The following estimated regression equation relating sales to inventory investment and advertising expenditures was given. ý = 28 + 15x, + 8x2 The data used to develop the model came from a survey of 10 stores; for those data, SST = 17,000 and SSR = 13,260. (a) For the estimated regression equation given, compute R2. R2 = 0.78 2 (b) Compute R. (Round your answer to two decimal places.) (c) Does the model appear to explain a large amount of variability in the data? Explain. (For purposes of this exercise, consider an amount large if it is at least 55%. Round your answer to the near The adjusted coefficient of determination shows that 78 % of the variability has been explained by the two independent variables; thus, we conclude that the model does Select- does

The following estimated regression equation relating sales to inventory investment and advertising expenditures was given. ý = 28 + 15x, + 8x2 The data used to develop the model came from a survey of 10 stores; for those data, SST = 17,000 and SSR = 13,260. (a) For the estimated regression equation given, compute R2. R2 = 0.78 2 (b) Compute R. (Round your answer to two decimal places.) (c) Does the model appear to explain a large amount of variability in the data? Explain. (For purposes of this exercise, consider an amount large if it is at least 55%. Round your answer to the near The adjusted coefficient of determination shows that 78 % of the variability has been explained by the two independent variables; thus, we conclude that the model does Select- does

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter4: Linear Functions

Section: Chapter Questions

Problem 38RE: For the following exercises, consider the data in Table 5, which shows the percent of unemployed in...

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The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.
For the following exercises, consider the data in Table 5, which shows the percent of unemployed in a city of people 25 years or older who are college graduates is given below, by year. 38. Determine whether the trend appears to be linear.If so, and assuming the trend continues, find alinear regression model to predict the percent of unemployed in a given year to three decimal places.
For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or older who are college graduates in a particular city, by year. Determine whether the trend appears linear. If so, and assuming the trend continues, find a linear regression model to predict the percent of unemployed in a given year to three decimal places.
For the following exercises, consider the data in Table 5, which shows the percent of unemployed in a city ofpeople25 years or older who are college graduates is given below, by year. 41. Based on the set of data given in Table 7, calculatethe regression line using a calculator or othertechnology tool, and determine the correlationcoefficient to three decimal places.
For the following exercises, consider this scenario: The profit of a company decreased steadily overa ten-year spam.The following ordered pairs shows dollars and the number of units sold in hundreds and the profit in thousands ofover the ten-year span, (number of units sold, profit) for specific recorded years: (46,600),(48,550),(50,505),(52,540),(54,495). Use linear regression to determine a function Pwhere the profit in thousands of dollars depends onthe number of units sold in hundreds.
For the following exercises, consider this scenario: The population of a city increased steadily over a ten-year span.The following ordered pairs show the population and the year over the ten-year span (population, year) for specific recorded years: (3,600,2000);(4,000,2001);(4,700,2003);(6,000,2006) 42. Use linear regression to determine a function y,where the year depends on the population, to threedecimal places of accuracy.
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?
The personnel director for Electronics Associates developed the following estimated regression equation relating an employee's score on a job satisfaction test to his or her length of service and wage rate.y-hat = 14.4 - 8.69x1 +13.5x2 where: x1 = length of service (years)x2 = wage rate (dollars)y = job satisfaction test score (higher scoresindicate greater job satisfaction)Round your answers to 2 decimal places. a. Interpret the coefficients in this estimated regression equation. If the wage rate does not change, a one-year increase in length of service is associated with - Select your answer - in job satisfaction score by _______________ units. If the length of service does not change, a dollar increase in wage results in - Select your answer - in job satisfaction score by _______________ units. b. Predict the job satisfaction test score for an employee who has four years of service and makes $6.50 per hour. _________________
The personnel director for Electronics Associates developed the following estimated regression equation relating an employee's score on a job satisfaction test to length of service and wage rate. ŷ = 14.4 − 8.69x1 + 13.52x2 where x1 = length of service (years) x2 = wage rate (dollars) y = job satisfaction test score (higher scores indicate greater job satisfaction). #1) Find the value of the test statistic. (Round your answer to two decimal places.) F = Find the p-value. (Round your answer to three decimal places.) p-value = #2)Did the estimated regression equation provide a good fit to the data? Explain. (Round your answers to two decimal places.) Since R2 = Ra2 = #3) Find the value of the test statistic for ?2. (Round your answer to two decimal places.) Find the p-value. (Round your answer to three decimal places.) p-value =
The admissions officer for a certain college developed the following estimated regression equation relating the final college GPA to the student's SAT mathematics score and high school GPA. ŷ = −1.39 + 0.0234x1 + 0.00482x2 where x1 = high-school grade point average x2 = SAT mathematics score y = final college grade point average. #1)A high-school average 84 corresponds to x1 = 84 and a score of 535 on the SAT mathematics test corresponds to x2 = 535. Substitute these values into the estimated regression equation to find the final college GPA, rounding the result to two decimal places. GPA = −1.39 + 0.0234x1 + 0.00482x2 = -1.39 +0.0234 (_____________) + 0.00482 (535) = __________________
The admissions officer for a certain college developed the following estimated regression equation relating the final college GPA to the student's SAT mathematics score and high school GPA. ŷ = −1.39 + 0.0234x1 + 0.00482x2 where x1 = high-school grade point average x2 = SAT mathematics score y = final college grade point average. #1) The given regression equation follows where x1 is the high-school grade point average, x2 is the SAT mathematics score, and y is the final college grade point average. ŷ = −1.39 + 0.0234x1 + 0.00482x2 We are to predict the final college GPA for a student who has a high-school average of 84 and scored 535 on the SAT mathematics test. Thus, the corresponding values to substitute into the regression equation are x1 = and x2 = .
The following table displays the mathematics test scores for a random sample of college students, along with their final SY16C grades. a. Fit the regression line y = a+bx to the data and interpret the results. b. Use the regression equation to determine the SY16C grade for a college student who scored60 on their achievement test. What would their SY16C grade be?