A random sample of some of the heaviest carnivores on Earth was reviewed to determine if there is anassociation between the length (in meters) and weight (in kilograms) of these carnivores. Here are the scatterplot, the residuals plot, a histogram of the residuals, and the regression analysis of the data. Use thisinformation to analyze the association between the length and weight of these carnivores.Hgram the eteRasiduala Vera the Fited VlSertf htLeuThe regression equation isWeight = - 668 + 485 LengthCoef SE Coef232.890.63PredictorP- 668.3-2.87 0.0245.35 0.001ConstantLength485.21S = 194.794R-Sq = 80.4%R-Sq (adj) = 77.6%Perform a significance test and state your conclusion in context.O The population is the random sample of carnivores - the parameter is the relationship between heightand weights for the sample of carnivoresThe population is carnicarnivoresparameter is the relationship between height and weight forHo: ?Ha: ?Choose the conditions which must be met:On(1 – p) 2 10ON > 10nMacBook Pro@$134&6.7QWEYUSFGHJKCVN M* 00< CO Choose the conditions which must be met:On(1 - p) 2 10ON> 10nO Residuals approximately normalO Randomly Sampledn(p) > 10On 2 30 or normal population.Onp 2 10n(1 - p) > 10O Linear ScatterplotO No pattern in residuals-randomly scattered about the lineName the test:O Linear Regression T TestO2 Prop. Z TestChi Square GOF TestO2 Sample T Test01 Prop. Z TestO Chi Square IndependenceMatched Pairs T Test01 Sample T TestTest Statistict =The P-value isless than alphaMacBook ProescくQ@$%23478.QWEY UTASLL

Since we only do up to three sub parts of a problem, we'll be doing the first three subparts. Please…

A random sample of some of the heaviest carnivores on Earth was reviewed to determine if there is an association between the length (in meters) and weight (in kilograms) of these carnivores. Here are the scatter plot, the residuals plot, a histogram of the residuals, and the regression analysis of the data. Use this information to analyze the association between the length and weight of these carnivores.

A random sample of some of the heaviest carnivores on Earth was reviewed to determine if there is an association between the length (in meters) and weight (in kilograms) of these carnivores. Here are the scatter plot, the residuals plot, a histogram of the residuals, and the regression analysis of the data. Use this information to analyze the association between the length and weight of these carnivores.

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

See similar textbooks

Similar questions

Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
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?
Find the mean hourly cost when the cell phone described above is used for 240 minutes.
Does Table 1 represent a linear function? If so, finda linear equation that models the data.
Two lines are proposed to describe the relationship between x and y for a particular data set. Shown below are the lines and the sum of squared error for each line. Line1: yˆ = 5+ 4x, Sumofsquarederror= 48.6 Line2: yˆ = 6 + 3.5x, Sumofsquarederror= 40.8 One of these two lines is the regression line. Which line is the regression line?
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). Which regression equation is best for predicting city fuel consumption? Why? E Click the icon to view the table of regression equations. Choose the correct answer below. O A. The equation CITY = 6.65 - 0.00161WT + 0.675HWY is best because it has a low P-value and the highest adjusted value of R2. O B. The equation CITY = 6.83 - 0.00132WT - 0.253DISP + 0.654HWY is best because it has a low P-value and the highest value of R?. OC. The equation CITY = 6.83 - 0.00132WT - 0.253DISP + 0.654HWY is best because it uses all of the available predictor variables. O D. The equation CITY = - 3.14 + 0.823HWY is best because it has a low P-value and its R2 and adjusted R? values are comparable to…
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). Which regression equation is best for predicting city fuel consumption? Why? Click the icon to view the table of regression equations. Choose the correct answer below. OA. The equation CITY = -3.12 +0.824HWY is best because it has a low P-value and its R2 and adjusted R² values are comparable to the R2 and adjusted R2 values of equations with more predictor variables. OB. The equation CITY=6.88-0.00131WT-0.251DISP+0.654HWY is best because it has a low P-value and the highest value of R². OC. The equation CITY = 6.65 -0.00156WT +0.665HWY is best because it has a low P-value and the highest adjusted value of R². CITY=6.88-0.00131WT-0.251DISP+0.654HWY is best because it uses all of the…
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). Which regression equation is best for predicting city fuel consumption? Why? Click the icon to view the table of regression equations. Choose the correct answer below. A. The equation CITY=6.86 -0.00131WT -0.258DISP+0.659HWY is best because it has a low P-value and the highest value of R². B. The equation CITY=6.73 -0.00157WT +0.668HWY is best because it has a low P-value and the highest adjusted value of R². C. The equation CITY= -3.15+0.823HWY is best because it has a low P-value and its R² and adjusted R² values are comparable to the R² and adjusted R² values of equations with more predictor variables. O D. The equation CITY=6.86 -0.00131WT-0.258DISP + 0.659HWY is best because it…
In a sample of cars reviewed by Motor Trend magazine, the mean horsepower (hp) was 150 hp with a standard deviation of 36 hp. The mean weight (lbs) was 2500 lbs with a standard deviation of 720 lbs. Assume the relationship between weight and horsepower is linear and has a correlation of r = +0.55. What is the slope of the linear regression model predicting weight (y-variable) from horsepower (x-variable)? using the value of the slope you found, now compute the intercept of the linear regression model predicting weight (y-variable) from horsepower (x-variable). 850 550 1150 250
To estimate ages of trees, rangers use a simple regression model that relates the tree diameter (inches) to their age (years). The model is useful because tree diameter has an association to tree age (count of cross section rings). 8 trees were sampled. The regression model is: TreeAge= -0.15228+6.451 (Diameter) d) If in fact, the tree age is 76 years for a tree with diameter 9.5 inches, the residual is approximately ["", ""] . The age has been a(n) [a7]. e) If the correlation , then the coefficient of determination is [a8].
To estimate ages of trees, rangers use a simple regression model that relates the tree diameter (inches) to their age (years). The model is useful because tree diameter has an association to tree age (count of cross section rings). 8 trees were sampled. The regression model is: TreeAge= -0.15228+6.451 (Diameter) d) If in fact, the tree age is 76 years for a tree with diameter 9.5 inches, the residual is approximately ["", ""] . The age has been a(n) [a7]. e) If the correlation , then the coefficient of determination is [a8].
Solve attached photo.