The following table displays the mathematics test scores for a random sample ofcollege 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 whoscored 60 on their achievement test. What would their SY16C grade be?Mathematics test (x) SY16C grades (y)13965437832152464825792478972873875989.34562.

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Answered: The following table displays the…

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter4: Linear Functions

Section: Chapter Questions

Problem 30PT: For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or...

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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. Based on the set of data given in Table 5, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient. Round to three decimal places of accuracy
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 the data in Table 5, which shows the percent of unemployed ina city of people 25 years or older who are college graduates is given below, by year. 40. Based on the set of data given in Table 6, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient to three decimal places.
An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.
RaceJA started manufacturing race cars four years ago. The company wants to know the relationship between the number of workers on its assembly line and the number of cars they can make in a week. A research team collected the following data presented in the table below: Number of Workers (X) Number of cars produced (y) 2 10 7 15 15 50 20 100 Derive a regression formula using the data in the table and determine how many cars will be produced by 100 workers.
A business student needs cash, so he decides to sell his car. The car is a classic BMW "8 series" that was only made over the course of a few years in the 1990s. He would like to sell it on his own, rather than through a dealer, so he'd like to predict the price he'll get for his car's model year using the accompanying data. Complete parts a through f below. Click the icon to view the car data. a) Make a scatterplot for the data on used BMW 8's provded. Choose the c A. Price 25K- 20K- 15K- 10K- 1991 Year n example Get more help. 1997 Q B. Car Data Year 1995 1994 1997 1996 1991 1993 1992 1991 1991 1991 1991 1993 1995 1991 1993 1994 1996 1997 1992 1995 1997 1995 1995 1997 1997 1994 1995 1991 1993 1994 1993 Year Print Price 11,999 6988 12,990 9995 15,995 14,999 24,995 19,750 15,900 16,988 14,999 10,999 16,999 13,995 16,995 11500 7,994 12,500 14,788 12,490 12,999 9,988 12900 15,600 15,995 19,995 19,900 27,000 28,900 15,995 10,900 Price LU Done X 991 Year 1997 D. Price 25K- 20K- 15K- 10K-…
Using the lengths (in.), chest sizes (in.), and weights (lb) of bears from a data set, the resulting regression equation is Weight=-274 +0.426 Length + 12.1 Chest Size. The P-value is 0.000 and the adjusted R² value is 0.925. If an additional predictor variable of neck size (in.) is included, the P-value becomes 0.000 and the adjusted R² becomes 0.933. Why is it better to use values of adjusted R² instead of simply using values of R²? Choose the correct answer below. C O A. The unadjusted R² can only be calculated for regression equations with two or fewer predictor variables, while the adjusted R² can be calculated for regression equations with any number of predictor variables. O B. The unadjusted R² increases or remains the same as more variables are included, but the adjusted R² is adjusted for the number of variables and sample size. OC. The unadjusted R² can only be calculated for regression equations with P-values greater than 0, while the adjusted R² can be calculated for…
Using the lengths (in.), chest sizes (in.), and weights (lb) of bears from a data set, the resulting regression equation is Weight= -274 +0.426 Length + 12.1 Chest Size. The P-value is 0.000 and the adjusted R² value is 0.925. If an additional predictor variable of neck size (in.) is included, the P-value becomes 0.000 and the adjusted R² becomes 0.933. Why is it better to use values of adjusted R² instead of simply using values of R²? Choose the correct answer below. C O A. The unadjusted R² can only be calculated for regression equations with P-values greater than 0, while the adjusted R² can be calculated for regression equations with any manner of P-value. O B. The unadjusted R² increases or remains the same as more variables included, but the adjusted R² is adjusted for the number of variables and sample size. OC. The unadjusted R² decreases or remains the same as more variables are included, but the adjusted R² is adjusted for the number of variables and sample size. O D. The…
We recorded the pre-statistics course grade (in percent) and introductory statistics course grade (in percent) for 60 community college students. In this data set, no one earned a 90% for the pre-statistics course grade. How could you estimate a student’s introductory statistics course grade if she earned 90% for the pre-statistics course grade? A. Substitute 90 into the regression equation: Predicted introductory statistics course grade = -0.147 + 0.981(90) . B. Substitute the decimal 0.90 into the regression equation: Predicted introductory statistics course grade = -0.147 + 0.981(0.90).
At one of the San Diego area beaches, there has been a problem with thefts in the summer months. A random sample of summer days was selected. For each day, the number of street cameras (X) in the beach area and the number of reported thefts (Y) were recorded. The data was analyzed to produce the results below: The regression equation is: Thefts = 9.78 – 0.493*(cameras) On a certain day there were 5 cameras in the area and 3 reported thefts. Calculate the residual for this day. 8.301 -4.315 4.315 3.301 7.315
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 85 mm Hg. Use a significance level of 0.05. Right Arm 100 99 91 76 76 5 Left Arm 175 170 146 147 146 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y = +x (Round to one decimal place as needed.) Given that the systolic blood pressure in the right arm is 85 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed.)
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