What's the solution Consider the model and data table as given below.Modely=axeẞxDatax0.2 0.6 0.71.1 1.4 1.6 2.3 2.8 2.9y 1.54.17.5 11.8 14.8 21.6 25.5 32.8 38.4Estimate a based on the given data. Use least-squares regressionto fit a nonlinear function (Round the final answers to four decimalplaces.)◄ PreviousNext-

Step 1:To estimate the parameter (a) for the given model 𝑦=𝑎𝑥𝑒𝛽𝑥, we need to fit the data…

Answered: Consider the model and data table as…

Elements Of Electromagnetics

7th Edition

ISBN:9780190698614

Author:Sadiku, Matthew N. O.

Publisher:Sadiku, Matthew N. O.

ChapterMA: Math Assessment

Section: Chapter Questions

Problem 1.1MA

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The following data have the form of exponential function y = a*Exp(x), where Exp(x) denotes the exponential operation of x. Find the nearest regression equation. In the answer, 1.00e0.5x represents 1.00*e^0.5x = 1.00*Exp(0.5x) Y: 0.4, 1, 4, 36 X: 0.2, 2, 4, 8 y = 1.00e0.06x All solutions are not correct O y = 0.35e0.58x O y = 1.35e1.5x
Forest Fires and Acres Burned Numbers (in thousands) of forest fires over the year and the number (in hundred thousands) of acres burned for 6 recent years are shown. The regression line equation is y'=-18.779+0.761x. The standard error of the estimate is sest 9.55. Find the 80% interval when x=60. Round intermediate answers to three decimal places. Round your final answers to two decimal places as needed. Number of fires x 58 47 84 62 57 45 Number of acres burned y 19 26 51 15 30 15 Send data to Excel One can be 80% confident that the interval
Q2: File Upload الزامي ABET äbäi 15 solve the question on a piece of [Q2] paper and upload it as ONE FILE here .{as an image or PDF only} Question 2 Use the least square regression to fit the data in the following table to the equation yfit = ae*. 1.5 2 y 1.6 3.7 7 13.5 24.6 1 2.5 (A) Determine the values of a and 3. (B) What is the standard error of this estimation? (C) Using the fit equation, what the value of y at r = 2.25? إضافة ملف
For the Thermistor given below using piecewise approximation method combined with line regression to find the best equation and value for Temperature if the system has counts =800 20 40 60 80 |ADC counts 928 785 654 420 152 T=129.7902-0.14586*Counts, T= 13.1022 T=129.7902-0.13986*Counts, T=17.9021 T=129.7902-0.12358*Counts, T= 30.9262 T=135.4745-0.14599*Counts, T= 18.68613
Problem 1/MatlabGrader (20 points) (Core Course Outcome 4) Develop a Matlab function myFitExam that finds the best fit of the function p(t) = as sin³ (t) + b sin² (t) + csin(t) + d (1) to a given set of data points (ti, pi) using regression. Here t is in radians. The function input shall be ⚫t: column vector of data values t ⚫ p: column vector of data values p The function output shall be • a: scalar containing the best fit coefficient a b: scalar containing the best fit coefficient b c: scalar containing the best fit coefficient c d: scalar containing the best fit coefficient d In the function use only functions developed in this class in modules 1 - 4. You do not need to provide these functions in your submission. They will be provided when assessing your function after the deadline. Note: no assessments will be performed on your submitted function before the exam deadline. Scores on Canvas for this problem before the exam has been fully graded are meaningless. Required submission: ☐…
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Problem 1/MatlabGrader (20 points) (Core Course Outcome 4) Develop a Matlab function myFit Exam that finds the best fit of the function p(t) = a sin³ (t) +bsin² (t) + csin(t) +d (1) to a given set of data points (t., p.) using regression. Here t is in radians. The function input shall be t: column vector of data values t p: column vector of data values p The function output shall be a: scalar containing the best fit coefficient a b: scalar containing the best fit coefficient b c: scalar containing the best fit coefficient c d: scalar containing the best fit coefficient d In the function use only functions developed in this class in modules 1 - 4. You do not need to provide these functions in your submission. They will be provided when assessing your function after the deadline. Note: no assessments will be performed on your submitted function before the exam deadline. Scores on Canvas for this problem before the exam has been fully graded are meaningless. Required submission: well…
Develop a Matlab function myFitExam that finds the best fit of the function p(t) = a sin³ (t) +b sin² (t) + csin(t) + d (1) to a given set of data points (ti, pi) using regression. Here t is in radians. The function input shall be t: column vector of data values t ⚫ p: column vector of data values p The function output shall be • a: scalar containing the best fit coefficient a b: scalar containing the best fit coefficient b ⚫ c: scalar containing the best fit coefficient c d: scalar containing the best fit coefficient d In the function use only functions developed in this class in modules 1 - 4. You do not need to provide these functions in your submission. They will be provided when assessing your function after the deadline. Note: no assessments will be performed on your submitted function before the exam deadline. Scores on Canvas for this problem before the exam has been fully graded are meaningless.
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Linearize the model ? = ? ? ?^(?? )and solve for the coefficients by hand using the following data x 0.1 0.2 0.4 0.6 0.9 1.3 1.5 1.7 1.8 y 0.75 1.25 1.45 1.25 0.85 0.55 0.35 0.28 0.18
Hello Sir.Good night.Permission, i have a question in my homework related numerical methods lesson. The following bellow is question. Please advice. Thank you so much Regards,Irfan Mention the types of problems that exist in the field of engineering, especially mechanical engineering that can be solved by the linear regression method

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