Run a regression analysis on the following bivariate set of data with y as the response variable. 42 -28.2 54.1 41.1 57.9 -8.9 85.9 74.8 84.4 116 51.4 -9.8 57.8 25.5 63.5 47 61.1 33.9 66.4 6.5 43.9 49.2 73.6 143.3 Find the correlation coefficient and report it accurate to three decimal places. 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. (If the answer is 0.84471, then it would be 84.5%...you would enter 84.5 without the percent symbol.) Based on the data, calculate the regression line (each value to three decimal places) Predict what value (on average) for the response variable will be obtained from a value of 41.3 as the explanatory variable, Use a significance level of a = 0.05 to assess the streng th of the linear correlation. What is the predicted response value? (Report answer accurate to one decimal place.)

Run a regression analysis on the following bivariate set of data with y as the response variable. 42 -28.2 54.1 41.1 57.9 -8.9 85.9 74.8 84.4 116 51.4 -9.8 57.8 25.5 63.5 47 61.1 33.9 66.4 6.5 43.9 49.2 73.6 143.3 Find the correlation coefficient and report it accurate to three decimal places. 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. (If the answer is 0.84471, then it would be 84.5%...you would enter 84.5 without the percent symbol.) Based on the data, calculate the regression line (each value to three decimal places) Predict what value (on average) for the response variable will be obtained from a value of 41.3 as the explanatory variable, Use a significance level of a = 0.05 to assess the streng th of the linear correlation. What is the predicted response value? (Report answer accurate to one decimal place.)

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

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
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.
Research was conducted on the amount of training for 5K and the time a contestant took to run the race. The researcher recorded the number of miles during training ( a 1 month period) and the time to complete the 5K. The results are below. Miles Trained 45 77 37 25 60 64 Time (Minutes) 32.9 28 36.2 27.5 19.5 18.3 a) Give the correlation coefficient. r = b) Give the equation of the regression line. Round the values to 4 deimal places.This is in the form y = ax + b, so the slope goes in the first box and the y-inercept in the 2nd box.y = x + c) Interpret the y-intercept (Explain it's meaning, do not just give its value.)d) Predict the time in the 5K if someone trained 37 miles.Use the rounded values entered for the slope and intercept of the regression line to compute the predicted y-value.y = e) Give the residual for 37 miles trained.This is the actual y value for the runner who trained for miles minus the predicted value.Residual=
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 =
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%)
Listed below are paired data consisting of amounts spent on advertising (in millions of dollars) and the profits (in millions of dollars). Determine if there is a significant linear correlation between advertising cost and profit Use a significance level of 0.01 and round all values to 4 decimal places. Advertising Cost Profit 3 21 4 16 5 29 17 7 28 8 21 Ho: p = 0 Ha: p +0 Find the Linear Correlation Coefficient r= Find the p-value p-value = The p-value is Less than (or equal to) a OGreater than a The p-value leads to a decision to Do Not Reject Ho OReject Ho OAccept Ho
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 =?