Linearize this model and use it to estimate A and B based on the following data.y=(A).(x).(eBx)0.2 0.40.60.91.31.5 1.7 1.8*10.750.75 1.25 1.45 1.25 0.85 0.55 0.35 0.28 0.18Develop a Pseudocode for the calculations. Plot your fit along with thedata.

Given data is as followsx0.2 0.4 0.45 0.5 0.65 0.80.91.0y 0.164…

Answered: Linearize this model and use it to…

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

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Consider the regression equation In Y₁ = 3.89 +0.5X₁ +0.3X₂i a. Predict the value of Y when X₁ = 7.1 and X₂ = 4.9. b. Interpret the meaning of the regression coefficients 100.b₁, and 100.b₂.
Perform Linear Regression Analysis by Octave (or MatLab) software using formulas for calculation of required parameters. Make the graph y versus x values and ytted VS. X on the same plot. x Answers: 0.05 0.04 35.7210 23.3960 7 0.03 13.3970 X S 0.02 6.0310 0.01 1.5190 0 0.0000
The annual expenditure for cell phones varies by the age of an individual. The average annual expenditure E(a) (in $) for individuals of age a (in years) is given below:a: 20, 30, 40, 50, 60, 70E(a): 502, 658, 649, 627, 476, 2131. Use quadratic regression to find the model that best represents the data.2. At what age is the yearly expenditure for cell phones the greatest? Round the answer to the nearest year. YOU MUST SHOW THE WORK FOR THIS PART!
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a snow plower recorded inches of snow fall and the yield by measure of square acreage over the previous 5 seasons. The regression equation is determined to be y=38.4+6.3x where x represents the inches of snowfall and y the measure of yield. 1. use the best fitting line to predict yield for 6 inches of snow 2. one of the records is (6,75) what is teh residual if x is 6
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were:y=ax+b a=-1.063 b=24.198 r2=0.559504 r=-0.748 Use this to predict the number of situps a person who watches 7 hours of TV can do (to one decimal place).
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The number of banks in a country has been dropping steadily since 1984, and the trend in recent years has been roughly linear. The annual data for the years 1999 through 2008 can be summarized as follows, where x represents the years since 1990 and y the number of banks, in thousands. n=10 Ex-235 ²-5605 Ey-77.564 Ey²=603.80424 Exy-1810.155 a. Find an equation for the least squares line. b. Use your result from part a to predict the number of banks in the year 2020. c. If this trend continues linearly, in what year will the number of banks drop below 6200? d. Find and interpret the correlation coefficient.
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Linearize this model and use it to estimate A and B based on the following data. y=(A).(x).(e³x) 0.2 0.4 0.6 0.9 1.3 1.5 1.7 1.8 *10.75 0.75 1.25 1.45 1.25 0.85 0.55 0.35 0.28 0.18 Develop a Pseudocode for the calculations. Plot your fit along with the data.