Run a regression analysis on the following bivariate set of data with y as the response variable.y62.450.140.868.535.87345.861.657.848.965.429.257.550.123.896.626.881.540.676.762.957.853.560.1Find 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? Report answer asa percentage accurate to one decimal place. (if the answer is 0.84471, then it would be 84.5%...you wouldenter 84.5 without the percent symbol.)r: =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 35.8 as theexplanatory variable.What is the predicted response value? (Report answer accurate to one decimal place.)y =

The Correlation coefficient is denoted with 'r' and it determines the direction and strength of…

Answered: Run a regression analysis on the…

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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Use the given data set to complete parts (a) through (c) below. (Use a = 0.05.) 10 8 13 9. 11 14 4 12 7 y 7.45 6.77 12.73 7.11 7.81 8.84 6.08 5.39 8.16 6.41 5.72 Click here to view a table of critical values for the correlation coefficient. 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. O A. There is sufficient evidence to support the claim of a nonlinear correlation between the two variables. O B. There is insufficient evidence to support the claim of a nonlinear correlation between the two variables. O C. There is sufficient evidence to support the claim of a linear correlation between the two variables. O D. There is insufficient evidence to support the claim of a linear correlation between the two variables. c. Identify the feature of the data that would be missed if part (b) was completed without constructing…
Annual high temperatures in a certain location have been tracked for several years. Let X represent the year and Y the high temperature. At the 0.05 significance level, does the data below show significant (linear) correlation between X and Y? x y 4 29.04 5 26.4 6 29.06 7 44.22 8 35.98 9 49.64 10 46.3 11 46.46 12 50.42 13 61.78 Yes, significant correlation No
Determine if there is a significant correlation between the sets of data at a 10% significance level. X 63 32 28 23 39 37 43 21 y 89 64 61 57 70 68 73 56 Negative Critical Value, tcrit [three decimal accuracy] Positive Critical Value, tcrit [three decimal accuracy] Test Statistic, ttest %3D [three decimal accuracy] Test Conclusion: Reject Ho. There is enough evidence to suggest a significant (positive or negative) linear correlation between the data sets. Fail to Reject Ho. There is not enough evidence to suggest a significant (positive or negative) linear correlation between the data sets.
Perform rank correlation analysis on the following data set: x y -5 1.9 -4 0.9 -3.25 0.7 -2.55 -0.5 -1.85 -1.2 -0.9 -1.7 5.5511151231258E-17 0.7 0.8 1.5 1.55 1.6 2.35 -0.1 3.3 0.8 4.1 -1.1 5.1 -0.9 5.8 -0.8 6.7 0.7 What is the rank correlation coefficient? (Round to three decimal places.) What is the critical rho value at a 0.01 significance? Do we have correlation? Yes, the rank correlation coefficient is smaller (in absolute value) than the critical rho value. No, the rank correlation coefficient is larger (in absolute value) than the critical rho value. No, the rank correlation coefficient is smaller (in absolute value) than the critical rho value. Yes, the rank correlation coefficient is larger (in absolute value) than the critical rho value.
Here is a bivariate data set. y 30.4 1.4 28.5 37.7 34.4 46.8 47.9 29.8 23.3 6.3 46.3 32.9 56.6 64.6 42.1 35.1 39.7 41.4 58.5 69.9 Find the correlation coefficient and report it accurate to three decimal places. r =
You wish to determine if there is a linear correlation between the two variables at a significance level of α=0.02α=0.02. You have the following bivariate data set. x y 25.2 64.3 15.3 79.6 39 49.8 30 75.1 14.8 82.2 15.7 69.9 4.3 92.5 17.9 84.4 13.4 77.6 18.4 74.4 25 78.1 24.4 65 23.7 69.7 10.9 89.1 31.3 65.3 20.6 82.4 20.4 81.1 29 68.2 13.9 92 8.7 88.4 5.3 93.4 What is the correlation coefficient for this data set? r = (report answer accurate to at least 3 decimal places)What is the p-value for this correlation coefficient? p-value =
Here is a bivariate data set. x y 51.3 19.3 11.7 115.1 43.8 41.8 -2.9 173.8 27.7 109.3 50.9 63.7 40.6 76.9 40.1 46.2 41.6 63.2 26.4 43.5 32.3 66.8 51.6 14.9 36.9 42.9 Find the correlation coefficient and report it accurate to three decimal places.r = Incorrect (NOT 0.110)What proportion of the variation in y can be explained by the variation in the values of x? Report answer as a percentage accurate to one decimal place.R² = Incorrect% (NOT 1.21%)
Post secondary education information was examined for a random selection of states. The data below show the number of community colleges in the number of universities for each particular state. Community: 12, 17, 13, 10, 8 University: 4, 6, 5, 4, 3 A. Create a scatterplot of the given data sets B. Find the correlation coefficient of the sets of data. C. Create the equation of the regression line for the data and evaluate it when X=300.
Suppose a doctor measures the height, x, and head circumference, y, of 8 children and obtains the data below. The correlation coefficient is 0.858 and the least squares regression line is y = 0.228x +11.187. Complete parts (a) and (b) below. Height, x 27.5 25.75 26.5 25.5 27.25 26.25 25.75 27.25 27 27.25 27 Head Circumference, y 17.4 17.2 17.2 16.9 17.6 17.1 17.1 17.4 17.4 17.3 17.3 (a) Compute the coefficient of determination, R². R² =% (Round to one decimal place as needed.) (b) Interpret the coefficient of determination and comment on the adequacy of the linear model. Approximately % of the variation in (Round to one decimal place as needed.) is explained by the least-squares regression model. According to the residual plot, the linear model appears to be
R =?

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