thahs and the weia = .05a = .01icie.950.990.878.959.811.917.754.875.707.834(kg)6..666.79810.632.76511.602.73512.576.70813.553.684n (cm14.532.66115.514.64116.497.62317482.60618.468.59019.456.57520.444.56125.396.50530.361.4634567 Listed below are the overhead widths (in cm) of seals measured from photographs and the weights (in kg) of the seals. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the criticalvalues of r using a = 0.05. Is there sufficient evidence to conclude that there is a linear correlation between overhead widths of seals from photographs and the weights of the seals?Overhead WidthWeight| 7.28.1 D1797.59.79.38.9118170242198204Click here to view a table of critical values for the correlation coefficient......Construct a scatterplot. Choose the correct graph below.OC.OD.OA.OB.Aweight (kg)300-Aweight (kg)300-Aweight (kg)300-Aweight (kg)300-----------1001100100-100-110width (cm)7101010width (cm)width (cm)width (cm)

Use the excel tools to draw the scatter plot with the help of following steps: Enter the data…

Answered: Listed below are the overhead widths…

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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A researcher obtained the following results for a study: n = 44, SSy = 340603, SSx = 33712, B̂ = 2.0025 Find the correlation for these results?
Suppose the correlation coefficient r=0.16 Find:a. The coefficient of determination r2= (round to 3 decimal places)b. The percentage of explained variation = % (round to 1 decimal place)c. The percentage of unexplained variation = % (round to 1 decimal place)
A researcher at a large company has collected data on the beginning salary and current salary of 50 randomly selected employees. The correlation between the data sets is r = 0.912. The summary statistics from the data collected are shown bellow: Beginning Salary Ending Salary Mean x¯=56,340 y¯=82,070 Standard deviation Sx = 5,470 Sy = 7,800 Use a complete sentence to describe the strength and direction of the linear relationship between beginning salary and current salary. Find the equation of the least-squares regression line. (Round to two decimal places)
Match the following values of the correlation coefficients with the proper scatter plots below.
B). Using the linear correlation coefficient found in the previous step, determine whether there is sufficient evidence to support the claim of a linear correlation between the two variables. Choose the correct answer below. C) Identify the feature of the data that would be missed if part (b) was completed without constructing the scatterplot. Choose the correct answer below. A. The scatterplot reveals a distinct pattern that is a straight-line pattern with positive slope. B. The scatterplot does not reveal a distinct pattern. C. The scatterplot reveals a distinct pattern that is a straight-line pattern with negative slope. D. The scatterplot reveals a distinct pattern that is not a straight-line pattern. Clear
Please answer all sections
Listed below are the overhead widths (in cm) of seals measured from photographs and the weights (in kg) of the seals. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the critical values of r using a = 0.05, Is there sufficient evidence to conclude that there is a linear correlation between overhead widths of seals from photographs and the weights of the seals? Overhead Width 7.1 7.5 178 Click here to view a table of critical values for the correlation coefficient. 9.6 9.4 8.8 8.2 Weight 112 205 238 204 188 OA. YB. OC. KD D. Aweight (kg) 300- A weight (kg) Aweight (kg) 300- Aweight (kg) 300- 300- .. 100 100+ 100 7. rect: 0 10 10 10 10 width (om) width (cm) width (on) widn (om) The linear correlation coefficient is r= (Round to three decimal places as needed.) Enter your answer in the answer box and then click Check Answer. O parts remaining Check Answer वी
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the number of rubber bands used and the distance, incentimeters, of her jump. When they created a scatterplotthe relationship appeared to be linear, and the correlationwas r = 0.996. After discussing these results, the teacherinstructed the students to convert the distances to inches.What effect will this have on the correlation between thetwo variables?a) Because 1 cm = 0.393701 inches, the correlationwill become .392126.b) Because 1 cm = 0.393701 inches, the correlationwill become 2.5298.c) Because only the length measurements have changed,the correlation will decrease substantially.d) Because changing from centimeters to inches doesnot affect the value of the correlation, the correlationwill remain 0.996. e) Because inches is a much more common measure-ment for distance in the United States, the relation-ship between the data will be stronger and thus the correlation will increase.
To examine the linear correlation between adult age and weight, data was collected from 8 adults. See the table below. Age Weight (Years) (pounds) 35 223 38 231 48 121 50 148 52 219 55 210 56 132 57 218 Does this result suggest that the correlation is statistically significant? Use a = 0.10. Important Note: Round your value for r to three decimals before computing the STS. DO NOT use the built- in LinRegTTest on a calculator because you might get marked wrong. When computing the P-value, use the rounded STS. 1. Select the correct statement: O Even if the correlation is found to be statistically significant, that does not necessarily mean that changes in x cause changes in y O If the correlation is found to be statistically significant, that means that changes in x cause changes in y The hypotheses are: O Ho:r = 0; Ha:r#0 O Ho:r 0 O Ho:p = 0; Ha:p+ 0 O Ho:p 2 0; Ha:p 0 O Ho:r > 0; Ha:r <0 MacBook Air 20 F3 000 11 F8 F4 F5 F6 F7 F9
Complete the following instructions:i. Identify the independent and dependent variables.ii. Calculate and interpret the Pearson correlation coefficient r for the paired data. Be sure to indicate if the correlation is positive or negative, and whether it is strong, moderate, or weak, or if there does not appear to be any significant correlation. A researcher for a gasoline retailer examines the relationship between the volume of gasoline purchased in kL(i.e. 1000 L) compared to the price of gasoline (in $/L), over a course of several days, to see if there is a correlation between the data: Amount of Gasoline Purchased Price of Gasoline 2.257 1.079 3.126 1.015 2.793 1.046 4.671 0.973 2.137 1.021 5.916 0.939 2.775 0.995 4.88 0.927 3.162 0.964 7.498 0.899 2.971 0.982 3.596 0.945 1.372 1.129 3.961 1.059 i. Independent Variable: Dependent Variable: ii. Pearson correlation coefficient (r): Round to 3 decimal…
The following shows a linear correlation between weight and height. Image result for scatter plot scatter plot positive histogram negative O pie chart random frequency polygon biased

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