The following data is on number of hours studied (x) (per week) and the final grade (y) (out of100) for 10 students in the course Stat-240X111216691424320y31586573374460912184givingEx = 121, Ex = 1855, Ey = 564, Ey =36562, Exy = 8157.(A) Find the equation of the least squares line that approximates the regression of the test scoreson the number of hours studied.(B) Predict the average test score of a person who studied 16 hours for the test. Compute theestimated variance of your prediction.(C) Compute the correlation coefficient and then comment.

Given data as ( x ) number of hours studied per week and the final grade ( y ). and also given that…

Answered: The following data is on number of…

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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A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y-ax+b a=-0.656 b=36.681 r2-0.877969 r=-0.937 Use this to predict the number of situps a person who watches 10 hours of TV can do (to one decimal place) Question Help: Message instructh Submit Question to search
A local retail store compared their monthly sales of umbrellas with the amount of rainfall that occured during that month. They computed the following statistics: Rainfall (in) # of umbrellas mean = 4.64 mean = 34.2 SD = 1.17 SD = 13.2 r = 0.8 1. Find the equation for the regression line that predicts the monthly sales of umbrellas from monthly rainfall.
The weight (in pounds) and height (in inches) for a child were measured every few months over a two- The equation ý = 17.4 + 0.5x is called the least- squares regression line because it year period. The results are displayed in the scatterplot. O is least able to make accurate predictions for the data. O makes the strongest association between weight and height. A Child's Weight and Height 40 O minimizes the sum of the squared distances from the actual y-value to the predicted y-value. 36 O maximizes the sum of the squared distances from the actual y-value to the predicted y-value. 32 28 24 20 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 4042 Weight (Pounds) Height (Inches)
A least squares regression line was calculated to relate the length (cm) of newborn boys to their weight in kg. The line is weight=−5.59+0.1826 length. A newborn was 48 cm long and weighed 3 kg. According to the regression model, what was his residual? What does that say about him?
The equation used to predict college GPA (range 0-4.0) is y= 0.17 +0.52x, +0.002x,, where x, is high school GPA (range 0-4.0) and X2 is college board score (range 200-800). Use the multiple regression equation to predict college GPA for a high school GPA of 3.1 and a college board score of 600.
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were: y=ax+b a=-1.383 b=24.599 r2=0.962361 r=-0.981 Use this to predict the number of situps a person who watches 5.5 hours of TV can do (to one decimal place)
A grocery store manager did a study to look at the relationship between the amount of time (in minutes) customers spend in the store and the amount of money (in dollars) they spend. The results of the survey are shown below. Time 10 8 6 13 19 18 27 10 7 Money 58 25 41 51 84 81 87 45 15 1. The equation of the linear regression line is: ˆyy^ = ______ +_____ xx (Please show your answers to two decimal places)
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…
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please show work
The table shows the number of goals allowed and the total points earned (2 polnts for a win, and 1 point for an overtme or shootout loss) by 14 lce hockey teams over the course of a season The equation of the regression line is (a) Find the coefficient of determination,, and interpret the result (b) Find the standard error of the estimate, and interpret the resut Goals Allowed, x Points, y -0.560x 218.067 Use he data to answer the folowing questions 217 213 221 229 260 262 277 205 216 20s 217 208 257 246 66 70 105 105 0 83 47106 103 97 9182 91 88 Inco (a) r-O (Round to three decimal places as noeded.)

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