You run a regression analysis on a bivariate set of data (n = 84). With = 45.6 and y = 42.8, you obtain the regression equation y = - 3.196x + 55.817 with a correlation coefficient of r = – 0.183. You want to predict what value (on average) for the response variable will be obtained from a value of 180 as the explanatory variable. What is the predicted response value? y = (Report answer accurate to one decimal place.)

You run a regression analysis on a bivariate set of data (n = 84). With = 45.6 and y = 42.8, you obtain the regression equation y = - 3.196x + 55.817 with a correlation coefficient of r = – 0.183. You want to predict what value (on average) for the response variable will be obtained from a value of 180 as the explanatory variable. What is the predicted response value? y = (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

The table below gives the number of hours seven randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Studying 2 2.5 3 3.5 4 5 5.5 Midterm Grades 63 67 76 78 84 85 90 Table Step 6 of 6 : Find the value of the coefficient of determination. Round your answer to three decimal places.
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age Bone Density34 35745 34148 33160 32965 325 Step 2 of 6: Find the estimated y-intercept. Round your answer to three decimal places.
The table below gives the number of hours seven randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Studying 1 1.5 2 2.5 3 3.5 4.5 Midterm Grades 61 62 75 77 79 83 88 Table Step 1 of 6 : Find the estimated slope, y intercept and correlation cofficient. Round your answer to three decimal places.
You run a regression analysis on a bivariate set of data (n=40). With ¯x = 56.7 and ¯y = 45.8, you obtain the regression equation y=−3.352x+45.294 with a correlation coefficient of r= − 0.154. You want to predict what value (on average) for the response variable will be obtained from a value of 70 as the explanatory variable.What is the predicted response value?y^ = (Report answer accurate to one decimal place.)
You run a regression analysis on a bivariate set of data (n=109). With x¯=72.2 and y¯=34.7, you obtain the regression equation y= - 0.898x - 42.514 with a correlation coefficient of r=-0.086. You want to predict what value (on average) for the response variable will be obtained from a value of 140 as the explanatory variable. What is the predicted response value? y =
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age 50 59 60 64 68 Bone Density 331 326 325 320 315 Table Step 3 of 6 : Substitute the values you found in steps 1 and 2 into the equation for the regression line to find the estimated linear model. According to this model, if the value of the independent variable is increased by one unit, then find the change in the dependent variable yˆ.
It is known that the linear regression equation: y= -2.88+1.77x, with a coefficient of determination of 0.81. Based on the two information, the correlation coefficient is
The data show the number of viewers for television stars with certain salaries. Find the regression equation, letting salary be the independent (x) variable. Find the best predicted number of viewers for a television star with a salary of $6 million. Is the result close to the actual number of viewers, 8.9 million? Use a significance level of 0.05. Salary (millions of $) Viewers (millions) Click the icon to view the critical values of the Pearson correlation coefficient r. 98 3.5 3 7 13 12 13 10 2 6.8 6.3 10.2 8.5 4.4 1.8 2.7 What is the regression equation? y=+x (Round to three decimal places as needed.) What is the best predicted number of viewers for a television star with a salary of $6 million? The best predicted number of viewers for a television star with a salary of $6 million is million. (Round to one decimal place as needed.) Is the result close to the actual number of viewers, 8.9 million? O A. The result is very close to the actual number of viewers of 8.9 million. O B. The…
You run a regression analysis on a bivariate set of data (n = 22). With ☎ = 22.3 and y = 58.5, you obtain the regression equation y = 0.842x + 37.664 with a correlation coefficient of r = 0.897. You want to predict what value (on average) for the response variable will be obtained from a value of 180 as the explanatory variable. What is the predicted response value? y = (Report answer accurate to one decimal place.)
You run a regression analysis on a bivariate set of data (n = 120). With i = 66.3 and y = 50.6, you obtain the regression equation y = 4.097x – 11.636 with a correlation coefficient of r = 0.56. You want to predict what value (on average) for the response variable will be obtained from a value of 120 as the explanatory variable. What is the predicted response value? y = (Report answer accurate to one decimal place.)
In a fisheries researchers experiment the correlation between the number of eggs in tge nest and the number of viable (surviving ) eggs for a sample of nests is r=0.67 the equation of the regression line for number of viable eggs y versus number of eggs in the nest x is y =0.72x + 17.07 for a nest with 140 eggs what is the predicted number of viable eggs ?
You run a regression analysis and obtain the regression equation y 3.176x +124.355 with a correlation coefficient of r = - 0.748. You want to predict what value (on average) for the response variable will be obtained from a value of x = 150 as the explanatory variable. What is the predicted response value? y = (Report answer accurate to one decimal place.)