Run a regression analysis on the following bivariate set of data with y as the response variable.y89.643.483.836.752.819.361.531.882.932.589.740.968.923.963.531.753.25.787.14552.826.376.435.8Find the correlation coefficient and report it accurate to three decimal places.r =What proportion of the variation in y can be explained by the variation in the values of x? Reportanswer as a percentage accurate to one decimal place. (If the answer is 0.84471, then it would be84.5%...you would enter 84.5 without the percent symbol.)r2 =%Based on the data, calculate the regression line (each value to three decimal places)y =X +Predict what value (on average) for the response variable will be obtained from a value of 53.1 asthe explanatory variable. Use a significance level of a0.05 to assess the strength of the linearcorrelation.What is the predicted response value? (Report answer accurate to one decimal place.)y =

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Description: Run a regression analysis on the following bivariate set of data with y as the response variable.y89.643.483.836.752.819.361.531.882.932.589.740.968.923.963.531.753.25.787.14552.826.376.435.8Find the correlation coefficient and report it accurate to

Given, x y 89.6 43.4 83.8 36.7 52.8 19.3 61.5 31.8 82.9 32.5 89.7 40.9 68.9 23.9…

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84.5%...you would enter 84.5 without the percent symbol.) 12 = % Based on the data, calculate the regression line (each value to three decimal places) y=D Predict what value (on average) for the response variable will be obtained from a value of 53. the explanatory variable. Use a significance level of a = 0.05 to assess the strength of the lin

84.5%...you would enter 84.5 without the percent symbol.) 12 = % Based on the data, calculate the regression line (each value to three decimal places) y=D Predict what value (on average) for the response variable will be obtained from a value of 53. the explanatory variable. Use a significance level of a = 0.05 to assess the strength of the lin

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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Which correlation would allows for the best prediction of Y based on X? Group of answer choices r = .50 r = .80 r = .05 r = -.90
Suppose the correlation between height and weight for adults is +0.80. What proportion of the variability in weight can be explained by the relationship with height? A. 20% B. 36% C. 64% D. 80%
Assume the correlation coefficient between advertising dollars spent and sales is .78. How much of the variability in sales is explained by the relationship between sales and advertising dollars? a. 39.16% b. 60.84% c. 78% d. 22%
The organization of the Girl Scouts has completed its annual cookie drive. The sales are reported in the accompanying ogive. Approximate the percentage of girls who sold more than 70 boxes of cookies.
Assume that you have paired values consisting of heights (in inches) and weights (in lb) from 40 randomly selected men. The linear correlation coefficient r is 0.594. Find the value of the coefficient of determination. What practical information does the coefficient of determination provide? Choose the correct answer below. O A. The coefficient of determination is 0.353. 64.7% of the variation is explained by the linear correlation, and 35.3% is explained by other factors. O B. The coefficient of determination is 0.647. 35.3% of the variation is explained by the linear correlation, and 64.7% is explained by other factors. O C. The coefficient of determination is 0.647. 64.7% of the variation is explained by the linear correlation, and 35.3% is explained by other factors. O D. The coefficient of determination is 0.353. 35.3% of the variation is explained by the linear correlation, and 64.7% is explained by other factors.
You wish to determine if there is a linear correlation between the age of a driver and the number of driver deaths. The following table represents the age of a driver and the number of driver deaths per 100,000. Use a significance level of 0.01 and round all values to 4 decimal places. Driver Age 65 69 48 37 49 27 62 64 Ho: p = 0 Ha: p = 0 r= Find the p-value p-value= Number of Driver Deaths per 100,000 22 22 34 Find the Linear Correlation Coefficient The p-value is Greater than a Less than (or equal to) a 23 28 20 The p-value leads to a decision to O Do Not Reject Ho O Accept Ho O Reject Ho 33 33 The conclusion is O There is a significant negative linear correlation between driver age and number of driver deaths. There is a significant linear correlation between driver age and number of driver deaths. O There is insufficient evidence to make a conclusion about the linear correlation between driver age and number of driver deaths. O There is a significant positive linear correlation…
0.05 level of significance. Solve for appropriate test statistic and show written work!
answer please
Use the output below to test to see if there is a significant positive linear relation between height and head circumference in children. Use a 5% level of significance. Correlations: Height, Head Circ Pearson correlation of Height and Head Circ 0.911 P-Value 0.000
Assume that you have paired values consisting of heights (in inches) and weights (in lb) from 40 randomly selected men. The linear correlation coefficient r is 0.506. Find the value of the coefficient of determination. What practical information does the coefficient of determination provide? Choose the correct answer below. O A. The coefficient of determination is 0.256. 25.6% of the variation is explained by the linear correlation, and 74.4% is explained by other factors. O B. The coefficient of determination is 0.744. 25.6% of the variation is explained by the linear correlation, and 74.4% is explained by other factors. O C. The coefficient of determination is 0.744. 74.4% of the variation is explained by the linear correlation, and 25.6% is explained by other factors. O D. The coefficient of determination is 0.256. 74.4% of the variation is explained by the linear correlation, and 25.6% is explained by other factors.
your car is more crowded than you think. table 5.8 reports results from a 1969 personal transportation survey on "home-to-work" trips in metropolitan areas. The survey stated that the average car occupancy was 1.4 people. check that calculation.