You determined there was a positive correlation between the numberof study hours a week a student spends studying and their overallgrade at the end of the semester.You determined the regression equation: = 1.806x+40, where x isthe number of hours a student spends studying each week.What would be the predicted overall test score for a student whostudies 14 hours a week?Round your answer to 3 decimal places if needed.9ΣTh

The regression equation is y^=1.806x+40, where x is the number of hours a student spends studying…

Answered: You determined there was a positive…

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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After interviewing salespersons at Harley Davidson dealerships, a researcher has created a linear regression line to explain the relationship between a Harley Davidson motorcycle's age (x) and price (y). The regression has an R² = 87.7%. Write a sentence summarizing what R² says about this regression. The age of the motorcycle explains 12.3% of the variation in price. The age of the motorcycle explains 9.36% of the variation in price. The age of the motorcycle explains 87.7% of the variation in price. The price of the motorcycle explains 12.3% of the variation in age. The price of the motorcycle explains 87.7% of the variation in age.
A set of X and Y scores has SSX = 26, SSY = 10, and SP = 42. What is the slope for the regression equation? Round your answer to 2 decimal places.
A pediatrician wants to determine the relation that exists between a child's height (x) and head circumference (y). She randomly selects 11 children from her practice and measures their height and head circumference in inches. She finds that the correlation is 0.477 and the regression equation is y = 0.149x + 8.15 What proportion of the variation in head circumference can be explained by the variation in the values of height? % Report answer as a percentage accurate to one decimal place.
Here are two sets of bivariate data with the same response variable. The first contains the variables x & y. The second contains the variables w & y | y w y 25.3 43.9 68.8 36.7 43.8 56.7 57 55 49.2 61.3 27.9 52 57.4 69.3 48 | 50.5 33.7 47.3 58.3 49.2 46.3 56 46.6 34.4 67.1 75.8 35.1 48.3 25.7 43.4 19 76.8 47.2 57.9 7.3 56.8 37.5 57.4 32.9 56.2 44.8 60.4 43.8 49 29.7 38.5 26.4 | 60.9 42.8 53.6 55.6 49.4 60.3 69.7 53.9 55 53.2 67.1 47.8 42.7 57.4 66.3 25 54.8 45.5 57.6 13.9 65 42 51.6 69.5 43.6 38.9 54.8 79.5 29
The line of best fit through a set of data is y = -23.849 + 0.834x According to this equation, what is the predicted value of the dependent variable when the independent variable has value 10? y = decimal place. Round to 1
Mary used a sample of 68 large U.S. cities to estimate the relationship between Crime (annual property crimes per 100,000 persons) and Income (median annual income per capita, in dollars). Her estimated regression equation was Crime = 428 + 0.050 Income. If Income decreases by 500, we would expect that Crime will Group of answer choices remain unchanged. decrease by 25. increase by 500. decrease by 50.
Use the data below for the PHONES questions that follow. Data compiled from the CIA World Factbook, https://www.cia.gov/. The number of cell phones in use in a country can be approximated from the number of main lines in use in that country by using the regression equation yhat=25+3x which had an R² of about 90%. (The numbers here are millions. For example, "1.2" would represent 1.2 million or 1,200,000.) PHONES 1 At the time the data was collected, the USA had about 314 million cell phones in use and about 150 million main lines in use. What was the residual for the USA? Enter a number of millions. For example, if your answer is 2.85 million enter "2.85". Do not enter anything else such as symbols or words.
The arm span and foot length were both measured (in centimeters) for each of 20 students in a biology class. The computer output displays the regression analysis. Which of the following is the best interpretation of the coefficient of determination r2? About 37% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 65% of the variation in foot length is accounted for by the linear relationship formed with the arm span. About 63% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 63% of the variation in foot length is accounted for by the linear relationship formed with the arm span.
A pediatrician wants to determine the relation that exists between a child's height (x) and head circumference (y). She randomly selects 11 children from her practice and measures their height and head circumference in inches. She finds that the correlation is 0.694, and the regression equation is y = 0.294x + 2.02. What proportion of the variation in head circumference can be explained by the variation in the values of height? Round your answer to three decimal places. %
Write down the two regression equation.
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and the number of sit-ups a person can do (y). The results were: Y a+bx b = -0.79 = 23.04 a r² = 0.4493 r = -0.6703 a. If a person watches 10 hours of television a day, predict how many sit-ups he can do. b. If a person can do 7 sit ups, predict how many hours of television a day they watch. hours
Use B = -15.812961, and ₁ = 0.096300 to determine the equation of the estimated regression line. (Round your numerical answers to four decimal places.) ŷ =

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