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 leastsquares 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 DHeight, xHead Circumference, y 17.5 17.1 17.2 17.1 17.4 17.217.2 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 inV is explained by the least-squares regression model. According to the residual plot, the linear modelappears to be(Round to one decimal place as needed.)arch

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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Several bakeries in a town were asked the price for different amounts of donuts at their shop. The scatter plot with Total Cost (in dollars) 876543 ~ TO 0 0 1 Donut Pricing 2 3 4 5 Number of Donuts 6 7 8 line of best fit was created from the data gathered. Part A: Estimate the correlation coefficient. Explain your reasoning for the given value. Part B: How many positive and negative residuals are there? Explain your reasoning. Part C: State the point with the largest absolute value residual and interpret the point in terms of the context of the data.
The following data represent the speed at which a ball was hit (in miles per hour) and the distance it traveled (in feet) for a random sample of home runs in a Major League baseball game in 2018. Complete parts (a) through (f). Click here to view the data. Click here to view the critical values of the corelation coefficient (a) Find the least-squares regression line treating speed at which the ball was hit as the explanatory variable and distance the ball traveled as the response variable. y (Round to three decimal places as needed.) (b) Interpret the slope and y-intercept, if appropriate. Begin by interpreting the slope. Data table O A. The slope of this least-squares regression line says that the distance the ball travels increases by the slope with every 1 mile per hour increase in the speed that the ball was hit. O B. The slope of this least-squares regression line shows the increase in the speed that the ball was hit with every 1 foot increase in the distance that the ball was…
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.)
Suppose a car's city miles per gallon rating can be determined by the car's weight. The means and standard deviations of these variables and the correlation coefficient between them are reported in the following table: What proportion of the variability in cars' miles per gallon ratings is explained by the least squares line with weight?
Use Excel’s Data Analysis tool to conduct a multiple regression, using Risk of Stroke as the dependent variable, and all the rest of variables as independent variables. Choose confidence level at 99%. Risk of Stroke (%) Age Pressure Smoker (Yes=1) 12 57 152 0 24 67 163 0 13 58 155 0 56 86 177 1 28 59 196 0 51 76 189 0 18 56 155 1 31 78 120 0 37 80 135 1 15 78 98 0 22 71 152 0 36 70 173 1 15 67 135 1 48 77 209 0 15 60 199 0 36 82 119 1 8 66 166 0 34 80 125 1 3 62 117 0 37 59 207 1 Interpret the multiple coefficient of determination R-square. Compare the R-square of the simple linear regression and the R-square of the multiple regression, what does the differences in R-squares tell us? What does F-test for significance tell us?
The following data give the family size (2) and number of pets per family (y) for a random sample of 10 families. 4. 2 6. 3. 0. 4. 9. 10 Create a scatterplot for the data. y 10 6. 7- 4 1 2 3 4 5 6 7 8 9 10 11 Clear All Draw: Determine the least squares regression line for the data. Round values to four decimal places, if necessary. Determine the correlation coefficent. Round the solution to four decimal places, if necessary. Use the model to predict the number of pets owned by a family with 13 members. Round the solution to the nearest whole number. 5. 5. 2. 3. 3.
Suppose a doctor measures the height, x, and head circumference, y, of 8 children and obtains the data below. The correlation coefficient is 0.858 and the least squares regression line is y = 0.228x +11.187. Complete parts (a) and (b) below. Height, x 27.5 25.75 26.5 25.5 27.25 26.25 25.75 27.25 27 27.25 27 Head Circumference, y 17.4 17.2 17.2 16.9 17.6 17.1 17.1 17.4 17.4 17.3 17.3 (a) Compute the coefficient of determination, R². 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 (Round to one decimal place as needed.) is explained by the least-squares regression model. According to the residual plot, the linear model appears to be
The cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen cadets, each bought new two years ago, and each sold used within the past month . For each cadet in the sample, we have listed both the mileage x (in thousands of miles) that the cadet had on its odometer at the time it was sold used and the price y (in thousand dollars) at which the cadet was sold used. The least-squares regression line for these data has equation of y=41.79-0.50x. This line is shown in the scatter plot below. A) for these data, used selling prices that are greater than the mean of the used selling prices tend fo be paired with mileages that are ___ the mean of the mileages ? B) according to the regression equation, for an increase of one thousand miles in cadet mileage, there is a corresponding decrease of how many thousand dollars in the used selling price?
The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. The least-squares regression line for these data has equation Ŷ=42.80-0.53x. This line is shown in the scatter plot below. (The 2nd picture contains the rest of the data as it would not fit in the first pic and it includes the question as well.)
The average number of students in the college of business is 56, with a variance of 5. The calculated median was greater than the mean by 0.4. Calculate Pearson's coefficient of skewness.
You are conducting a study to see if students do better when they study all at once or in intervals. One group of 12 participants took a test after studying for one hour continuously. The other group of 12 participants took a test after studying for three twenty minute sessions. The first group had a mean score of 75 and a variance of 120. The second group had a mean score of 86 and a variance of 100. What is the calculated t value? Are the mean test scores of these two groups significantly different at the .05 level? What would the t value be if there were only 6 participants in each group? Would the scores be significant at the .05 level?
Choose the alternative that best answer the questions: I. Perfect multicollinearity occurs when the correlation coefficient between two independent variable is zero or closer to zero. II. Perfect multicollinearity will produce unbiased estimates II. Perfect multicollinearity will produce estimates with higher variance IV. Perfect multicollinearity should be ignored in the regression model and all variables should be kept in the model. O Only I is TRUE ALL are TRUE Only IV is TRUE O Only Il and III are TRUE

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