Problem 3 RecitationThe following table gives the approximate population of the US for selected years from 1835 to 19901835 1861 1905 1944 199030yearpopulation (millions) 20100160 280mxAssume that the population growth can be modeled with an exponential function p = be where x is the year andp is the population in millions. By hand, write the equation in linear form and use linear least squares regression todetermine the constants b and m for which the function best fits the data. Use the function to estimate the populationin the year 1960.Problem 3 required submission:Handwritten (or printed) answers.

Step 1: Transform the exponential equation in linear form. We begin by rewriting the given…

Answered: Problem 3 Recitation The following…

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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As an industrial engineer, you intend to use linear trend (or linear regression) method to solve a forecasting problem. You have decided to use the equation of y = m(x) + c to establish the relationship between the sales (y) and the related month (x). It is known that 8 consecutive months data (Jan to Aug) were used and they resulted to the following parameter values of m = 320 and c = 1017. Using the regression technique, estimate the percentage of sales improvement from December this year to June next year.
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
In your biomechanical testing lab, you perform a series of compression tests to determine the relationship between apparent bone density (p, units of g/cm³) and ultimate stress (ơult, units of MPa). Using the set of experimental measurements below, write an m-file to fit a power relationship of the form O uli = Ap to the data. Use the log transform method to linearize the system and data, followed by linear regression. Plot the data points and the power relationship on a single plot. Be sure to label your axes and provide a legend. Provide a printout of your m-file and a printout of the command window showing your results. Write down the best fit equation and box it. 8.76 5.25 4.26 5.51 3.88 18.45 2.09 13.72 5.42 2.17 Oult (MPa) p (g/cm³) 0.598 | 0.459 0.319 | 0.235 0.141 0.754 0.177 0.553 0.394 0.246
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The heat flux (q) can be computed with Fourier's law Where q has units of W/m2 and k is the thermal conductivity of the material (W/m.K). T: temperature (K) and x = distance (m) along the path of heat flow. Determine at each point for the following data using the best finite divided approximation formulas. x (cm) T (K)| 10 800 15 760 25 630 35 480 370 40 300 55 200 70
4. Given the following data : T(k') 600 700 800 900 (Cp/R) 3.671 3.755 3.838 3.917 Where "T" is the absolute temperature and (C,/R) is the dimensionless specific heat of air. Use Newton's forward interpolation method to find the specific heat at T = 670 k°. %3D
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: ☐…
Solve problem 4 in the correlation and regression handout. Submit your spreadsheet file
The orthographic views of an object are shown below. a) Draw its isometric drawing using a pencil. Provide all important dimensions on your isometric drawing. b) You need to take pictures of all main steps and need to provide a step-by-step method to draw isometric drawing accompanied by pictures. 307303030 | 20 60 15 15 55 30 20 60 + 27.5 4 25 47.5 25 R20 20 T 15 60 016 120 R30 120 120 30
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.
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…
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

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