Please answer in short like it definitely but asap.. Use regression to find a quartic model for the given data set. Round the regression parameters to three decimal places.X1 3467 8 10Y1252137

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Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section: Chapter Questions

Problem 43SGR

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Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
Life Expectancy The following table shows the average life expectancy, in years, of a child born in the given year42 Life expectancy 2005 77.6 2007 78.1 2009 78.5 2011 78.7 2013 78.8 a. Find the equation of the regression line, and explain the meaning of its slope. b. Plot the data points and the regression line. c. Explain in practical terms the meaning of the slope of the regression line. d. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 2019? e. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 1580?2300
XYZ Corporation Stock Prices The following table shows the average stock price, in dollars, of XYZ Corporation in the given month. Month Stock price January 2011 43.71 February 2011 44.22 March 2011 44.44 April 2011 45.17 May 2011 45.97 a. Find the equation of the regression line. Round the regression coefficients to three decimal places. b. Plot the data points and the regression line. c. Explain in practical terms the meaning of the slope of the regression line. d. Based on the trend of the regression line, what do you predict the stock price to be in January 2012? January 2013?
A regression was run to determine whether there is arelationship between the diameter of a tree (x, in inches) and the tree’s age (y, in years). Theresults of the regression are given below. Use this topredict the age of a tree with diameter 10 inches. y=ax+ba=6.301b=1.044r=0.970
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?
Noise and Intelligibility Audiologists study the intelligibility of spoken sentences under different noise levels. Intelligibility, the MRT score, is measured as the percent of a spoken sentence that the listener can decipher at a cesl4ain noise level in decibels (dB). The table shows the results of one such test. (a) Make a scatter plot of the data. (b) Find and graph the regression line. (c) Find the correlation coefficient. Is a linear model appropriate? (d) Use the linear model in put (b) to estimate the intelligibility of a sentence at a 94-dB noise level.
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. height 775 619 519 508 491 474 (a) x=503 feet (b) x=644 feet Stories, y 53 47 44 42 39 38 (c) x=798 feet (d) x=734 feet
The figure shows a scatterplot with the regression line. The equation of the regression line is shown below. The data are for the 50 regions in Country A. The predictor is the percentage of smoke-free homes. The response is the percentage of high school students who smoke. Complete parts a and b. Predicted Pct. Smokers=56.373−0.466(Pct. Smoke-free) 405060708090010203040Pct. Smoke-freePct. Smokers A scatterplot has a horizontal axis labeled “Percent Smoke-free” from 40 to 90 in intervals of 10 and a vertical axis labeled “Percent Smokers” from 0 to 40 in intervals of 10. There is a straight line, falling from left right, passing through approximate points (50, 34) and (80, 20). Of the 50 points plotted, most follow the path of the line between horizontal coordinates 50 and 80 with a vertical spread of about 10, with an approximately equal number of points above and below the line. There are no significant outliers. a. Explain what the trend…
The given paired data lists the heights (in inches) of various males who were measured on their eighth and sixteenth birthdays. Height (8 yrs) 52 50 48 51 51 48 (x) Height (16 yrs) 69 66 64 67 69 65 (y) Find the equation of the regression line and plot it on your scatter diagram using an x of 48 and an x of 51.
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. The table shows the shoe size and heights (in.) for 6 men. Shoe size, x 9.0 10.0 11.0 12.0 13.0 13.5 O Height, y 66.0 69.0 70.0 71.0 72.0 73.0 ... Find the regression equation. y =x+ (Round to three decimal places as needed.)
The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable. Find the best predicted temperature for a time when a bug is chirping at the rate of 30003000 chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value? Chirps in 1 min 918918 12021202 950950 12071207 11361136 10481048 Temperature (degrees°F) 72.772.7 93.393.3 81.881.8 8787 85.485.4 82.682.6 What is the regression equation? ModifyingAbove y with caretyequals=nothingplus+nothingx (Round the x-coefficient to four decimal places as needed. Round the constant to two decimal places as needed.)
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. Height, x Stories, y 520 508 (a) x = 501 feet (c) x = 318 feet (b) x = 639 feet (d) x = 732 feet 766 620 46 494 484 51 45 43 39 35 Find the regression equation. (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.) Choose the correct graph below. OA. OB. Oc. Oc. OD. 60+ 60 60+ 50+ 0- 0- 800 800 Height (feet) 800 Height (feet) 800 Height (feet) Height (feet) (a) Predict the value of y for x = 501. Choose the correct answer below. O A. 40 O B. 50 C. 46 O D. not meaningful (b) Predict the value of y for x = 639. Choose the correct answer…

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Use regression to find a quartic model for the given data set. Round the regression parameters to three decimal places. X 1 3 4 6 7 8 10 y 1 2 5 2 1 3 7 y=