Bivariate data obtained for the paired variables x and y are shown below, in the table labeled "Sample data." These data are plotted in the scatter plot in Figure1, which also displays the least-squares regression line for the data. The equation for this line is y=2.93 +0.96x.In the "Calculations" table are calculations involving the observed y-values, the mean y of these values, and the values y predicted from the regressionequation.Sample data(a)XAnswer the following.y54.3 52.658.8 65.066.4 63.670.7 67.274.277.8(c)Calculations-ỹ)²6.041831.60699.449512.974413.2350Column sum:73.3076(îŷ−y)²103.673134.36302.056430.935879.6021Column sum:250.6305(y-y)²159.76960.05762.68963.8416157.7536Column sum:324.1120(b)The total variation in the sample y-values is given by the (Choose one)(Choose one)3075+705550-55+50Figure 160 65 70 75The least-squares regression line given above is said to be a line that "best fits" the sample data. The term "bestfits" is used because the line has an equation that minimizes the (Choose one)which for thesedata is (Choose one)which for these data isThe value is the proportion of the total variation in the sample y-values that is explained by the estimated linearrelationship between x and y. For these data, the value of ² is. (Round your answer to at least 2 decimalplaces.)80X (a)The least-squares regression line given above is said to be a line that "best fits" the sample data. The term "bestfits" is used because the line has an equation that minimizes the (Choose one)which for thesedata is (Choose one) ▼total sum of squaresregression sum of sauaros

Given data: x y 54.3 52.6 58.8 65 66.4 63.6 70.7 67.2 74.2 77.8

Answered: (a) The least-squares regression line…

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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The defending attorney Mr. Justin Case was interested in how a lengthy trial could affect howlong a jury would deliberate on a case (and see if he should just cut to the chase). He observed asample of courtroom trials and noticed the following:Days inTrial (X) 5 2 6 4 5 6 2 4 2 1 HoursDeliberation (Y) 4 4 1 3 1 3 9 2 3 7 1) What is the slope of the regression line?
For major league baseball teams, do higher player payrolls mean more gate money? Here are data for each of the American League teams in the year 2002. The variable x denotes the player payroll (in millions of dollars) for the year 2002, and the variable y denotes the mean attendance (in thousands of fans) for the 81 home games that year. The data are plotted in the scatter plot below, as is the least-squares regression line. The equation for this line is y = 11.43 + 0.23x. Player payroll, x (in Mean attendance, y (in $1,000,000s) thousands) Anaheim 62.8 28.52 Baltimore 56.5 33.09 40- Boston 110.2 32.72 35 Chicago White Sox 54.5 20.74 30- Cleveland 74.9 32.35 25- Detroit 54.4 18.52 Kansas City 49.4 16.30 15- Minnesota 41.3 23.70 10+ New York Yankees 133.4 42.84 Oakland 41.9 26.79 20 40 60 80 100 120 140 Seattle 86.1 43.70 Player payroll, Тarmpa Bay 34.7 13.21 X (in $1,000,000s) Техas 106.9 29.01 Toronto 66.8 20.25 Send data to calculator Send data to Excel Based on the sample data and…
An engineer wants to determine how the weight of a gas-powered car, x, affects gas mileage, y. The accompanying data represent the weights of various domestic cars and their miles per gallon in the city for the most recent model year. Complete parts (a) through (d) below. Click here to view the weight and gas mileage data. (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable. Car Weight and MPG (Round the x coefficient to five decimal places as needed. Round the constant to two decimal places as needed.) Weight (pounds), x Miles per Gallon, y 3806 16 3796 15 2669 24 3520 18 3361 21 2911 22 3808 18 2612 24 3375 19 3737 16 3320 19
(e) Find the line of best fit (or, the regression line) for the following data points: (3,13)(3,13), (4,18)(4,18), (5,23)(5,23), and (7,24)(7,24).
Draw a graph of the least-squares regression line on your scatterplot. (For hand-drawing, round the slope and y-intercept to one decimal place before drawing the line.) Be sure to show how you were able to plot the line starting with its equation. Model City Miles per Gallon Highway Miles per Gallon Acura RLX 20 29 BMW 530i 24 34 Buick LaCrosse eAssist 25 35 Chevrolet Malibu 29 36 Ford Hybrid FWD 43 41 Honda Civic 32 42 Infiniti Q50 Red Sport 20 26 Kia Forte 30 40 Lexus ES 350 22 33 Mercedes Benz AMG S 21 30 Mini Cooper Clubman 24 32 Nissan Maxima 20 30 Suburu Legacy AWD 25 34 Toyota Prius ECO 58 53
1.64 and 1.63 is incorrect please help ()()))
We have data from 209 publicly traded companies (circa 2010) indicating sales and compensation information at the firm-level. We are interested in predicting a company's sales based on the CEO's salary. The variable sales; represents firm i's annual sales in millions of dollars. The variable salary; represents the salary of a firm i's CEO in thousands of dollars. We use least-squares to estimate the linear regression sales; = a + ßsalary; + ei and get the following regression results: . regress sales salary Source Model Residual Total sales salary cons SS 337920405 2.3180e+10 2.3518e+10 df 1 207 208 Coef. Std. Err. .9287785 .5346574 5733.917 1002.477 MS 337920405 111980203 113066454 Number of obs F (1, 207) Prob > F R-squared t P>|t| = Adj R-squared = Root MSE 1.74 0.084 5.72 0.000 = = -.1252934 3757.543 = 209 3.02 0.0838 0.0144 0.0096 10582 [95% Conf. Interval] 1.98285 7710.291 This output tells us the regression line equation is sales = 5,733.917 +0.9287785 salary. Interpret the…
18)The regression equation is intended to be the “best fitting” straight line for a set of data. Whatis the criterion for “best fitting”?
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.
Below are bivariate data giving birthrate and life expectancy information for each of twelve countries. For each of the countries, both x, the number of births per one thousand people in the population, and y, the female life expectancy (in years), are given. Also shown are the scatter plot for the data and the least-squares regression line. The equation for this line is y = 81.87– 0.46x. Birthrate, x (number of births per 1000 рop.) Female life expectancy, y (in years) 85- 35.7 67.7 80 41.5 63.9 31.9 63.3 75+ メメ 19.9 73.0 70- 50.5 60.4 65- 24.4 72.7 60- 50.1 63.2 55 13.8 72.5 50 50.3 54.6 45.6 57.9 15.9 76.2 Figure 1 26.6 71.9

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