A researcher wishes to examine the relationship between years of schooling completed and thenumber of pregnancies in young women. Her research discovers a linear relationship, and the leastsquares line is: ŷ = 2 – 3xwhere x is the number of years of schooling completed and y is thenumber of pregnancies. The slope of the regression line can be interpreted in the following way:%3DO When amount of schooling increases by one year, the number of pregnancies decreases by 2.O When amount of schooling increases by one year, the number of pregnancies increases by 2.O When amount of schooling increases by one year, the number of pregnancies decreases by 3.O When amount of schooling increases by one year, the number of pregnancies increases by 3.

From the given information, the regression equation is, y^=2-3x x is the number of years of…

Answered: A researcher wishes to examine the…

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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10) Using advertised prices for used Ford Escorts a linear model for the relationship between a car's age and its price is found. The regression has an R2-87.4%. Write a sentence summarizing what R2 says about this regression. A) The age of the car explains 9.35% of the variation in price. B) The price of the car explains 12.6% of the variation in age. C) The age of the car explains 87.4% of the variation in price. D) The price of the car explains 87.4% of the variation in age. E) The age of the car explains 12.6% of the variation in price. Solve. Round to two decimal places if necessary. 11) At one college, 68% of students have credit cards. A credit card company, looking for new customers, sets up a booth at the college. If they pick students at random, how many students should they expect to talk to before finding one who does not have a credit card? A) 32 B) 3.13 D) 68 C) 0.32 E) 1.47
The value of a sports franchise is directly related to the amount of revenue that a franchise can generate. Below is the data that represents the value (in $millions) and the annual revenue (in $millions) for 30 Major League Baseball franchises. Suppose you want to develop a simple linear regression model to predict franchise value based on annual revenue generated. Team Revenue Value Baltimore 179 460 Boston 310 1000 Chicago White Sox 214 600 Cleveland 178 410 Detroit 217 478 Kansas City 161 354 Los Angeles Angels 226 656 Minnesota 213 510 New York Yankees 439 1850 Oakland 160 321 Seattle 210 585 Tampa Bay 161 323 Texas 233 674 Toronto 188 413 Arizona 186 447 Atlanta 203 508 Chicago Cubs 266 879 Cincinnati 185 424 Colorado 193 464 Houston 196 549 Los Angeles 230 1400 Miami 148 450 Milwaukee 195…
A researcher wanted to predict the sodium content (in milligrams) of beef hot dogs by looking at the calorie content. His findings from a sample of 10 beef hot dogs are summarized below. The regression equation for his data is ŷ= -299.48 + 4.35 x Calories 186 181 176 149 184 190 158 139 175 148 Sodium 495 477 425 322 482 587 370 322 479 375 Interpret the slope in terms of this problem. If appropriate, find the sodium content if the calorie content is 170. If not, why? If appropriate, find the sodium content if the calorie content is 136. If not, why?
A professor in the School of Business in a university polled a dozen colleagues about the number of professional meetings they attended in the past five years (x) and the number of papers they submitted to refereed journals (y) during the same period. The summary data are provided. Fit a simple linear regression model between x and y by finding out the estimates of intercept and slope. Comment on whether attending more professional meetings would result in publishing more papers. n n n = 12, x = 4, y = 12, X₁Y₁ = 320 i=1 x² = 234, i=1 The estimated regression line is y = 36.4 + (-6.10 )x. (Round to two decimal places as needed.) Comment on whether attending more professional meetings would result in publishing more papers. Let it be unclear that attending more professional meetings affects the number of published papers if the value used to make that determination is between 1 and 1. Because the of the regression equation is it would appear that attending more professional meetings ▼
A real estate analyst has developed a multiple regression line, y = 60 + 0.068 x1 – 2.5 x2, to predict y = the market price of a home (in $1,000s), using independent variables, x1 = the total number of square feet of living space, and x2 = the age of the house in years. The regression coefficient of x2 suggests this: __________. If the square feet area of living space is kept constant, a 1 year increase in the age of the homes will result in a predicted drop of $2500 in the price of the homes If the square feet area of living space is kept constant, a 1 year increase in the age of the homes will result in a predicted increase of $2500 in the price of the homes Whatever be the square feet area of the living space, a 1 year increase in the age of the homes will result in a predicted increase of $2500 in the price of the homes Whatever be the square feet area of the living space, a 1 year increase in the age of the homes will result in a predicted drop of $2500 in the price of the homes
A researcher wishes to examine the relationship between years of schooling completed and the number of pregnancies in young women. Her research discovers a linear relationship, and the least squares line is: ŷ = 25x where x is the number of years of schooling completed and y is the number of pregnancies. The slope of the regression line can be interpreted in the following way: When amount of schooling increases by one year, the number of pregnancies tends to increase by 5. When amount of schooling increases by one year, the number of pregnancies tends to decrease by 5. When amount of schooling increases by one year, the number of pregnancies tends to increase by 2. When amount of schooling increases by one year, the number of pregnancies tends to decrease by 2.
A biology student is investigating the claim that the temperature can be predicted by counting cricket chirps. She has found a linear regression equation T=42.2+0.21r, where T is the temperature in degrees Fahrenheit and r is the number of chirps per minute. What is the predicted temperature for 90 chirps per minute? 18.9 degrees 227.6 degrees 23.3 degrees 61.1 degrees
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The value of a sports franchise is directly related to the amount of revenue that a franchise can generate. The following data represents the value in 2014 (in $millions) and the annual revenue (in $millions) for the 30 Major League Baseball franchises. Suppose you want to develop a simple linear regression model to predict franchise value based on annual revenue generated. Team Revenue Value Baltimore 245 1000 Boston 370 2100 Chicago White Sox 227 975 Cleveland 207 825 Detroit 254 1125 Houston 175 800 Kansas City 231 700 Los Angeles Angels 304 1300 Minnesota 223 895 New York Yankees 508 3200 Oakland 202 725 Seattle 250 1100 Tampa Bay 188 625 Texas 266 1220 Toronto 226 870 Arizona 211 840 Atlanta 267 1150 Chicago Cubs 302 1800 Cincinnati 227 885 Colorado…
Use the following information to answer the question. The following linear regression model can be used to predict ticket sales at a popular water park. Ticket sales per hour = -631.25 + 11.25(current temperature in °F) What is the predicted number of tickets sold per hour if the temperature is 86°F? Round to the nearest whole ticket. O About 336 tickets O About 252 tickets O About 276 tickets About 301 tickets
For nine months, Joan observes the average daily gas consumption for her house and the corresponding average temperature that month. She gets the following scatterplot (with regression line equation presented and drawn in): After collecting the data, Joan had insulation installed in her home. The next month, the average temperature was 30 and her home (with insulation) consumed 682 gas daily. Approximately how much gas did the insulation save her daily that month? Which of the terms does apply to the relationship between temperature and gas consumed?

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