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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Hello. How do you calculate this question on a TI-83? Thanks.
4. From the data given below, find the number of items. Er = 90 r = 0.5 Exy = 120 Oy = 8 (where x and y are deviations from arithmetic average).
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).
Given the following summary statistics of the data: Σχ 77, Σy = 161, Σχ=621, Σ= 2643, Σχy= 1258, n= 10 a = 11.087, b=0.651 %3D calculate the total, error and regression sums of squares. TSS = ESS = %3D RSS = (Round to three decimal places as needed.)
The regression equation is: ŷ = 67.16 + 8.417x where ŷ is the miles traveled, and x is the MPG. The sample size used was all 110 MPG records. The correlation coefficient r = 0.620. Use the information to obtain an estimate of my mileage if my MPG is 22. Is it option: a.) cannot estimate ŷ rcrit = 0.195; the correlation IS NOT significant b.) ŷ = 252.33 rcrit = 0.195; the correlation IS significant c.) ŷ = 252.33 rcrit = 0.187; the correlation IS significant d.) cannot estimate ŷ rcrit = 0.187; the correlation IS NOT significant
A movie studio wishes to determine the relationship between the revenue from rental of comedies on streaming services and the revenue generated from the theatrical release of such movies. The studio has the following bivariate data from a sample of fifteen comedies released over the past five years. These data give the revenue x from theatrical release (in millions of dollars) and the revenue y from streaming service rentals (in millions of dollars) for each of the fifteen movies. Also shown are the scatter plot and the least-squares regression line for the data. The equation for this line is y = 3.58 +0.15x. Theater revenue, x (in millions of dollars) 26.4 36.7 43.5 31.6 60.4 14.8 21.3 49.3 7.7 24.8 27.8 12.9 60.6 26.4 66.4 Send data to calculator V Rental revenue, y (in millions of dollars) 8.1 12.4 6.8 5.1 15.4 2.6 5.8 16.5 2.2 9.1 3.3 9.8 10.8 11.7 9.6 Based on the studio's data and the regression line, complete the following. Rental revenue in millions of dollars) 0 X 10 X 20…
ANSWER D ONLY
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 =
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.
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.
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