VariablesTotalDistanceVariablesMaximalspeedCreate graph of the two-variable data with a regression line, r, r2,Independent of position25 to 29.9years10164 + 914< 20 years10211 + 784< 20 years31.01.920 to 24.9years10381 + 990Independent20 to 24.9years30.8 ± 1.9of position25 to 29.9years30.5 ± 1.930 to 34.9years10156 + 109130 to 34.9years30.2 1.8More than 35years10142 +972More than 35years29.6 ± 1.8

Taking total distance as response and maximal speed as predictor and ignoring the deviations () in…

Answered: Variables Total Distance Variables…

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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please help! rate will be given. write the complete solutions and legibly. APPLIED STATISTICS AND PROBABILITY FOR ENGINEERS > SIMPLE LINEAR REGRESSION An article in the Journal of the Environmental Engineering Division [“Least Squares Estimates of BOD Parameters” (1980, Vol. 106, pp. 1197–1202)] took a sample from the Holston River below Kingport, Tennessee, during August 1977. The biochemical oxygen demand (BOD) test is conducted over a period of time in days. The resulting data follow: See the attached photo Answer the following: a. Assuming that a simple linear regression model is appropriate, fit the regression model relating BOD (y) to the time (x). What is the estimate of σ2?b. What is the estimate of expected BOD level when the time is 15 days?c. What change in mean BOD is expected when the time changes by 3 days?d. Suppose that the time used is 6 days. Calculate the fitted value of y and the corresponding residual.e. Calculate the fitted ŷi for each value of xi used to…
Simple linear regression results: Dependent Variable: Heart. DiseaseIndependent Variable: BikingHeart. Disease = 18.115809 - 0.20845321 BikingSample size: 68R (correlation coefficient) = -0.94616452R-sq = 0.8952273Estimate of error standard deviation: 1.5273175 Correlation between Biking and Heart. Disease is: -0.94616452. Correlation between Smoking and Heart. Disease is: 0.32421. 1) State r 2 (i.e., the coefficient of determination) for “Biking” and “Heart.Disease” and explain what this value means in context of the data set.
Linear regression activity. Tune current between one and six years old were randomly selected from the classified ads of the Arizona republic.The following data were obtained where X donates age in years,and y donates price,in hundreds of dollars. x (age) 6,6,6,2,2,5,4,5,1,4 price(100ths) 270,260,275,405,364,295,335,308,405,305 Draw a scatter diagram. Find the linear correlation coefficientr. describe the shape strength and direction.does a linear relationship exist between the age of Corvette and its price (use the critical value to explain).If appropriate explain the slope in contexts. If appropriate explain the Y intercept in context.Predict the price of a one year old Corvette.Find the residual of a one year old Corvette.
The data below represents commute times (in minutes) and scores on a well-being survey. Complete parts (a) through (d) below. Commute time (in minutes), x 5, 20, 25, 40, 60, 84, 105 Well-being index score, y 69.1, 67.8, 67.1, 66.3, 66.0, 64.3, 62.3 Find the least-squares regression line treating the commute time, x, as the explanatory variable and the index score, y, as the response variable. y=___x + ____ (round to three decimal places as needed).
70. College Costs The table lists the average tuition and fees (in constant 2014 dollars) at public colleges and universi- ties for selected years. Year 1984 1994 2004 2014 Tuition and Fees (in 2014 dollars) 12,179 16,188 7626 9386 Source: National Center for Education Statistics. (a) Find the equation of the least-squares regression line that models the data. (b) Graph the data and the regression line in the same viewing window. (c) Estimate the cost in 2009. (d) Use the model to predict the cost in 2019.
Tutorial 8: Multiple Regression The table below reports the results of the regression analysis on the relationships between salary data for n=14 randomly sampled systems analysts from a large population of such individuals and their years of experience (Exper) and years of postsecondary education (Educ): Variable Coefficients Standard Error I-stat p-value Intercept EXPER 2690 204.6 45511 16.92 0.000 825.7 4.04 0.002 EDUC 1603.7 536.3 2.99 0.012 s= 2156 R-sq = 0.672 R-sq (Adj) = 0.613 ANOVA Source DF SS MS F-stat P 0.002 Regression Residual Total 2 104862168 52431084 11.28 11 51149976 4649998 13 156012144 Prediction Interval 3 Salary 4 5 Predicted value 56467 6. 7 Prediction Interval (95%) 8 Lower limit 51548 9 Upper limit 61386 10 Interval Estimate of Expected Value (95%) Lower limit 11 12 55180 13 Upper limit 57755
Exhibit 14-25 You are given the following information about x and y. Independent Variable Dependent Variable 3 7 3 6 7 O 5.45 08.07 0¹ O-1.32 8 6 7 3 8 نها Refer to Exhibit 14-25. The least squares estimate of bo (intercept)equals_
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The paired data below show the Coursework marks of 6 randomly selected students from a statistics course at UWI and their Final Exam marks in the same course. Coursework Mark, x Final Exam Mark, y > x yresult summary (result). Call: 1m (formula y x) Residuals: 1 A 27 22 B 28 34 A linear regression equation was found using R, and the output is shown below. 2 3 4 -7.5563 3.5822 0.3659 3.3830 -1.7252 1.9509 Student Coefficients: Estimate Std. Error t value Pr (>t|) (Intercept) 6.2964 4.6774 1.346 0.24949 0.1706 5.048 0.00724 ** 0.8615 C с 12 17 D 41 45 Signif. codes: 0 **** 0.001 **0.01 0.05 0.11 Residual standard error: 4.698 on 4 degrees of freedom Multiple R-squared: 0.8643, Adjusted R-squared: 0.8304 F-statistic: 25.49 on 1 and 4 DF, p-value: 0.007239 E 33 33 F 9 16 a) Write out the model for the simple linear regression. Define all terms used in the model. b) What are the values of the intercept and the slope of the regression equation? c) Write out the regression equation. d) A UWI…

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