The table contains thestate population andthe number of licenseddrivers in thestate (both in millions)for the states withpopulation under 1million in 2014. Theregression model forthis data isy=0.64x+0.01 wherex is the statepopulation (in millions)and v is the number of0.7 1.4State pop. (millions.StateABCDEFGTLicensed Drivers in 2014Population0.750.930.960.750.860.620.590.7 14 €0State pop. (millions.Licensed Drivers0.490.610.6360.714State pop. (millions,The estimated number of licensed drivers in state H in 2014 is(Type a whole number. Round to the nearest thousand as needed.)0.490.550.410.39(B) If the population of state H in 2014 was about 1.6 million, use the model toestimate the number of licensed drivers in state H in 2014.00.7State pop. (millions,The estimated population of state J in 2014 is(Type a whole number. Round to the nearest thousand as needed.)(C) If the number of licensed drivers in state J in 2014 was about 1,019,000, use themodel to estimate the population of state J in 2014.

The regression model models the state population and the number of licensed drivers.The aim is to…

Answered: The table contains the state population…

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

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It is believed that the electrical energy that a chemical plant consumes each month is related to the average environmental temperature, the number of days in the month, the average purity of the product, and the tons of product manufactured. Historical data is available for the previous year and is presented in the following table (seen in the image=. Perform simple linear regression analysis
The table below shows the amounts of crude oil (in thousands of barrels per day) produced by a country and the amounts of crude oil (in thousands of barrels per day) imported by a country, for the last seven years. Construct and interpret a 90% prediction interval for the amount of crude oil imported by the this country when the amount of crude oil produced by the country is 5,603 thousand barrels per day. The equation of the regression line is Oil produced,x Oil imported, y -1117x+ 15,844.101. 5,158 5,093 5,008 10,026 5,822 5.724 5,651 5,449 9,328 9,131 9,667 10,088 10,145 10,199 Construct and interpret a 90% prediction interval for the amount of crude oil imported when the amount of crude oil produced by the country is 5,603 thousand barrels per day Select the correct choice below and fill in the answer boxes to complete your choice. (Round to the nearest cent as needed.) O A. There is a 90% chance that the predicted amount of oil imported is between thousand barreis, when there are…
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 58 inches. Is the result close to the actual weight of 672 pounds? Use a significance level of 0.05. Chest size (inches) Weight (pounds) 46 57 53 41 40 40 384 580 542 358 306 320
The least-squares regression equation is y=620.6x+16,624 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7004. Interpret the slope.
A temperature dataset gives y mean April temperature (Fahrenheit) and x = geographic latitude for 20 U.S. cities. The simple linear regression equation for the sample is ŷ = 119 - 1.64x. (a) The value of latitude for a certain city is 41. What is the predicted April temperature for this city? (Round your answer to the nearest tenth.) degrees Fahrenheit (b) The mean April temperature for this city is 48. What is the residual for the city? (Round your answer to the nearest tenth.) degrees Fahrenheit You may need to use the appropriate table in the Appendix of Tables to answer this question.
The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). The equation CITY = -3.14 +0.816HWY was previously determined to be the best for predicting city fuel consumption. A car weighs 2740 lb, it has an engine displacement of 1.5 L, and its highway fuel consumption is 40 mi/gal. What is the best predicted value of the city fuel consumption? Is that predicted value likely to be a good estimate? Is that predicted value likely to be very accurate? Click the icon to view the table of regression equations. Regression Table The best predicted value of the city fuel consumption is (Type an integer or a decimal. Do not round.) Predictor (x) Variables WT/DISP/HWY WT/DISP WT/HWY DISP/HWY WT DISP HWY P-Value R² 0.000 0.943 0.000 0.748 0.000 0.942…
The owner of Showtime Movie Theaters, Inc., used multiple regression analysis to predict gross revenue (y) as a function of television advertising (x 1) and newspaper advertising (x 2). The estimated regression equation was Weekly Gross Revenue ($1000s) Televison Advertising ($1000s) Newspaper Advertising ($1000s) 97 6 1.5 91 3 2 95 5 2.5 93 3.5 2.5 96 4 4.3 94 4.5 2.3 95 3.5 4.2 95 4 3.5 ŷ = 82.5 + 2.01 x 1 + 1.26 x 2The computer solution provided SST = 24 and SSR = 22.876. Compute R 2 and R a 2 (to 3 decimals). R 2 R a 2 When television advertising was the only independent variable, R 2 = 0.551 and R a 2 = 0.476. Are the multiple regression analysis results preferable?
The scatter plot below shows the average cost of a designer jacket in a sample of years between 2000 and 2015. The least squares regression line modeling this data is given by yˆ=−4815+3.765x. A scatterplot has a horizontal axis labeled Year from 2005 to 2015 in increments of 5 and a vertical axis labeled Price ($) from 2660 to 2780 in increments of 20. The following points are plotted: (2003, 2736); (2004, 2715); (2007, 2675); (2009, 2719); (2013, 270). All coordinates are approximate. Interpret the slope of the least squares regression line. Select the correct answer below: 1.The average cost of a designer jacket decreased by $3.765 each year between 2000 and 2015. 2.The average cost of a designer jacket increased by $3.765 each year between 2000 and 2015. 3.The average cost of a designer jacket decreased by $4815 each year between 2000 and 2015. 4. The average cost of a designer jacket increased by $4815 each year between 2000 and…
3- Find the equation of the regression line and find the y_ value for the specified x value. Calories and Cholesterol Calories and cholesterol are as follows: Calories X 390 535 720 300 430 500 440 Cholesterol y 43 45 80 50 55 52 60 Find y-hat when x = 600 calories. LO LO
The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). The equation CITY = -3.14 +0.818HWY was previously determined to be the best for predicting city fuel consumption. A car weighs 2760 lb, it has an engine displacement of 2.2 L, and its highway fuel consumption is 34 mi/gal. What is the best predicted value of the city fuel consumption? Is that predicted value likely to be a good estimate? Is that predicted value likely to be very accurate? Click the icon to view the table of regression equations. The best predicted value of the city fuel consumption is (Type an integer or a decimal. Do not round.) Regression Table Predictor (x) Variables P-Value R² Adjusted R² WT/DISP/HWY WT/DISP WT/HWY DISP/HWY 0.000 0.943 0.000 0.747 0.000 0.941…
A researcher interested in explaining the level of foreign reserves for the country of Barbados estimated the following multiple regression model using yearly data spanning the period 2001 to 2016: FR=a+B01L+YEXP+8FDI Where FR = yearly foreign reserves (So000's), OIL = annual oil prices, EXP = yearly total exports (S000's) and FDI = annual foreign direct investment ($000's). The sample of data was processed using MINITAB and the following is an extract of the output obtained: Predictor Coef StDev t-ratio p-value Constant 5491.38 2508.81 2.1888 0.0491 OIL 85.39 18.46 4.626 0.0006 EXP -377.08 112.19 0.0057 FDI -396.99 160.66 -2.471 s - 2.45 R-sq = 96.3% R-sq(adj) = 95.3% Analysis of Variance Source DF MS F Regression 3 1991.31 663.77 ?? Error 12 43. רר 6.45 Total 15 a) What is dependent and independent variables? b) Fully write out the regression equation c) Fill in the missing values **', **', '?'and *??"
The table shows the numbers of new-vehicle sales (in thousands) in the United States for Company A and Company B for 10 years. The equation of the regression line is y = 0.991x + 1,222.81. Complete parts (a) and (b) below. New-vehicle sales (Company A), x New-vehicle sales (Company B), y 4,149 3,923 3,566 3,400 3,266 3,076 2,868 2,485 1,952 2,066 4,912 4,871 4,827 4,721 4,672 4,474 4,684 3,822 2,956 2,754 (a) Find the coefficient of determination and interpret the result. r² = r2 = 0.821 (Round to three decimal places as needed.) How can the coefficient of determination be interpreted? The coefficient of determination is the fraction of the variation in new-vehicle sales for Company B that can be 2 explained by the variation in new-vehicle sales for Company A and is represented by The remaining fraction of the variation, 1-2, is unexplained and is due to other factors or to sampling error. s (b) Find the standard error of estimates and interpret the result. Se O (Round to three decimal…

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