For the data set shown below, complete parts (a) through (d) below.x| 20 30 40 50 60-y 102 95 93 81 68Se -U(Round to four decimal places as needed.)(c) Assuming the residuals are normally distributed, use technology to determine sp. -(Round to four decimal places as needed.)(d) Assuming the residuals are normally distributed, test Ho: B, = 0 versus H,: ß, #0 at the a = 0.05 level of significance. Use the P-Determine the P-value for this hypothesis test.P-value = (Round to three decimal places as needed.)Which of the following conclusions is correct?O A. Do not reject Ho and conclude that a linear relation does not exist between x and y.O B. Reject H, and conclude that a linear relation does not exist between x and y.O C. Reject Ho and conclude that a linear relation exists between x and y.O D. Do not reject H, and conclude that a linear relation exists between x and y. (c) Draw a scatter diagram, treating year as the explanatory variable. Choose the correct graph below.OA.OB.OC.2ж 101x 10190020001900200019002000Year, xYear, xYear, xPopulation, yPopulation, yPopulation, y

Use the Excel and Perform the Regression. Followings are the Steps to perform the Regression 1) Open…

Answered: For the data set shown below, complete…

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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Given are five observations for two variables, x and y. Use the estimated regression equation: y = 73.9561 - 3.1463x (NEED ANSWERS FOR A, B, C, D, E)
pls only answer b-c i am having a hard time understanding those and would like to learn esspecially with plotting residuals. pls use ms excel and thank u
The following data are the monthly salaries and the grade point averages for students who obtained a bachelor's degree in mathematics. GPA: 2.6, 3.4, 3.6, 3.2, 3.5, and 2.9; Monthly Salary: 330o, 3600, 4000, 3500, 3900, and 3600. Plot the scatter diagram showing the deviations about the estimated regression line and the line y = y(bar).
Solve for the predicted values of y and the residuals for the following data. x 12 21 28 8 20 y 17 15 22 19 24 Do not round the intermediate values. (Round your answers to 3 decimal places.) x y Predicted (ŷ) Residuals (y-ŷ) 12 17 21 15 28 22 8 19 20 24
The commercial division of a real estate firm is conducting a regression analysis of the relationship between x, annual gross rents (in thousands of dollars), and y, selling price (in thousands of dollars) for apartment buildings. Data were collected on several properties recently sold and the following computer output was obtained. Analysis of Variance SOURCE DF Adj SS Regression 1 41587.3 Error 7 Total 8 51984.1 Predictor Coef SE Coef T-Value Constant 20.000 3.2213 6.21 X 7.210 1.3626 5.29 Regression Equation Y = 20.0 + 7.21 X (a) How many apartment buildings were in the sample? (b) Write the estimated regression equation. ŷ = (c) What is the value of sb1? Find the value of the test statistic. (Round your answer to two decimal places.) Find the p-value. (Round your answer to three decimal places.) p-value =
Interpret the following three sets of data (X versus Y) using scatter chart and regression analyze X: C16 (number of cars on the sales lot) versus Y: C17 (cars sold per day)
Calculate the coefficient of variation between X and Y from the following data: 3 4. 6. Y 9. 10 12 11 13 14 16 重5
Data on the price ($1000s) and the size (1000 ft2 sqaured) for 156 randomly selected houses that have been sold is summarized in the accompanying regression output and plots. Complete parts a and b a) Check the assumptions and conditions for inference. Select all that apply. A. The residuals look random. B. The residuals are nearly normal. C. The scatterplot looks straight enough. D. The residual plot and the scatterplot show consistent variability. E. None of the assumptions and conditions for inference are satisfied.
An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.
11. For temperature (x) and number of ice cream cones sold per hour (y). (65, 8), (70, 10), (75, 11), (80,13), (85, 12), (90, 16). Interpret the coefficient of determination. Optional Answers: 1. 88.2% of the variability in the number of cones sold is explained by the least-squares regression model. 2. 93.9% of the variability in the number of cones sold is explained by the least-squares regression model. 3. 88.2% of the variability in the temperature is explained by the least-squares regression model. 4. 93.9% of the variability in the temperature is explained by the least-squares regression model.
please do this urgently
Calculate the co-efficient of correlation and the lines of regression for the following data: 1 2 3 4 5 7 8 9 X= y= 8 10 12 11 13 14 16 15. Obtain an estimate of y which should correspond on the average to x= 6. 2.

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