Suppose a doctor measures the height, x, and head circumference, y, of 8 children and obtains the data below. The correlation coefficient is 0.860 and the least squares regressionline is y = 0.196x + 12.045. Complete parts (a) and (b) below.Beight, x27.5 25 26 25.5 27.75 26.5 26.25 26.75 26.75 27 27.25Head Circumference, y 17.3 17.0 17.2 16.9 17.5 17.4 17.1 17.4 17.3 17.4 17.4(a) Compute the coefficient of determination, R².R² =% (Round to one decimal place as needed.)

Given info: The correlation coefficient is given as r = 0.860.

Answered: Suppose a doctor measures the height,…

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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Let x be the weight of a car (in hundreds of pounds), and let y be the miles per gallon (mpg). Suppose a car weighs 3800 pounds. What does the data forecast for the miles per gallon expected? x= 27 44 32 47 23 40 34 52 y= 30 19 24 13 29 17 21 14 State the correlation coefficient and state the regression lin equation.
Suppose a doctor measures the height, x, and head circumference, y, of 8 children and obtains the data below. Thecorrelation coefficient is 0.944 and the least squares regression line is y = 0.199x + 11.982. Complete parts (a) and (b)below.Height, x27.5 25.5 26.25 25.25 27.5 26.25 26 27.25 27.25 27 27.25 ФHead Circumference, # 17.5 17.0 17.2 17.0 17.5 17.3 17.2 17.4 17.3 17.3 17.4(a) Compute the coefficient of determination, R?R?.% (Round to one decimal place as needed.)(b) Interpret the coefficient of determination and comment on the adequacy of the linear model.Approximately % of the variation inis explained by the least-squares regression model.According to the residual plot, the linear model appears to be (Round to one decimal place as needed.)
ANSWER D ONLY
The fish in my pond have mean length 10 inches and mean weight 4 pounds. The correlation coefficient of length and weight is .9. If the length of a particular randomly selected fish is reported to be 10 inches, then what should we predict for the weight of that fish using simple linear regression? 4 10 2.5 NOT ENOUGH INFORMATION GIVEN NONE OF THE OTHERS
We give the total variation, the unexplained variation (SSE), and the least squares point estimate b1 . Total variation = 13.459; SSE = 2.806; b1 = 2.6652 Click here for the Excel Data File Using the information given, find the explained variation, the simple coefficient of determination (r2), and the simple correlation coefficient (r). Interpret r2. (Round your answers to 3 decimal places. Round your percent to 1 decimal place.) Explained variation r2 r % of the variation in demand can be explained by variation in price differential.
Suppose a doctor measures the height, x, and head circumference, y, of 8 children and obtains the data below. The correlation coefficient is 0.818 and the least squares regression line is y = 0.137x + 13.622. Complete parts (a) and (b) below. 27 25 75 26.75 25.25 27.75 26.25 26.25 27. 25 27 25 27.25 27.25 D Height, x Head Circumference, y 17.5 17.1 17.2 17.1 17.4 17.2 17.2 17.4 17.3 17.3 17.3 (a) Compute the coefficient of determination, R? R2 =% (Round to one decimal place as needed.) (b) Interpret the coefficient of determination and comment on the adequacy of the linear model. Approximately % of the variation in V is explained by the least-squares regression model. According to the residual plot, the linear model appears to be (Round to one decimal place as needed.) arch
You are studying how a penguin's bill length (in mm) explains its body mass (in grams) using linear regression. You choose a non-directional alternative to be safe. Given the information below, choose the formula for the least squares regression line. b₁ = 87.42 bo = 362.31 x = 43.92 y = 4202.0 O Bill Length = 87.42 Body mass + 362.31 O Bill Length = 87.42*4202.0 + 362.31 O 4202.0 = 362.31*43.92 +87.42 O Body mass = 87.42 * Bill Length + 362.31 O Body mass = 362.31 *Bill Length + 87.42 O Body mass = 362.31 43.92 + 87.42
11. For temperature (x) and number of ice cream cones sold per hour (y). (65, 8), (70, 10), (75, 11), (80,13), (85, 12), (90, 16). Interpret the coefficient of determination. Optional Answers: 1. 88.2% of the variability in the number of cones sold is explained by the least-squares regression model. 2. 93.9% of the variability in the number of cones sold is explained by the least-squares regression model. 3. 88.2% of the variability in the temperature is explained by the least-squares regression model. 4. 93.9% of the variability in the temperature is explained by the least-squares regression model.
10.2.13 Question Help 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 562 pounds? Use a significance level of 0.05. Chest size (inches) Weight (pounds) 58 50 65 59 59 48 414 312 499 450 456 260 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.)
Prev The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, y = bo + b₁x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, In practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age Bone Density 61 62 68 69 40 357 350 343 340 315 Step 1 of 6: Find the estimated slope. Round your answer to three decimal places. Table Copy Data Next

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