Compute the least-squares regression equation for the given data set. Use a TI-84 calculator. Round the slope and y-Intercept to at least four decimal places. 4 6. 12 -9 -3 5 -11 y 3. 31 36 -2 -14 Send data to Excel Regression line equation: y =

Compute the least-squares regression equation for the given data set. Use a TI-84 calculator. Round the slope and y-Intercept to at least four decimal places. 4 6. 12 -9 -3 5 -11 y 3. 31 36 -2 -14 Send data to Excel Regression line equation: y =

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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Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 85 mm Hg. Use a signicance level of 0.05. Right Arm 102 101 93 76 77 Left Arm 177 170 147 148 148
Q5/ Use Linear Regression to fit the following data: X 1 2 4 5 6 Y 4 10 10 9 3
A biologist wants to predict the height of male giraffes, y, in feet, given their age, x1, in years, weight, x2, in pounds, and neck length, x3, in feet. She obtains the multiple regression equation yˆ=7.36+0.00895x1+0.000426x2+0.913x3. Predict the height of a 12-year-old giraffe that weighs 3,100 pounds and has a 7-foot-long neck, rounding to the nearest foot.
Compute the least-squares regression equation for the given data set. Use a TI-84 calculator. Round the slope and y-intercept to at least four decimal places. 5.8 4.1 6.5 4.6 6.6 5.8 5.2 2.0 4.9 1.0 4.1 1.2 1.9 2.9 x y Send data to Excel Regression line equation: y
The value of a sports franchise is directly related to the amount of revenue that a franchise can generate. The accompanying data table gives the value and the annual revenue for 15 major sport teams. Suppose you want to develop a simple linear regression model to predict franchise value based on annual revenue generated. b. Assuming a linear relationship, use the least-squares method to determine the regression coefficients b0 and b1. (Round the value of b0 to the nearest integer as needed. Round the value of b1 to two decimal places as needed.)
Compute the least-squares regression equation for the given data set. Use a TI-84 calculator. Round the slope and y-intercept to at least four decimal places. 43 37 15 19 24 37 18 y 72 67 25 29 42 65 32 Send data to Excel Regression line equation: y = 1.7777x + 1.5838
The following table gives the data for the grades on the midterm exam and the grades on the final exam. Determine the equation of the regression line, y = bo + b₁x. Round the slope and y-intercept to the nearest thousandth. Grades on Midterm and Final Exams Grades on Midterm 66 68 71 86 78 85 62 82 79 61 Grades on Final 90 63 82 96 72 83 79 91 84 65
Find the regression equation, letting the first variable be the predictor (x) variable. Using the listed lemon/crash data, where lemon imports are in metric tons and the fatality rates are per 100,000 people, find the best predicted crash fatality rate for a year in which there are 500 metric tons of lemon imports. Is the prediction worthwhile? Find the equation of the regression line. (Round the constant three decimal places as needed. Round the coefficient to six decimal places as needed.) The best predicted crash fatality rate for a year in which there are 500 metric tons of lemon imports is _______ fatalities per 100,000 population. (Round to one decimal place as needed.) Is the prediction worthwhile? A. Since the sample size is small, the prediction is not appropriate. B. Since common sense suggests there should not be much of a relationship between the two variables, the prediction does not make much sense. C. Since all of the requirements…
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.
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The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable. Find the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value? Chirps in 1 min 924 1150 840 1166 1087 930 Temperature (°F) 77.5 84.6 74.1 91 79.6 79.7 What is the regression equation? What is the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute? What is wrong with this predicted value? Choose the correct answer below. A. It is unrealistically high. The value 3000 is far outside of the range of observed values. B. The first variable should have been the dependent variable. C. It is only an approximation. An unrounded value would be considered accurate. D. Nothing is wrong with this value. It can be…
Researchers are interested in predicting the height of a child based on the heights of their mother and father. Data were collected, which included height of the child (height ), height of the mother ( mothersheight ), and height of the father ( fathersheight ). The initial analysis used the heights of the parents to predict the height of the child (all units are inches). The results of the analysis, a multiple regression, are presented below. . regress height mothersheight fathersheight Source Model Residual Total height mothersheight fathersheight _cons SS df 208.008457 314.295372 37 522.303829 2 104.004228 8.49446952 MS 39 13.3924059 Coef. Std. Err. .6579529 .1474763 .2003584 .1382237 9.804327 12.39987 Calculate the test statistic that is labeled "C" in the output. t P>|t| 4.46 0.000 C 0.156 0.79 0.434 Number of obs = F( 2, Prob > F 37) = R-squared Adj R-squared = Root MSE 40 12.24 0.0001 = 0.3983 0.3657 2.9145 .3591375 -.0797093 -15.32021 = [95% Conf. Intervall .9567683 .4804261…