Consider the following.XyX332.11125.96432.421223.88532.941324.3632.461424.22730.981522.148Determine the correlation coefficient to three decimal places of accuracy.27.81622.96926.721721.481028.7418yUse the set of data to calculate the regression line using a calculator or other technology tool. (Round the coefficients to three decimal places.)y =19.3

Answered: Image /qna-images/answer/3b536a86-b74e-4077-a87b-c3997cbddd22.jpg

Answered: X y X 3 32.1 11 4 32.42 12 5 32.94 13 6…

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

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The personnel director of a large hospital is interested in determining the relationship (if any) between an employee’s age and the number of sick days the employee takes per year. The director randomly selects ten employees and records their age and the number of sick days which they took in the previous year. Employee 1 2 3 4 5 6 7 8 9 10 Age 30 50 40 55 30 28 60 25 30 45 Sick Days 7 4 3 2 9 10 0 8 5 2 Copy Data The estimated regression line and the standard error are given. Sick Days=14.310162−0.2369(Age) se=1.682207se=1.682207 Find the 95% confidence interval for the average number of sick days an employee will take per year, given the employee is 26. Round your answer to two decimal places.
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A. run a simple regression- dependent variable is Weeks, independent variable is Age. B. run a multiple regression with dependent variable weeks and independent variable-age, married, head, manager and sales. C. Create the regular and standardized residual plots for both. Please show the tables when entering values of the regression for both the outputs and the scatter plots.
Bluereef real estate agent wants to form a relationship between the prices of houses, how many bedrooms, House size in sq ft and Lot Size in sq ft. The data pertaining to 100 houses were processed using MINITAB and the following is an extract of the output obtained: The regression equation is Price = B + ¢Bedroom + yHouse Size + ALot Size Predictor Coef SE Coef т P 37718 14177 2.66 ** Constant Bedrooms 2306 6994 0.33 0.742 House Size 74.3 52.98 0.164 Lot Size -4.36 17.02 -0.26 0.798 S= 25023 R-Sq=56.0% R-Sq (adj) =54.6% Source DF SS MS F P Regression 3 76501718347 25500572782 *** **** Residual 96 60109046053 626135896 Error Total 99 a) Write out the regression equation. b) Fill in the missing values *, ** *** and ****
Based on the data shown below, calculate the regression line and the correlation coefficient. Round each value to three decimal places. y = 14.43 15.86 21.09 4 23.42 22.35 6. 25.08 29.31 8 31.54 9. 31.97 10 36.3 11 39.93 一 N
Run a regression analysis on the following bivariate set of data with y as the response variable. x y 58.3 81.3 77.7 48.5 49.3 67.2 68.5 58.9 46.4 93.1 65.5 75.8 48.9 90.5 65.2 66.9 63.2 79.4 56.5 89.3 65.6 75.5 40.4 96.2 Find 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 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.)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 77.8 as the explanatory variable. Use a significance level of α=0.05 to assess the strength of the linear correlation.What is the predicted response value? (Report answer accurate to one decimal place.)y =
The correlation between first year college GPA and high school GPA is 0.683. If a simple linear regression was conducted to predict first year college GPA from high school GPA what does the correlation tell us about the fit of the model?
Run a regression analysis on the following bivariate set of data with y as the response variable. x y 68.5 65.9 88.3 71.6 66.1 61.2 63.6 59.1 67.6 56.7 34.3 47.4 65.4 58.4 59.6 61.1 60.6 54.6 55.2 59.5 67.1 60.7 60.6 57.5 Find 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 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.)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 81.6 as the explanatory variable. Use a significance level of α=0.05α=0.05 to assess the strength of the linear correlation.What is the predicted response value? (Report answer accurate to one decimal place.)y =…
Let x be the weight of a car (in hundreds of pounds), and let y be the miles per gallon (mpg). Suppose a car weighs 3800 pounds. What does the data forecast for the miles per gallon expected? x= 27 44 32 47 23 40 34 52 y= 30 19 24 13 29 17 21 14 State the correlation coefficient and state the regression lin equation.
Run a regression analysis on the following bivariate set of data with y as the response variable. y 62.8 75.2 60 70.4 59.8 63 59.3 68.5 59 70.3 52.4 50.8 63.4 61.9 70.3 68.1 65.6 69.3 56.8 60.9 67.5 77.5 55.5 57.2 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.) r2 = Based on the data, calculate the regression line (each value to three decimal places) y = Predict what value (on average) for the response variable will be obtained from a value of 52.5 as the explanatory variable. Use a significance level of a = 0.05 to assess the strength of the linear correlation. What is the predicted response value? (Report answer accurate to one decimal place.) y = Submit Question IO 01 100 O Details
Use the given data to find the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line. 11 8 11 13 6 13 6 y 7.24 6.87 12.78 6.87 7.75 8.85 6.28 5.61 8.28 6.72 5.52 Create a scatterplot of the data. Choose the correct graph below. VA. OB. OC. OD. 25- Ay 25- 25- 25- 20- 20 20- 20 15 155 155 155 10- 10- 10 10- 5- 5- 5 55 0- 05 10 15 20 25 0- O 5 10 15 20 25 04 05 10 15 20 25 0- 05 10 15 20 25 Find the equation of the regression line. (Round the constant two decimal places as needed. Round the coefficient to three decimal places as needed.)
Use the value of the linear correlation coefficient to calculate the coefficient of determination. What does this tell you about the explained variation of the data about the regression line? About the unexplained variation?

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