A fourth order regression model was built for a total of 45 students' grades in the numerical class. If the standard error of the estimate for the model was 0.438 and the total sum of the squares of the residual was 12.07, determine the coefficient of correlation: Round to 2 decimal at least Answer:

A fourth order regression model was built for a total of 45 students' grades in the numerical class. If the standard error of the estimate for the model was 0.438 and the total sum of the squares of the residual was 12.07, determine the coefficient of correlation: Round to 2 decimal at least Answer:

Name: A fourth order regression model was built for a total of 45 students'
Uploaded: 2024-03-20T07:07:12.000Z
Channel: Bartleby
Description: A fourth order regression model was built for a total of 45 students' grades in the numericalclass. If the standard error of the estimate for the model was 0.438 and the total sum of theof the residual was 12.07, determine the coefficient of correla

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter4: Linear Functions

Section: Chapter Questions

Problem 30PT: For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or...

See similar textbooks

Similar questions

For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or older who are college graduates in a particular city, by year. Based on the set of data given in Table 5, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient. Round to three decimal places of accuracy
If your graphing calculator is capable of computing a least-squares sinusoidal regression model, use it to find a second model for the data. Graph this new equation along with your first model. How do they compare?
Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?