Fifty-four wild bears were anesthetized, and then their weights and chest sizeswere measured and listed in a data set. Results are shown in the accompanyingdisplay. Is there sufficient evidence to support the claim that there is a linearcorrelation between the weights of bears and their chest sizès? When measuringan anesthetized bear, is it easier to measure chest size than weight? If so, does it P-value (two tailed): 0.000Critical r:appear that a measured chest size can be used to predict the weight? Use asignificance level of a = 0.05.Determine the null and alternative hypotheses.Ho: PH₁: P▼(Type integers or decimals. Do not round.)y an example Get more help.Correlation ResultsCorrelation coeff, r: 0.964493+0.2680855Clear allCheck answerInc

The following information has been given: Correlation coefficient r = 0.964493 Critical value…

Answered: Correlation Results Correlation coeff,…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The accompanying technology output was obtained by using the paired data consisting of foot lengths (cm) and heights (cm) of a sample of 40 people. Along with the paired sample data, the technology was also given a foot length of 21.4 cm to be used for predicting height. The technology found that there is a linear correlation between height and foot length. If someone has a foot length of 21.4 cm, what is the single value that is the best-predicted height for that person? The regression equation is Height=52.3+4.58 Foot Length Predictor Coef SE Coef T P Constant 52.33 11.01 4.75 0.000 Foot Length 4.5818 0.4872 9.40 0.000 S=5.50437 R-Sq=70.8% R-Sq(adj)=70.0% Predicted Values for New Observations New Obs Fit SE Fit 95% CI 95% PI 1 150.381 1.722 (146.730, 154.032) (138.758, 162.004) Values of Predictors for New Observations New Obs Foot Length 1 21.4 The…
The accompanying table shows eleven altitudes (in thousands of feet) and the speeds of sound (in feet per second) at these altitudes. Complete parts (a) through (d) below. Click here to view the data table. LOADING... Click here to view the table of critical values for the Pearson correlation coefficient. LOADING... (a) Display the data in a scatter plot. Choose the correct graph below. A. -5559401140Altitude (1000's ft)Speed of sound (ft/s) A scatter plot has a horizontal axis labeled Altitude (thousands of feet) from negative 5 to 55 in increments of 5 and a vertical axis labeled Speed of sound (feet per second) from 940 to 1140 in increments of 20. The following 11 points are plotted: (0, 967); (5, 967); (10, 967); (15, 969); (20, 996); (25, 1015); (30, 1037); (35, 1056); (40, 1078); (45, 1097); (50, 1116). From left to right, the first 3 points follow the pattern of a horizontal line and the next 8 points tightly follow the…
The accompanying table lists the ages of acting award winners matched by the years in which the awards were won. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value of r. Determine whether there is sufficient evidence to support a claim of linear correlation between the two variables. Should we expect that there would be a correlation? Use a significance level of α = 0.01. Click the icon to view the ages of the award winners. Construct a scatterplot. Choose the correct graph below. Best Actor (years) A. 70- 20+ CHIT • 20 70 Best Actress (years) The linear correlation coefficient is r = (Round to three decimal places as needed.) B. Best Actor (years) 70- 20- IDI ... ● 20 70 Best Actress (years) Best Actress 28 30 Best Actor 41 37 O Best Actor (years) Print 70- 20+ Best Actresses and Best Actors 29 61 33 32 36 43 49 50 20 70 Best Actress (years) 46 64 29 60 23 53 40 56 Done 43 43 57 32 n X Best Actor (years) 70- 20+ Mid 18 ● ● 20 70 Best…
I'm still having a hard time trying to compute r the sample correlation coefficient, thank you.
Measuring Seals from PhotosListed below are the overhead widths (in cm) of seals measured from photographs and the weights of the seals (in kg). The data are based on “Mass Estimation of Weddell Seals Using Techniques of Photogrammetry,” by R. Garrott of Montana State University. The purpose of the study was to determine if weights of seals could be determined from overhead photographs. Is there sufficient evidence to conclude that there is a correlation between overhead widths of seals from photographs and the weights of the seals?
A correlation Study provides the following data r= -0.74 and the study involved 62 participants. What is the effect size ???
Which of the following is NOT one of the three common errors involving correlation? Question content area bottom Part 1 Choose the correct answer below. A. Correlation does not imply causality B. The conclusion that correlation implies causality C. Mistaking no linear correlation with no correlation D. The use of data based on averages
D Correlation coeff, r: 0.961961 Fifty-four wild bears were anesthetized, and then their weights and chest sizes were measured and listed in a data set. Results are shown in the accompanying Correlation Results display. Is there sufficient evidence to support the claim that there is a linear correlation between the weights of bears and their chest sizes? When measuring an anesthetized bear, is it easier to measure chest size than weight? If so, does it appear that a measured chest size can be used to predict the weight? Use a significance level of a=0.05. Critical r: +0.2680855 P-value (two tailed): 0.000 C -- there is not a linear correlation between the two. OB. Yes, it is easier to measure a chest size than a weight because measuring weight would require lifting the bear onto the scale. The chest size could not be used to predict weight because there is too much variance in the weight of the bears. OC. Yes, it is easier to measure a chest size than a weight because measuring weight…
A researcher is trying to determine if there is a significant relationship between number of hours spent cycling each week and resting heart rate among study participants. Which procedure should he use? Group of answer choices: Mean ANOVA One-way chi square Correlation
estion 3 of 38 university and gathers their freshman year GPA data and the high school SAI score reported on each of their college applications. He produces a scatterplot with SAT scores on the horizontal axis and GPA on the vertical axis. The data has a linear correlation coefficient of 0.506701. Additional sample statistics are summarized in the table below. Variable Sample Sample standard Variable description mean deviation high school SAT score x 1504.291401 Sx = 105.782904 %3D y freshman year GPA y = 3.240805 Sy = 0.441205 r = 0.506701 slope 0.002113 Determine the y-intercept, a, of the least-squares regression line for this data. Give your answer precise to at least four decimal places. tems of use Thelp about us குசங் careers 2:30 PM 10/24/20 o耳国 @ hp
Media periodically discuss the issue of heights of winning presidential candidates and heights of their main opponents. The accompanying table lists the heights (cm) from several recent presidential elections. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value of r. Determine whether there is sufficient evidence to support a claim of linear correlation between the two variables. Should we expect that there would be a correlation? Use a significance level of a = 0.05. Click the icon to view the heights of the candidates. Construct a scatterplot. Choose the correct graph below. O A. 200- 160+ 160 W 200 President Height (cm) Q Q The linear correlation coefficientis r= (Round to three decimal places as needed.) Determine the null and alternative hypotheses. Ho: P H₁: P (Type integers or decimals. Do not round.) The test statistic is t=. (Round to two decimal places as needed.) The P-value is (Round to three decimal places as needed.)…
For a random sample of households in the US, we record annual household income, whether the location is east or west of the Mississippi River, and number of children. We are interested in determining whether or not there is a linear relationship between household income and number of children. Part 1 (a) State the null and alternative hypotheses. Ho: II vs Ha: 44₁ :: p ₂ ^ P₁ p II :: P₁ A P₂ : 0 p

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