A biologist collected data on a sample of 20 porcupines. She wants to be able to predict the body mass of a porcupine (M, in grams)based on the length of the porcupine (L, in cm). Her least squares regression equation is M = – 3089 + 175.6L.Predict the body mass of a porcupine that is 51 cm long. Report your answer using one decimal place.O 5661.0 gO 5732.5 gO 5866.6 gO 6139.2 geTextbook and MediaSave for LaterLast saved 3 minutes ago.Attempts: 2 of 3 usedSubmit AnswerSaved work will be auto-submitted on the due date. Auto-submission can take up to 10 minutes.

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Answered: A biologist collected data on a sample…

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 file Galton on D2L contains the 928 observations Francis Galton used in 1885 to estimate the relationship between the heights of parents and the heights of their children. The column Children refers to the height (in inches) of a child, and the column Mid-Parents refers to the average height (in inches) of the mother and father of that child. You can download this file into Excel and Minitab. a. Calculate the regression Height of Children = a +b (Height of Mid-Parents). b. Calculate the average for Height of Children, and calculate the average Height of Mid-Parents. c. Create a new variable in Minitab which is the Height of Children measured in terms of deviations from its mean. Call this new variable y. Also, create a new variable in Minitab with is the Height of Mid-Parents measured in terms of deviations from its mean. Call this new variable x. Calculate the regression y = a + bx. You can create the new y and x variables in Excel of Minitab, whichever you find more convenient.…
The volume (in cubic feet) of a black cherry tree can be modeled by the equation y=−50.8+0.3x1+5.1x2, where x1 is the tree's height (in feet) and x2 is the tree's diameter (in inches). Use the multiple regression equation to predict the y-values for the values of the independent variables.
Use the least squares regression model to estimate the maximum sustained wind speed in a hurricane when the pressure reading is 950 mb. Interpret the slope of the least squares regression model in the context of wind speed and atmospheric pressure. Determine the coefficient of determination and interpret its meaning in the context of wind speed and atmospheric pressure.
The volume (in cubic feet) of a black cherry tree can be modeled by the equation y = -51.5+0.3x, +5.3x2, where x, is the tree's height (in feet) and x, is the tree's diameter (in inches). Use the multiple regression equation to predict the y-values for the values of the independent variables. x₁ = 72. x₂ = 8.9 The predicted volume is cubic feet. (Round to one decimal place as needed.)
Please see below.
You performed a regression analysis of a set of bibariate data (two variables) and found that the coefficient od determination was .99 for a quadratic model and .88 for cubic model Explain which model you would use and why
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 63 inches. Is the result close to the actual weight of 522 pounds? Use a significance level of 0.05. Chest size (inches) 58 50 65 59 59 48 D 414 312 499 450 456 260 Weight (pounds) Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? y=+x (Round to one decimal place as needed.) What is the best predicted weight of a bear with a chest size of 63 inches? The best predicted weight for a bear with a chest size of 63 inches is pounds. (Round to one decimal place as needed.) Is the result close to the actual weight of 522 pounds? O A. This result is not very close to the actual weight of the bear. O B. This result is exactly the same as the actual weight of the bear. O C. This result is close to the actual weight of the…
Obtain the descriptive statistics for each type of fertilizer. Interpret the result
solve a,b and c given in the image provided.
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 =
The least squares regression equation is y=18.7841+0.5616x. If the systolic pressures of two patients differs by 8 mmHg, by how much would you predict their diastolic pressures to differ?

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