109 5) 21.818 Save Question 6 (1 point) You work for a company in the marketing department. Your manager has tasked you with forecasting sales by month for the next year. You notice that over the past 12 months sales have consistently gone up in a linear fashion, so you decide to run a regression the company's sales history. You find that the regression equation for the data is (sales) = 64.079*(time) + 165.424. In April (time = 4) you see the actual sales quantity was 400.074. What is the residual? Question 6 options: 1) -396.074 2) -21.666 3) -417.74 4) 21.666 5) 396.074 Save Question 7 (1 point) While attempting to measure its risk exposure for the upcoming year, an insurance company notices a trend between the age of a customer and the number of claims per year. It appears that the number of claims keep going up as customers age. After performing a regression, they find that the relationship is (claims per year) = 0.395*(age) + 4.283. If a customer is 32.551 years old and they make an average of 8.656 claims per year, the residual is -8.485. Interpret this residual in terms of the problem. Question 7 options: 1) The age is 8.485 years less than than what we would expect. 2) The number of claims per year is 8.485 claims less than than what we would expect. 3) The number of claims per year is 8.485 claims greater than than what we would expect. 4) The age is 8.485 years larger than than what we would expect. 5) The number of claims per year is 8.656 claims less than than what we would expect. Save Question 8 (1 point) Zagat restaurant guides publish ratings of restaurants for many large cities around the world. The restaurants are rated on a 0 to 30 point scale based on quality of food, decor, service, and cost. Suppose the regression equation that predicts the cost of dinner using the rating of the quality of food for the restaurants in a particular city is (cost of dinner) = 4.759*(food quality) + 2.869. If a restaurant in the city was given a Zagat rating of 23.844, and the average cost of dinner there is 187.203, the residual is 70.86. Interpret the residual. Question 8 options: 1) The cost of dinner is 187.203 dollars larger than than what we would expect. 2) The cost of dinner is 70.86 dollars larger than than what we would expect. 3) The Zagat rating is 70.86 points less than than what we would expect. 4) The cost of dinner is 70.86 dollars less than than what we would expect. 5) The Zagat rating is 70.86 points larger than than what we would expect. Save Question 9 (1 point) You work for a company in the marketing department. Your manager has tasked you with forecasting sales by month for the next year. You notice that over the past 12 months sales have consistently gone up in a linear fashion, so you decide to run a regression the company's sales history. If 10 months are sampled and the regression output is given below, what can we conclude about the slope of time? Question 9 options: 1) The slope is 7.79 and therefore differs from 0. 2) The slope is equal to 0. 3) The slope significantly differs from 0. 4) Since we are not given the dataset, we do not have enough information to determine if the slope differs from 0. 5) Not enough evidence was found to conclude the slope differs significantly from 0. Save Question 10 (1 point) Zagat restaurant guides publish ratings of restaurants for many large cities around the world. The restaurants are rated on a 0 to 30 point scale based on quality of food, decor, service, and cost. Suppose that someone wants to predict the cost of dinner at a restaurant in a city based on the Zagat food quality ratings. If 10 restaurants in a city are sampled and the regression output is given below, what can we conclude about the slope of food quality? Question 10 options: 1) The slope is equal to 0. 2) The slope significantly differs from 0. 3) The slope is 1.528 and therefore differs from 0. 4) Not enough evidence was found to conclude the slope differs significantly from 0. 5) Since we are not given the dataset, we do not have enough information to determine if the slope differs from 0