Google Docsmy leo BD2L P math XL A win time schedule video editorParaphrasing Tool . EeTwoodEnglish1238.9-10TIME REMAINING39:18When a stone is dropped in a pond, ripples are formedand travel in concentric circles away from where thestone was dropped. The relationship between the timeWhat is the equation of the least-squares regressionline?and area of the circles was analyzed and is shown inthe computer output.O Area = 2.004 + 0.490 log(Time)O Time = 0.490 + 2.004 log(Area)%3DRegression Analysis: log (Area) versus log (Time)PredictorCoefSE Coeflog(Area)2.004 + 0.490 log(Time)0.4900.00107288.2350.000Constant0.00317633.1050.000Log (Time)2.004O log(Area)= 0.490 + 2.004 log(Time)R-Sq=0.999R-Sq (adj) =0.999S=0.002SubmitNextSave and ExitMark this and return55%

Y=mx+c Where c=constant m= regression coefficients of x

Answered: nen a stone is dropped in a pond,…

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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An engineer wants to determine how the weight of a gas-powered car, x, affects gas mileage, y. The accompanying data represent the weights of various domestic cars and their miles per gallon in the city for the most recent model year. Complete parts (a) through (d) below. LOADING... Click here to view the weight and gas mileage data. Question content area bottom Part 1 (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable. ModifyingAbove y with caretyequals=enter your response herexplus+(enter your response here) (Round the x coefficient to five decimal places as needed. Round the constant to one decimal place as needed.)
Select all the "vs fit residual plots" that violate the conditions for linear regression. Ob Oc a None of the Above Explain your reasoning for each b C
A researcher investigates the relationship between cigarette smoking (X) and work absences (Y). The number of cigarettes smoked daily and the number of days absent from work due to illness are collected for N= 12 employees. The preliminary results are below: What is the regression equation? How many absences (Y) would expect someone who smokes X = 10 cigarettes a day to have?
stion 4 of 14 > The scatterplot given compares data on the fuel consumption y of a car at various speeds x. Fuel consumption is measured in liters of gasoline per 100 kilometers driven, and speed is measured in kilometers per hour. A statistical software package gives the least-squares regression line ŷ = 11.058 +0.01466x. 10.0 - Use the residual plot to determine if this linear model is 75 appropriate. O No. There is an obvious positive- negative - positive 5.0 - pattern in the residual plot so a linear model is not appropriate for these data. O Yes. There is an obvious positive – negative – positive pattern in the residual plot so-a linear model is appropriate for these data: O No. The residuals do not have equal variability in the residual plot so a linear model is not appropriate for 2.5 - -2.5 -5.0 - 20 40 60 80 100 120 140 160 these data. Speed (km/h) O No. The residuals are not equally positive or negative in the residual plot so a linear model is not appropriate for these data.…
q12
In a regression analysis if SSE = 64 and SSR = 364, then what is the coefficient of determination? (please keep 2 decimal places)
Consider the accompanying data set of dependent and independent variables a. Perform a general stopwise regression using a 0.05 for the p-value to enter and to remove independent variables from the regression model b. Perform a residual analysis for the model developed in part a to verify that the regression conditions are met Click the icon to view the data a. Use technology to perform the general stepwise regression What is the resulting regression equation? Note that the coefficient is 0 for any variable that was removed or not significant -0.69 (050), (050)+(018) - X (Round to two decimal places as needed) • Data Table: y 63 43 51 49 40 42 23 37 30 27 20 31 FR 74 63 78 3534 52 44 47 35 17 15 20 17 Print X₂ 21 259. 15 9 38 18 17 5 40 27 30 33 x₂ 22 aadosa 2NNG 29 20 17 13 17 8 15 10 10 Done 1
Question 10. What is the expected value of the elasticity of education on wages for a person ith 15 years of education at its expected wage? (a) 1.5 (b) 150 160 15.15 (c) 150 (d) 1 (e) 160 150

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