What value of F is critical at the 0.01 level? 2.13 0.27 0.47 3.69 none of the above
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The MBA program surveyed the catastrophe that was his fall 2017 intake. His 257 admitted students had performed miserably and he needed to determine why. He built a regression model to predict their overall class average after their first semester based on these factors that weighed heavily in their admission process: undergraduate GPA, GMAT score, years of professional employment, shoe size, TOEFL score, and 40 yard dash. What value of F is critical at the 0.01 level?
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2.13 |
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0.27 |
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0.47 |
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3.69 |
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none of the above |
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- The datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…Consider the following passage: I ran a regression, with many variables to predict the result of another variable which was the murder rate. One can see that lots of things, can cause the murder rate to increase or decrease. I tried to account for all the important factors, and those factors are the SAT scores, unemployment rate, and international migration per 1,000. The SAT score is, average combined total score participants did on the SAT exam. The unemployment rate is, "a measure of the prevalence of unemployment and it is calculated as a percentage by dividing the number of unemployed individuals by all individuals currently in the labor force." (Wikipedia) International migration per 1,000 is, the number of people who come into a state from other countries per 1,000 people who live in the state. After I run the regression I will look at the t scores and p values and I should hopefully conclude that international migration does not cause crime. Which writing mistakes, if any, did…A major brokerage company has an office in Miami, Florida. The manager of the office is evaluated based on the number of new clients generated each quarter. Data were collected that show the number of new customers added during each quarter between 2015 and 2018. A multiple regression model was developed with the number of new customers as the dependent and the following four independent variables: Period (1, …, 16): A variable that measures the trend; Q1 = 1 for first quarter, Q1 = 0 otherwise; Q2 = 1 for second quarter, Q2 = 0 otherwise; Q3 = 1 for third quarter, Q3 = 0 otherwise. Questions: 1. Explain each of the four slopes (Period, Q1, Q2, Q3). 2. How many new customers would you expect in the second quarter of the following year (2019)?
- The U.S. Postal Service is attempting to reduce the number of complaints made by the public against its workers. To facilitate this task, a staff analyst for the service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year. The analyst ran a simple linear regression in SPSS. The results are shown below. The current minimum wage is $5.15. If an employee earns the minimum wage, how many complaints can that employee expect to receive? Is the regression coefficient statistically significant? How can you tell?A logistic regression was used to investigate obesity and poor physical health while controlling for the following variables: age, gender, race, income, health status, education, current smoker, and diet/exercise status. Justify the use of a logistic regression.Two new variables, the market value of the firm (a measure of firm size, in millions of dollars) and stock return (a measure of firm performance, in percentage points), are added to the regression: In(Earnings) = 3.86 – 0.28Female + 0.37In(MarketValue) + 0.004Return, (0.03) (0.04) (0.004) (0.003) n = 46,670, R = 0.345. If MarketValue increases by 1.88%, what is the increase in earnings? If MarketValue increases by 1.88%, earnings increase by 0.70 % (Round your response to two decimal places.) The coefficient on Female is now – 0.28. Why has it changed from the first regression? O A. Female is correlated with the two new included variables. O B. MarketValue is important for explaining In(Earnings). O C. The first regression suffered from omitted variable bias. OD. All of the above. Assume that the coefficient estimated in the second regression is correct. Forget about the effect of the Return variable, whose effect seems small and statistically insignificant. Calculate the correlation…
- Hiroshi Sato, an owner of a sushi restaurant in San Francisco, has been following an aggressive marketing campaign to thwart the effect of rising unemployment rates on business. He used monthly data on sales ($1,000s), advertising costs ($), and the unemployment rate (%) fromJanuary 2008 to May 2009 to estimate the following sample regression equation: Sales(t) = 17.51 +0.05 Advertising Costs(t-1) – 0.70 Unemployment Rate t-1 Requirement: a. Hiroshi had budgeted $620 toward advertising costs in May 2009. Make a forecast in June2009, if the unemployment rate in May 2009 was 9.1%b. What will be the forecast if he raises his advertisement budget to $700?c. Reevaluate the above forecast if the unemployment rate in May 2009 was 9.5%. Please, if possible, can you do the answer in Excel/Spreadsheet3. Describe the problem that outliers present for a regression analysis and outline what you could do to resolve this problem.You are analyzing a dataset containing 379 datapoints, and want to use 13 predictor variables to create a multiple variable linear regression model of the data. You conduct an ANOVA analysis, and yield a R² of 38%. Using this information, what would be the F statistic of your analysis?
- An e-commerce retailer was trying to understand how customer engagement correlates to customer purchases. They built the following regression model: Probability of Purchase = 0.1619 + 0.07 Emails Opened. If someone opens 5 emails how likely are they to purchase? If someone has a 75% chance of purchase how many emails have they opened? If someone opens no emails how likely are they to purchase? What is the slope here and what does it tell us?Consider the following passage: I ran a regression, with many variables to predict the result of another variable which was the murder rate. One can see that lots of things, can cause the murder rate to increase or decrease. I tried to account for all the important factors, and those factors are the SAT scores, unemployment rate, and international migration per 1,000. The SAT score is, average combined total score participants did on the SAT exam. The unemployment rate is, "a measure of the prevalence of unemployment and it is calculated as a percentage by dividing the number of unemployed individuals by all individuals currently in the labor force." (Wikipedia) International migration per 1,000 is, the number of people who come into a state from other countries per 1,000 people who live in the state. After I run the regression I will look at the t scores and p values and I should hopefully conclude that international migration does not cause crime. Which writing mistakes, if any, did…One of the biggest changes in higher education in recent years has been the growth of online universities. The Online Education Database is an independent organization whose mission is to build a comprehensive list of the top accredited online colleges. The following table shows the retention rate (%) and the graduation rate (%) for 29 online colleges.a). Use Excel Data Analysis Tool – Regression to get the relationship between the two variables;b). Create a scatter diagram for the two variables and display regression equation and R square on chart, then explain the relationship between the variables;c). Did the estimated regression equation provide a good fit?d). Suppose you were the president of South University. After reviewing the results, would you be able to use the regression result for forecasting. College RR(%) GR(%) Western International University 7 25 South University 51 25 University of Phoenix 4 28 American InterContinental University 29 32 Franklin…
