Test 1: Multiple Linear Regression. Given the following data:yX1X2X3X₁X5x68.019.694.92.35.229.62.18.319.889.71.85.232.42.18.519.696.22.05.831.02.08.819.495.62.16.432.42.29.018.686.51.85.828.62.09.318.884.52.15.230.62.19.320.488.81.95.632.42.29.519.085.71.96.032.62.19.820.893.62.15.232.22.310.019.986.01.85.831.82.110.318.087.11.66.432.62.010.520.693.12.16.033.42.110.820.283.42.16.231.82.211.020.294.51.96.232.42.111.319.283.41.86.231.41.911.517.085.22.15.633.22.111.819.884.11.86.035.42.012.318.886.91.85.834.02.112.518.683.02.05.634.21.9A. With x₁, x₂, X5 and x as regressors, fit a multiple linear regression model to the given data.B. Suppose that the regressors are grouped as follows:- Group 1 (B₁): x1, X4, X5- Group 2 (B₂): X2, X3, X6

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Answered: Test 1: Multiple Linear Regression.…

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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Which of the following is an assumption of the regression model? Select one: a. X and Y are independent variables. b. The errors of prediction are uniformly distributed. c. The error terms of X are dependent on the error terms of Y. d. There exists a linear association between variables X and Y.
37-38. Given the following data pairs (x, y): (1, 1.24), (2, 5.23). (3, 7.24). (4, 7.60), (5, 9.97), (6, 14.31), (7, 13.99).(8, 14.88), (9, 18.04), (10, 20.70). Find the regression equation O A. y = 0.490 x - 0.053 O B. y = 2.04 x O C. y = 1.98 x + 0.436 O D. y = 0.49 x
Calculate the regression coefficient and obtain the lines of regression for the following data: USE MICROSOFT EXCEL
Draw a graph of the least-squares regression line on your scatterplot. (For hand-drawing, round the slope and y-intercept to one decimal place before drawing the line.) Be sure to show how you were able to plot the line starting with its equation. Model City Miles per Gallon Highway Miles per Gallon Acura RLX 20 29 BMW 530i 24 34 Buick LaCrosse eAssist 25 35 Chevrolet Malibu 29 36 Ford Hybrid FWD 43 41 Honda Civic 32 42 Infiniti Q50 Red Sport 20 26 Kia Forte 30 40 Lexus ES 350 22 33 Mercedes Benz AMG S 21 30 Mini Cooper Clubman 24 32 Nissan Maxima 20 30 Suburu Legacy AWD 25 34 Toyota Prius ECO 58 53
Stoaches are fictional creatures that nest in truffula forests. A researcher wants to know whether there is a relationship between a stoach’s wingspan (?W, the predictor) and its nest height (?H, the response). A sample of 85 stoaches is observed, and for each, the wing-span (in cm) and the nest height (in m) are recorded. The observed data meet the assumptions for a linear regression, so the researcher fits the regression model and obtains a regression equation ℎ̂=−4.273+0.834?,h^=−4.273+0.834w, with standard error for the coefficient of ?w equal to 0.242. Determine the ?p-value from a test for a statistically significant linear dependence of nest height on wing-span. (Give your answer to 4 decimal places.
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city.
Run the Linear Regression Analysis for the following: → Submit Excel FileThe average driving distance (yards) and driving accuracy (percent of drives that land in thefairway) for 8 golfers are recorded in the table below.Rank Driving Distance (yards) Driving Accuracy (%)1 316.3 41.72 304.9 48.83 310.8 42.34 312.5 41.25 294.5 54.76 290.7 54.47 296.9 54.28 295.6 53.9a- Write the equation of a straight-line model relating driving accuracy (y) to drivingdistance (x). Select one of the following:i. Y = β1x + εii. Y = β1x2+ β0iii. Y = β1xiv. Y = β0 + β1x + εb- Fit the model and give the least square prediction equation c- Interpret the estimated y-intercept of the line. Choose the correct answer below:i. Since a drive with 0% accuracy is outside the range of the sample data, the y-intercepthas no practical interpretation.ii. Since a drive with distance 0 yards is outside the range of the sample data, the y-intercept has no practical interpretation.iii. For…
The data shows a systolic and a diastolic blood pressure of certain patients. Find the linear regression eqation, using the first variable (systolic) as the independent variable. Find the best predicted diastolic blood pressure for a patient with a systolic blood pressure reading of 140. Use a significance level of x Systolic Diastolic 116 82 124 88 112 72 144 95 138 96 112 76 145 88 124 76 130 90 129 94 1)Find the correlation coefficient. 2)State the appropriate critical r value to use to test the claim that the correlation between systolic blood pressure and diastolic blood pressure is significantly different from 0 3)The correlation between diastolic blood pressure and systolic blood pressure is: significant, not significant, weak, none 4)The best predicted diastolic blood pressure for a patient with a systolic blood pressure reading of 140 is closest to 1pts
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. (a) x 503 feet (c) x = 318 feet (b) x = 649 feet (d) x = 724 feet 766 494 484 520 44 620 508 Height, x Stories, y 51 46 43 39 38 (c) Predict the value of y for x=318. Choose the correct answer below. O A. 34 B. 50 O C. 47 O D. not meaningful (d) Predict the value of y for x=724. Choose the correct answer below. O A. 34 O B. 41 50
Which of the multivariate regression parameters listed below would be best interpreted as: the proportion of variation in the dependent variable explain by all the independent variables in the model. a b1 X1 R2
Based on data, check the linear regression for multicollinearity, and identify the best regression. Based on the final regression, check for autocorrelation of residuals, and make conclusion.

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