A scientist collected data on the mating call frequency of frogs and temperature. They found theleast squares line to be: y = 3 + 5x where x is the temperature (in Celsius) and y is mating callfrequency. The slope of the regression line can be interpreted in the following way:OWhen temperature decreases by one degree, the mating call frequency increases by 5.When temperature increases by one degree, the mating call frequency increases by 3.O When temperature increases by one degree, the mating call frequency increases by 5.O When temperature decreases by one degree, the mating call frequency increases by 3.OWhen temperature increases by one degree, the mating call frequency decreases by 5.Submit Question

Given data least square regression line : y^ = 3 + 5xhere x is temperature (in celsius) and y is…

A scientist collected data on the mating call frequency of frogs and temperature. They found the least squares line to be: y = 3 + 5x where is the temperature (in Celsius) and y is mating call frequency. The slope of the regression line can be interpreted in the following way: OWhen temperature decreases by one degree, the mating call frequency increases by 5. O When temperature increases by one degree, the mating call frequency increases by 3. OWhen temperature increases by one degree, the mating call frequency increases by 5. OWhen temperature decreases by one degree, the mating call frequency increases by 3. OWhen temperature increases by one degree, the mating call frequency decreases by 5. Submit Question

A scientist collected data on the mating call frequency of frogs and temperature. They found the least squares line to be: y = 3 + 5x where is the temperature (in Celsius) and y is mating call frequency. The slope of the regression line can be interpreted in the following way: OWhen temperature decreases by one degree, the mating call frequency increases by 5. O When temperature increases by one degree, the mating call frequency increases by 3. OWhen temperature increases by one degree, the mating call frequency increases by 5. OWhen temperature decreases by one degree, the mating call frequency increases by 3. OWhen temperature increases by one degree, the mating call frequency decreases by 5. Submit Question

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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estion 7 of 15 Suppose the manager of a gas station monitors how many bags of ice he sells daily along with recording the highest temperature each day during the summer. The data are plotted with temperature, in degrees Fahrenheit (°F), as the explanatory variable and the number of ice bags sold on as the response variable. The least squares regression (LSR) line for the data is y = -114.05 +2.17x. On one of the observed days, the temperature was 82 °F and 66 bags of ice were sold. Determine the number of bags of ice predicted to be sold by the LSR line, ŷ, when the temperature is 82 °F. Enter your answer as a whole number, rounding if necessary. ice bags Using the predicted value you just found, compute the residual at this temperature. residual = ice bags DOLL
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 = 2 – 3xwhere 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: %3D O 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.
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 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: i = 2 – 5z 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: O When amount of schooling increases by one year, the number of pregnancies tends to decrease by 5. O When amount of schooling increases by one year, the number of pregnancies tends to increase by 5. O When amount of schooling increases by one year, the number of pregnancies tends to decrease by ns 2. O When amount of schooling increases by one year, the number of pregnancies tends to increase by 2. urse Submit Question math.com/assess2/3/skip/10 here to search R
We use the form ŷ = a + bx for the least-squares line. In some computer printouts, the least-squares equation is not given directly. Instead, the value of the constant a is given, and the coefficient b of the explanatory or predictor variable is displayed. Sometimes a is referred to as the constant, and sometimes as the intercept. Data from a report showed the following relationship between elevation (in thousands of feet) and average number of frost-free days per year in a state. A Minitab printout provides the following information. Predictor Coef SE Coef T P Constant 316.62 28.31 11.24 0.002 Elevation -30.516 3.511 -8.79 0.003 S = 11.8603 R-Sq = 96.2% The printout gives the value of the coefficient of determination r2. What is the value of r? Be sure to give the correct sign for r based on the sign of b. (Round your answer to four decimal places.) What percentage of the variation in y can be explained by the corresponding variation in x and the least-squares…
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Suppose Wesley is a marine biologist who is interested in the relationship between the age and the size of male Dungeness crabs. Wesley collects data on 1,000 crabs and uses the data to develop the following least-squares regression line where X is the age of the crab in months and Y is the predicted value of Y, the size of the male crab in cm. Y = 8.2052 + 0.5693X What is the value of Ý when a male crab is 21.7865 months old? Provide your answer with precision to two decimal places. Interpret the value of Ý. The value of Ý is the probability that a crab will be 21.7865 months old. the predicted number of crabs out of the 1,000 crabs collected that will be 21.7865 months old. the predicted incremental increase in size for every increase in age by 21.7865 months. the predicted size of a crab when it is 21.7865 months old.
We use the form ŷ = a + bx for the least-squares line. In some computer printouts, the least-squares equation is not given directly. Instead, the value of the constant a is given, and the coefficient b of the explanatory or predictor variable is displayed. Sometimes a is referred to as the constant, and sometimes as the intercept. Data from a report showed the following relationship between elevation (in thousands of feet) and average number of frost-free days per year in a state. A Minitab printout provides the following information. Predictor Сoef SE Coef T Constant 315.81 28.31 11.24 0.002 Elevation -31.650 3.511 -8.79 0.003 S = 11.8603 R-Sq = 94.6% Notice that "Elevation" is listed under "Predictor." This means that elevation is the explanatory variable x. Its coefficient is the slope b. "Constant" refers to a in the equation ŷ = a + bx. (a) Use the printout to write the least-squares equation. ŷ : + %| (b) For each 1000-foot increase in elevation, how many fewer frost-free days…
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: ý = 2-5z 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: O When amount of schooling increases by one year, the number of pregnancies tends to decrease by 5. O When amount of schooling increases by one year, the number of pregnancies tends to increase by 5. ns O When amount of schooling increases by one year, the number of pregnancies tends to decrease by 2. re O When amount of schooling increases by one year, the number of pregnancies tends to increase by 2. urse Submit Question here to search 立
We use the form ý = a + bx for the least-squares line. In some computer printouts, the least-squares equation is not given directly. Instead, the value of the constant a is given, and the coefficient b of the explanatory or predictor variable is displayed. Sometimes a is referred to as the constant, and sometimes as the intercept. Data from a report showed the following relationship between elevation (in thousands of feet) and average number of frost-free days per year in a state. %3D A Minitab printout provides the following information. Predictor Сoef SE Coef P Constant 315.54 28.31 11.24 0.002 Elevation -28.950 3.511 -8.79 0.003 S = 11.8603 R-Sq = 96.2% Notice that "Elevation" is listed under "Predictor." This means that elevation is the explanatory variable x. Its coefficient is the slope b. "Constant" refers to a in the equation ŷ = a + bx. (a) Use the printout to write the least-squares equation. = 315.54 X x (b) For each 1000-foot increase in elevation, how many fewer frost-free…
Jackson finds that the heavier a person is, the higher his pulse rate tends to be. A linear regression model he built suggests that 20-kilogram differences in weight correspond to differences in pulse rate of 5 beats per minute. Which of the following must be true and explain your answer. (I) The correlation coefficient between body weight and pulse rate is 1/4. (II) Your pulse rate slows down 3 beats per minute if your weight decreases by 12 kilograms.