The least-squares regression equation is y = 650.9x + 16,443 where yis the median income and x is the percentage of 25 years and olderwith at least a bachelor's degree in the region. The scatter diagram55000-indicates a linear relation between the two variables with a correlationcoefficient of 0.7174. Complete parts (a) through (d).25000-15 20 25 30 35 40 45 50 55 60Bachelor's %(c) Interpret the slope. Select the correct choice below and fill in the answer box to complete your choice.(Type an integer or decimal. Do not round.)A. For 0% of adults having a bachelor's degree, the median income is predicted to be $B. For a median income of $0, the percent of adults with a bachelor's degree is%.O C. For every dollar increase in median income, the percent of adults having at least a bachelor's degree is%, on average.O D. For every percent increase in adults having at least a bachelor's degree, the median income increases by $, on average.Median Incomeof

Regression equation is used to relate the response variable with the explanatory variable. The…

Answered: The least-squares regression equation…

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 data show The chest size and weight of several bears find regression equation, letting chest size be the independent variable then find the best predicted weight of a bear with a chest size of 51 inches is the result close to the actual weight of 422 pounds use a significance level of 0.05 Chest size : 45, 50, 43, 43, 52, 52. Weight : 352, 374, 275, 314, 440, 367.
A least squares regression line was calculated to relate the length (cm) of newborn boys to their weight in kg. The line is weight = -5.25 +0.1696 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 table lists the average tuition and fees at private colleges and universities for selected years. Year 1985 1990 1995 2000 2008 5311 25,115 Tuition and Fees (in dollars) 9375 SO 12,418 (a) Find the equation of the least-squares regression line that models the data. y 840.450 (Type the slope as a decimal rounded to three decimal places. Round the y-intercept to the nearest integer.) (b) Graph the data and the regression line in the same viewing window using the parameters given below the graph choices. Choose the correct graph below. OA. O B. o HE RO OC. 16,230 A o ƠN
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…
An online retailer examined their transactional database to see how the value of total annual purchases ($) for individual customers was related to their annual income ($). They obtained the following regression model:Total annual purchases = -49.80 + 0.0246(annual income)correlation = 0.533(a) What does the slope tell us? (Annual Income/ Total Annual Purchases) is/are predicted to increase by ($0.0246 / -$49.80) for each additional $1 of (Annual Income/ Total Annual Purchases).(b) What does the intercept tell us? A.The intercept does not have a practical meaning in this context. B. Total annual purchases are predicted to be $0.0246 when annual income = 0. C. Total annual purchases are predicted to be $-49.80 when annual income = 0. D. Annual income is predicted to be $0.0246 when total annual purchases = 0. E. Annual income is predicted to be $-49.80 when total annual purchases = 0. (c) Which statement is correct concerning the quality of this model? A. 28.4% of the variability in…
In a fisheries researchers experiment the correlation between the number of eggs in tge nest and the number of viable (surviving ) eggs for a sample of nests is r=0.67 the equation of the regression line for number of viable eggs y versus number of eggs in the nest x is y =0.72x + 17.07 for a nest with 140 eggs what is the predicted number of viable eggs ?
A linear regression analysis reveals a strong, negative linear relationship between x and y. Which of the following could possibly be the results from this analysis? (A) ŷ 13.1 27.4x, r = 0.85 (B) == 27.4+ 13.1x, r = -0.95 (D) == (C) ŷ 13.1+ 27.4x, r = 0.95 542 385x, r = -0.15 (E) 0.85 0.25x, r = -0.85

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