EBK BUSINESS STATISTICS
7th Edition
ISBN: 8220102743984
Author: STEPHAN
Publisher: PEARSON
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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.
The following equation is the result of performing a multiple regression analysis:
Job performance = 10 + (5*job knowledge) + (0.7* conscientiousness), where job
knowledge is measured on a scale of 0-5 and conscientiousness is measured on a
scale of 0 to 100. Which of the following conclusions is correct?
!!
O If a person scored 5 on job knowledge and 100 on conscientiousness he or she
would have the maximum predictive score possible
If a person scored 0 on both job knowledge and conscientiousness, his or her
predictive score is 0
ONeither job knowledge nor conscientiousness predicts performance
O Conscientiousness is less important than job knowledge.
Question 3!
For a measuring tool to be usefulitmus be
Suppose that a researcher studying the weight of female college athletes wants to predict the weights based on height, measured in inches, and the percentage of body fat of an athlete. The researcher calculates the regression equation as (weight) = 3.544*(height) + 1.064*(percent body fat) - 87.076. If a female athlete is 64 inches tall and has a 21 percentage of body fat, what is her expected weight?
Question 24 options:
1)
55.444
2)
We do not know the observations in the data set, so we cannot answer that question.
3)
162.084
4)
249.16
5)
336.236
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- Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?arrow_forwardWhat does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?arrow_forwardDoes Table 1 represent a linear function? If so, finda linear equation that models the data.arrow_forward
- Table 2 shows a recent graduate’s credit card balance each month after graduation. a. Use exponential regression to fit a model to these data. b. If spending continues at this rate, what will the graduate’s credit card debt be one year after graduating?arrow_forwardThe 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. What proportion of variation in the number of complaints can be explained by hourly wages? From the results shown above, write the regression equation If wages were increased by $1.00, what is the expected effect on the number of complaints received per employee?arrow_forwardwhat is the dependent and independent variables. fully write out the regression equation. fill in the missing values ‘*’, ‘**’, ‘***’and ‘****’ then test whether ᵟ is significant. give reason for your answer.arrow_forward
- 9. A wildlife researcher is interested in predicting an alligator’s weight (in pounds) based on its length (in inches). Data was obtained from a large random sample of alligators, and the regression equation turned out as follows: Predicted weight = –393 + 5.9 (length) Which one of the following statements is a correct interpretation of this equation? 1. As weight increases by one pound, length is predicted to decrease by 393 inches. 2. As length increases by one inch, weight is predicted to increase by 5.9 pounds. 3. As weight increases by one pound, length is predicted to increase by 5.9 inches. 4. As length increases by one inch, weight is predicted to decrease by 393 pounds. 5. Approximately 5.9% of the variability in weight can be explained by the regression equation.arrow_forwardWhat is C,D and E? And how do i calculate it??arrow_forwardPlease help me understand this problem more in depth. A researcher is investigating possible explanations for deaths in traffic accidents. He examined data from 2000 for each of the 52 cities randomly selected in the US. The data included information on the following variables: Deaths: The number of deaths in traffic accidents per city Income: The median income per city As part of his study, he ran the following simple linear regression model attached in photo. Question: Based on the above results, the researcher tested the hypotheses: Ho: B1=0 versus B1 not equal to 0, versus using T test. What do we know about the test statistic of the test? Based on the approximate p-value, what's the conclusion?arrow_forward
- Please help me better understand proble and how to calculate predicted vale of Allen's final exam. In a accounting course, a linear regression equation was computed to predict the final exam score from the score on the midterm exam. The equation of the least-squares regression line was Y= 10 + 0.85X. Y represents the final exam score, and X is the midterm exam score. QUESTION: Suppose Allen scores 83 on the midterm exam. What would be the predicted value of his score on the final exam (assuming no extrapolation error)?arrow_forwardSuppose you are examining a multi-variable linear regression model that was designed to predict the weight of a person, measured in kg, using 3 predictor variables. One of the variables used in this analysis is "height", with the coefficient of this variable being equal to 3.96, with a standard error of the coefficient equal to 1.168. There are 300 datapoints in the dataset. Using this information, what would be the test statistic (t-ratio) for the test to see if the variable "height" is significant? Only round final answer. Round to two decimal places.arrow_forwardA real estate company wants to study the relationship between house sales prices and some important predictors of sales prices. Based on data from recently sold homes in the space, the following variables are used in a multiple regression model. y = sales price (in thousands of dollars) x₁ = total floor area (in square feet) x₂ = number of bedrooms x3 distance to nearest high school (in miles) = The estimated model is as follows. =76+0.098x₁ +16x₂ - 8x3 Answer the questions below for the interpretation of the coefficient of X₂ in this model. (a) Holding the other variables fixed, what is the average change in sales price for each additional bedroom in a house? dollars (b) Is this change an increase or a decrease? O increase O decrease Xarrow_forward
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