2. Fit the following data in a linear regression line. Find the standard error of estimate.1, s0.20.40.60.81.2i, 10-3 Al0.2 0.3683 0.3819 0.2282 0.0486 0.0082 0.14413. Using the following data by linear interpolation, find the temperature at y = 2 cm.3У. стT, K900480270200

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Answered: 2. Fit the following data in a linear…

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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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
a. What is a residual? b. In what sense is the regression line the straight line that "best" fits the points in a scatterplot? a. What is a residual? OA. Aresidual is a point that has a strong effect on the regression equation. OB. A residual is the amount that one variable changes when the other variable changes by exactly one unit. OC. Aresidual is a value that is determined exactly, without any error. OD. Aresidual is a value of y - y, which is the difference betvween an observed value of y and a predicted value of y. b. In what sense is the regression line the straight line that "best" fits the points in a scatterplot? The regression line has the property that the of the residuals is the possible sum.
A regression line calculated as y= 9.7-3.2x. The slope of this line is -3.2 TRUE OR FALSE
a. Estimate the regression line and also write the prediction equation. y = 83.4578-5.8795 x y = 5.8795 + 83.4578 x ŷ ŷ= = -5.8795 + 83.4578 x = 83.4578 + 5.8795 x
Run a regression analysis on the following data set, where y is the final grade in a math class and x is the average number of hours the student spent working on math each week. hours/week Grade y 8. 72.2 8. 58.2 10 60 11 71.4 14 92.6 14 76.6 14 75.6 14 83.6 15 81 20 100 State the regression equation with constants accurate to 2 decimal places. ý = What is the predicted value for the final grade when a student spends an average of 15 hours each week on math? Round to 2 decimal places. Question Help: D Video 1 Video 2 Submit Question
The table below shows the average weekly wages (in dollars) for state government employees and federal government employees for 10 years. Construct and interpret a 99% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $925. The equation of the regression line is
in simple linear regression, given the following table : n = 25 E(y,-7) = 1407 %3D SSR = 472 Calculate the standard error of the estimate (s): Select one: Oa. 7.82 Ob. 6.38 Oc. 58.62 d. 34.63 Next
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 80 mm Hg. Use a significance level of 0.05. Right Arm 100 99 93 77 77 Q Left Arm 174 168 148 148 146 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is ŷ=+x. (Round to one decimal place as needed.) mm Hg. Given that the systolic blood pressure in the right arm is 80 mm Hg, the best predicted systolic blood pressure in the left arm is (Round to one decimal place as needed.) Data table Critical Values of the Pearson Correlation Coefficient r α = 0.05 α = 0.01 0.950 0.990 0.959 0.878 0.811 0.917 0.754 0.875 0.707 0.834 0.666 0.798 0.632 0.765 0.602 0.735 0.576 0.708 0.553 0.684 0.532 0.661 0.514 0.641 0.497 0.623 0.482…
Which of the following data sets or plots could have a regression line with a negative slope? Select all that apply. Select all that apply: the difference in the number of ships launched by competing ship builders as a function of the number of months since the start of last year the number of hawks sighted per day as a function of the number of days since the two-week study started the total number of ships launched by a ship builder as a function of the number of months since the start of last year the average number of hawks sighted per day in a series of studies as a function of the number of days since the ten-week study started
Shown below is a portion of a computer output for a linear regression analysis relating an individual's income (y in thousands of dollars) to age (x1 in years), level of education (x2 ranging from 1 to 5), and the individual's gender (x3 where 0 = female and 1 = male). Coefficient Standard Error t-statistic p-value Intercept 15.934 1.389 11.47 0.000 x1 0.625 0.094 6.65 0.000 x2 0.921 0.190 4.85 0.000 x3 –0.510 0.920 –0.55 0.590 Source of Variation Sum of squares Degrees of freedom Mean square F-statistic p-value Regression 84 3 28 4 0.027 Error 112 16 7 Total 196 19 a. Is there a significant relationship between an individual's income and the set of variables, age, level of education, and gender (based on a significance level α =05 )? Explain why using one of the p-values in the output tables. b.Which of the three predictor variables are…
An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.
The following results were obtained from a simple linear regression analysis. Total sum of square = 5.7640. Unexplained sum of square = 0,2225. The coefficient of determination is: OA 0.0386 O B, 0.0402 OC0.9614 O D.0.9805

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2. Fit the following data in a linear regression line. Find the standard error of estimate. 1, s 0.2 0.4 0.6 0.8 1.2 i, 10-3 Al0.2 0.3683 0.3819 0.2282 0.0486 0.0082 0.1441