Calculate an estimate of the error standard deviation in the simple regression model /subimmtidep=27421ep3D4bodtags=Dautosave#question4737404_8For the past decade, rubber powder has been used in asphalt cement to improve performance. An article includes a regression of y = axial strength (MPa) on x = cube strength (MPa)based on the following sample data:112.3 97.0 92.786.0 102.099.2 95.8 103.5 89.0 86.775.2 70.7 57.948.573.973.3 68.159.358.0 48.7E USE SALT(a) Obtain the equation of the least squares line. (Round all numerical values to four decimal places.)y = 0.9847x - 31.5869y 3DInterpret the slope.OA one MPa increase in cube strength is associated with an increase in the predicted axial strength equal to the slope.A one MPa decrease in axial strength is associated with an increase in the predicted cube strength equal to the slope.O A one MPa decrease in cube strength is associated with an increase in the predicted axial strength equal to the slope.O A one MPa increase in axial strength is associated with an increase in the predicted cube strength equal to the slope.(b) Calculate the coefficient of determination. (Round your answer to four decimal places.)0.6351OThe coefficient of determination is the proportion of the observed variation in axial strength of asphalt samples of this type that cannot be attributed to its linearrelationship with cube strength.Interpret the coefficient of determination.The coefficient of determination is the number of the observed samples of axial strength of asphalt that can be explained by variation in cube strength.12:37 PMO The coefficient of determination is the number of the observed samples of axial strength of asphalt that cannot be explained by variation in cube strength.D ENG19°C A10/12/2021a160 耳Type here to searchhp

Given data, x y 112.3 75.2 97 70.7 92.7 57.9 86 48.5 102 73.9 99.2 73.3 95.8 68.1…

Answered: Calculate an estimate of the error…

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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The ols() method in statsmodels module is used to fit a multiple regression model using “Quality” as the response variable and “Speed” and “Angle” as the predictor variables. The output is shown below. A text version is available. What is the correct regression equation based on this output? What is the coefficient of determination? Select one.
Based on the scatter plot above, describe the relationship between the amount of cost damage and the distance from nearest fire station to the residential. Determine the regression line for the above regression output. Interpret the slope coefficient of distance to the nearest station affecting the amount of fire cost damage. Compute the correlation coefficient and interpret its meaning. Does the distance to the nearest fire station has significant influence on the cost damage amount? Conduct a test at 1% significance level. ] Predict the fire cost damage generated if the distance from nearest fire station is 2.5 kilometers. Is this estimate reliable? Explain. []
Spray drift is a constant concern for pesticide applicators and agricultural producers. The inverse relationship between droplet size and drift potential is well known. The paper "Effects of 2,4-D Formulation and Quinclorac on Spray Droplet Size and Deposition"† investigated the effects of herbicide formulation on spray atomization. A figure in a paper suggested the normal distribution with mean 1050 µm and standard deviation 150 µm was a reasonable model for droplet size for water (the "control treatment") sprayed through a 760 ml/min nozzle. (a) What is the probability that the size of a single droplet is less than 1455 µm? At least 900 µm? (Round your answers to four decimal places.) less than 1455 µm at least 900 µm (b) What is the probability that the size of a single droplet is between 900 and 1455 µm? (Round your answer to four decimal places.)(c) How would you characterize the smallest 2% of all droplets? (Round your answer to two decimal places.) The…
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A car was driven 24 different times with different octane levels. Using the output from the regression, give a 82% confidence interval for the effect of octane on the car. Use 3 decimal places.Simple linear regression results:Dependent Variable: mileageIndependent Variable: octanemileage = -50.657546 + 0.9122631 octaneSample size: 24R (correlation coefficient) = 0.7165R-sq = 0.5134Estimate of error standard deviation: 1.7101924Parameter estimates: Parameter Estimate Std. Err. DF T-Stat P-Value Intercept -50.657546 -9.9406 22 5.096 <0.0001 Slope 0.9122631 0.189345 22 4.818 <0.0001
Using the California Department of Education's API 2000 dataset with sample size be 45. This data file contains a measure of school academic performance as well as other attributes of the elementary schools, such as, class size, enrollment, poverty, etc. Let's dive right in and perform a regression analysis using the variables api00, acs_k3, meals and full. Fit the regression model and get the following SAS output. The following are the outputs from SAS. Source Model Error Corrected Total Variable Intercept acs_k3 meals full Label DF 3 a b Intercept avg class size k-3 pct free meals. pct full credential Sum of Squares DF Analysis of Variance 1 1 1 1 с 1271 e Mean Square 875 d Parameter Estimates Parameter Estimate 875.05923 -2.50651 -3.31802 0.12031 Answer the question using the SAS output. F Value f Standard Error 31.03465 1.59469 0.23408 0.12072 Pr > F g t Value 32.08 -1.92 -24.04 1.20 Pr > |t| <.0001 0.0553 <.0001 0.2321
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