The sweetness, y, of the fruit is supposed to be related to the average daily sunshine hours, x. The following datashows the sweetness of the same type of fruit at different locations (sunshine hours). Fit the data to a simple linearregression model.x: 5, 6, 7, 6, 6, 8, 7, 5.y: 9, 10, 10, 11, 12, 13, 12, 8.1Calculate Sty; SET; Syy.2Calculate the correlation between x, y. And interpret their relationship.

Answer Given X Y X*Y X^2 Y^2 5 9 45 25…

Answered: The sweetness, y, of the fruit is…

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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The following data show the brand, price ($), and the overall score for six stereo headphones that were tested by a certain magazine. The overall score is based on sound quality and effectiveness of ambient noise reduction. Scores range from 0 (lowest) to 100 (highest). The estimated regression equation for these data is ý = 22.525 + 0.325x, where x = price ($) and y overall score. %3D Brand Price ($) Score 180 78 B. 150 71 C 95 59 70 54 E 70 40 35 28 (a) Compute SST, SSR, and SSE. (Round your answers to three decimal places.) SST SSR %3D SSE = (b) Compute the coefficient of determination . (Round your answer to three decimal places.) 1 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares line. O The least squares line did not provide a good fit as a large proportion of the variability in y…
Q5/ Use Linear Regression to fit the following data: X 1 2 4 5 6 Y 4 10 10 9 3
A researcher is investigating possible explanations for deaths in traffic accidents. He examined data from 2000 for each of the 52 cities randomly selected in the US. The variables were death and income. Deaths: The number of deaths in traffic accidents per cityIncome: The median income per city The researcher ran a simple linear regression model: Deaths = Bo+B1(Income). Results shown in photo below. Question: Please help me better understand how to use results from photo to find value of R-squared of this simple linear regression model.
Tire pressure (psi) and mileage (mpg) were recorded for a random sample of seven cars of thesame make and model. The extended data table (left) and fit model report (right) are based on aquadratic model. Calculate R2. Describe what this value means in the context of the problem.
The following data show the brand, price ($), and the overall score for six stereo headphones that were tested by a certain magazine. The overall score is based on sound quality and effectiveness of ambient noise reduction. Scores range from 0 (lowest) to 100 (highest). The estimated regression equation for these data is ŷ = 22.391 + 0.326x, where x = price ($) and y = overall score. Brand Price ($) Score A 180 76 B 150 71 C 95 63 D 70 56 E 70 40 F 35 24 #1) Compute SST, SSR, and SSE. (Round your answers to three decimal places.) SST = SSR = SSE = #2) Compute the coefficient of determination r2.(Round your answer to three decimal places.) r2 = #2a) Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) A) The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line. B) The least squares line provided a good fit as a large…
A linear relationship between EmployeeSalary (Dependent) and degree(independent) has the following equation : Salary = 400+0.2 (Degree). SST= 736, SSR= 385. Calculate and interpret the coefficient of determination (r2) : Select one: O a. 0.48 , 47.69 percent of the variability in employee salary can be explained by the simple linear regression equation Ob. 0.52,52.31 percent of the variability in employee salary can be explained by the simple linear regression equation Oc. 0.48, 47.69 percent of the variability in the degree earned can be explained by the simple linear regression equation F Od. 0.52, 52.31 percent of the variability in the degree earned can be explained by the simple linear regression equation Next page JUN 2 12 étv W Ps Lr
9 students were surveyed to see what their age is and what their income level is. Find the equation of the line using linear regression. We want to predict their age using their income. age 18 24 38 22 19 35 28 19 27 income 456 786 835 855 645 244 587 1400 975 Just a side note, the 19 year old is making 1400, would be considered an outlier since they are making way more than everyone else. (y=-.0061x+34.9874 4 decimals) y=30.3709-.0064
Please answer letters b-e.
The accompanying data represent the weights of various domestic cars and their gas mileages in the city. The linear correlation coefficient between the weight of a car and its miles per gallon in the city is r= - 0.972. The least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable is y= - 0.0070x + 44.4405. Complete parts (a) and (b) below. Click the icon to view the data table. ..... (a) What proportion of the variability in miles per gallon is explained by the relation between weight of the car and miles per gallon? The proportion of the variability in miles per gallon explained by the relation between weight of the car and miles per gallon is %. (Round to one decimal place as needed.) (b) Interpret the coefficient of determination. % of the variance in is by the linear model. Data Table (Round to one decimal p Full data set gas mileage Miles per Weight (pounds), x Weight (pounds), x Miles per Gallon, y Car Car Gallon, y…
The following data show the brand, price ($), and the overall score for six stereo headphones that were tested by a certain magazine. The overall score is based on sound quality and effectiveness of ambient noise reduction. Scores range from 0 (lowest) to 100 (highest). The estimated regression equation for these data is ŷ = 21.592 + 0.324x, where x = price ($) and y = overall score. Brand Price ($) Score A 180 76 B 150 69 C 95 61 D 70 56 E 70 38 F 35 24 (a) Compute SST, SSR, and SSE. (Round your answers to three decimal places.) (b) Compute the coefficient of determination r2. (Round your answer to three decimal places.) (c) What is the value of the sample correlation coefficient? (Round your answer to three decimal places.)

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