Computer a 95 % confidence interval for the mean prediction when the true roughness is 0.8 um An article presents the results of an experiment in which the surface roughness (in μm) was measured for 15 D2 steelspecimens and compared with the roughness predicted by a neural network model. The results are presented in thefollowing table.TRUE Value (x)Predicted Value (y)Check my work0.450.4000.820.70.540.520.410.390.770.740.790.780.250.270.620.60.910.870.520.511.020.910.60.710.580.50.870.911.061.04To check the accuracy of the prediction method, the linear model y= Bo+B1x+ & is fit. If the prediction method isaccurate, the value of ẞo will be 0 and the value of ẞ1 will be 1.Note: This problem has a reduced data set for ease of performing the calculations required. This differs from the dataset given for this problem in the text.

The predicted value (y) is given.Want to calculate a 95% confidence interval for the mean…

An article presents the results of an experiment in which the surface roughness (in μm) was measured for 15 D2 steel specimens and compared with the roughness predicted by a neural network model. The results are presented in the following table. TRUE Value (x) Predicted Value (y) 0.45 0.400 0.82 0.7 0.54 0.52 0.41 $0.39 0.77 0.74 0.79 0.78 0.25 0.27 0.62 0.6 0.91 0.87 0.52 0.51 1.02 0.91 0.6 0.71 0.58 0.5 0.87 0.91 1.06 1.04

An article presents the results of an experiment in which the surface roughness (in μm) was measured for 15 D2 steel specimens and compared with the roughness predicted by a neural network model. The results are presented in the following table. TRUE Value (x) Predicted Value (y) 0.45 0.400 0.82 0.7 0.54 0.52 0.41 $0.39 0.77 0.74 0.79 0.78 0.25 0.27 0.62 0.6 0.91 0.87 0.52 0.51 1.02 0.91 0.6 0.71 0.58 0.5 0.87 0.91 1.06 1.04

Linear Algebra: A Modern Introduction

4th Edition

ISBN:9781285463247

Author:David Poole

Publisher:David Poole

Chapter4: Eigenvalues And Eigenvectors

Section4.6: Applications And The Perron-frobenius Theorem

Problem 25EQ

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