The worker has noticed that the more time he spends at work (x), the less money he is likely to make (y) in conducting transactions for his firm. Which of the regression equations MOST suggests such a possibility?
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.The worker has noticed that the more time he spends at work (x), the less money he is likely to make (y) in conducting transactions for his firm. Which of the regression equations MOST suggests such a possibility?
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- The term 'simple' in simple linear regression refers to the fact that a. the dependent variable is dichotomous b. there are multiple dependent and independent variables c. there is one independent variable d. there is more than one independent variable e. there are no independent variables1. An analyst hired by the multinational company to study the sales data of its more than 75 stores worldwide. Used regression analysis to predict $ sales (y) by using $ advertising (x1) and $ salary of sales representatives (x2) across all the branches. You obtained the following regression function: y = 7800 + 8.5x1-1.6x2. If the advertising budgets of one of the branches of the corporation is now $45,000 (which is 10% more than before) and the salary of sales representatives is now $8,500 (which is 20% less than before), then the predicted sales before in that branch is a. $417,750 b. $341,250 c. $376,700 d. $335,650A well-known university is interested in how salary (in thousands of dollars) is predicted from years of service for faculty and administrative staff. Below are the estimated regression equations.Faculty (n = 170): ŷ = 60 + 1.1xAdmin. (n = 155): ŷ = 57 + 1.5x a) How much would a faculty member be earning after 5 years of service? b) In how many years will an administrator earn the same amount as in a)?
- A well-known university is interested in how salary (in thousands of dollars) is predicted from years of service for faculty and administrative staff. Below are the estimated regression equations. Faculty (n = 193): ŷ = 50 + 2.1xAdmin. (n = 179): ŷ = 47 + 2.3x a) How much would a faculty member be earning after 10 years of service? b) In how many years will an administrator earn the same amount as in a)?A particular professor has noticed that the number of people, P, who complain about his attitude is dependent on the number of cups of coffee, n, he drinks. From eight days of tracking he compiled the following data: Cups of coffee (n) 1 1 2 3 4 5 5 People (P) 12 10 10 6 6 4 2 3 (a) Using regression to find a linear equation for P(n). Round to 2 decimal places. P(n)= (b) How many cups of coffee should he drink so that no one will complain about his attitude? Round to one decimal place.Suppose you are interested in the relationship between educational attainment (measured by years of completed schooling) and the how long people live (measured by age at death in years). For a sample of 100 recently-deceased people, you collect information on their educational attainment and their age at death. Treating educational attainment as the independent variable (X) and age at death as the dependent variable (Y), you compute the following regression equation. Y = 55 + 2.0X + e a) Interpret in words this equation. That is, what do each of the numerical values in the equation tell us? Be specific. b) How long would you predict that someone with a college education (that is, someone with 16 years of schooling) will live?
- In a linear regression model, the dependent variable is "Final exam score (%) for WPC 300" and the independent variable is "Hours studied". A coefficient of 4 could be interpreted as for every one % increase in the final exam score, the expected hours of study is 4. four hours of additional study, the expected increase in the final exam score is 1%. four hours of additional study, the final exam score is expected to increase by 4%. hour of additional study, the expected final exam score increases by 4%.1. Develop a simple linear regression equation for starting salaries using an independent variable that has the closest relationship with the salaries. Explain how you chose this variable.Recent research into the cost of various medical procedures has shown the impact of certain complications encountered in surgery on the total cost of a patient’s stay in the hospital. The researchers used regression analysis and found the following results: Total cost for patient = Constant, plus a × Length of stay (measured in days), plus b × Presence of one or more complications (= 1 if true, 0 if false), plus c × Use of a laparoscope (= 1 if true, 0 if false) where a, b, c are coefficients of the regression model. The laparoscope is an instrument somewhat like a miniature telescope with a fiber-optic system that brings light into the abdomen. It is about as big around as a fountain pen and twice as long. The following table shows additional regression results presented by the researchers in the study described above. There are two regressions. The right-hand column shows the results for all patients, including those treated with laparoscopic surgery. The left-hand column…
- In a simple regression problem, r and B O may have opposite signs. O must have the same sign. O must have opposite signs. O are equal.A 1 ROAA (%) Efficiency Ratio (%) 2 1.04 39.93 3 57.75 4 81.4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0.68 7.27 1.08 0.72 0.92 0.79 1.04 1.76 1.07 1.37 0.93 0.66 1.72 1.5 0.59 2.12 1.11 1.45 1.06 B A 53.49 71.08 65.41 68.07 68.14 68.1 64.82 48.58 63.1 59.16 49.93 54.7 81.6 75.21 69.82 49.47 57.09 с Total Risk-Based Capital (%) 17.04 13.88 27.77 18.31 14.66 14.04 13.38 16.8 16.69 13.86 12 18.65 19.76 17.69 26.6 15.08 14.55 17.5 16.03 14.62 D E F G H |Johnson Filtration, Inc. provides maintenance service for water-filtration systems. Suppose that in addition to information on the number of months since the machine was serviced and whether a mechanical or an electrical repair was necessarY, the managers obtained a list showing which repairperson performed the service. The revised data follow. Click on the datafile logo to reference the data. DATA file Repair Time Months Since in Hours Last Service Type of Repair Repairperson 2.9 Electrical Dave Newton 3.0 Mechanical Dave Newton 4.8 8. Electrical Bob Jones 1.8 3 Mechanical Dave Newton 2.9 2 Electrical Dave Newton 4.9 Electrical Bob Jones 4.2 6. Mechanical Bob Jones 4.8 8. Mechanical Bob Jones 4.4 Electrical Bob Jones 4.5 Electrical Dave Newton a. Ignore for now the months since the last maintenance service (1) and the repairperson who performed the service. Develop the estimated simple linear regression equation to predict the repair time (y) given the type of repair (2 ). Recall that…