Bivariate data for the quantitative variables x and y are given in the table below. These data are plotted in the scatter plot shown next to the table.In the scatter plot, sketch an approximation of the least-squares regression line for the data.x y10.1 10.011+1.51.610+5.96.6?7.36.89+7.18.58+2.93.03.53.37+6.66.56+5.24.34.44.78.89.14+9.510.39.18.64.34.72+1.82.215.44.7+7.87.8+1047112.61.8TTTT

The provided information is x y xy x2 10.1 10.0 101 102.01 1.5 1.6 2.4 2.25 5.9 6.6 38.94…

Answered: Bivariate data for the quantitative…

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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Biochemical oxygen demand (BOD) measures organic pollutants in water by measuring the amount of oxygen consumed by microorganisms that break down these compounds. BOD is hard to measure accurately. Total organic carbon (TOC) is easy to measure, so it is common to measure TOC and use regression to predict BOD. A typical regression equation for water entering a municipal treatment plant is BOD = -55.47 + 1.505 TOC Both BOD and TOC are measured in milligrams per liter of water. (a) What does the slope of this line say about the relationship between BOD and TOC? TOC rises (falls) by 1.505 mg/l for every 55.47 mg/l increase (decrease) in BOD BOD rises (falls) by 55.47 mg/l for every 1 mg/l increase (decrease) in TOC TOC rises (falls) by 1.505 mg/l for every 1 mg/l increase (decrease) in BOD BOD rises (falls) by 1.505 mg/l for every 1 mg/l increase (decrease) in TOC
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 442 pounds? Use a significance level of 0.05. Chest size (inches) 58 50 65 59 59 48 Weight (pounds) 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? Critical Values of the Pearson Correlation Coefficient r y=D+x (Round to one decimal place as ne Critical Values of the Pearson Correlation Coefficient r NOTE: To test Ho: p=0 against H,: p+0, reject Ho if the absolute value of r is greater than the critical value in the table. a = 0.05 a = 0.01 4 0.950 0.990 0.878 0.811 0.754 5 0.959 6 0.917 7 0.875 8 0.707 0.834 0.666 0.798 10 0.632 0.765 11 0.602 0.735 12 0.576 0.708 13 0.553 0.684 14 0.532 0.661 15 0.514 0.641 16 0.497 0.623 17 0.482…
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 2 104.004228 8.49446952 MS 522.303829 39 13.3924059 Coef. Std. Err. .6579529 .1474763 .2003584 .1382237 9.804327 12.39987 t P>|t| 4.46 0.000 C 0.156 0.79 0.434 Number of obs = F( 2, 37) = Prob > F R-squared Adj R-squared Root MSE = .3591375 -.0797093 -15.32021 = 40 12.24 0.0001 0.3983 0.3657 2.9145 [95% Conf. Interval] .9567683 .4804261 34.92886 What is the predicted height for a child born to a mother…
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 40 inches. Is the result close to the actual weight of 352 pounds? Use a significance level of 0.05. Chest size (inches) *Weight (pounds) 44 54 328 528 41 55 39 51 418 580 296 503 Click the icon to view the critical values of the Pearson correlation coefficient r. - What is the regression equation? x (Round to one decimal place as needed.)
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 57 inches. Is the result close to the actual weight of 476 pounds? Use a significance level of 0.05. 44 Chest size (inches) Weight (pounds) 58 48 51 58 60 425 266 347 453 282 408 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.) Activate Windows View an example Get more help- Help me solve this O Type here to search hp delete insert prt sc f12 f1o fg 1 f7 f6 f5 f3 米 IOI f1 esc hom backspace 6. L. 4 U E R tab F G J. A caps lock pause 00 9, %24 3. %23
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.
Lean body mass (weight leaving out fat ) helps predict metabolic rate (how many calories of energy are burned in a hour ) The relationship is roughly a straight line the least squares regression line for predicting metabolic rate (y in calories) from lean body mass (x in kilograms) is y =113.2+26.9x The slope of regression line is ?
Find the multiple regression equation with weight as the response variable and the dummy variable of sex and the variable of age as the explanatory variables.
The data show The chest size and weight of several bears find regression equation, letting chest size be the independent variable then find the best predicted weight of a bear with a chest size of 51 inches is the result close to the actual weight of 422 pounds use a significance level of 0.05 Chest size : 45, 50, 43, 43, 52, 52. Weight : 352, 374, 275, 314, 440, 367.
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 532 pounds? Use a significance level of 0.05. Chest size (inches) Weight (pounds) 58 414 50 312 65 59 59 499 48 g 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.) - Critical Values of the Pearson Correlation Coefficient r Critical Values of the Pearson Correlation Coefficient r α = 0.05 α=0.01 NOTE: To test Ho: p=0 4 0.950 0.990 against H₁: p0, reject Ho 5 0.878 0.959 if the absolute value of ris 6 0.811 0.917 7 0.754 0.875 greater than the critical value in the table. 8 0.707 0.834 9 0.666 0.798 10 0.632 0.765 11 0.602 0.735 12 0.576 0.708 13 0.553 0.684 14 0.532 0.661 15 0.514 0.641 16 0.497 0.623 17…
Compute the least-squares regression equation for the given data set. Round the slope and yFintercept to at least four decimal places. 4 y 7. 3. Send data to Excel Regression line equation: y =|

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Bivariate data for the quantitative variables x and y are given in the table below. These data are plotted in the scatter plot shown next to the table. In the scatter plot, sketch an approximation of the least-squares regression line for the data.