Discussion questions S2

What can surrogate models enable? Check all that apply. They can help linearize strongly nonlinear forward models. They can help accelerate evaluation of the forward model or likelihood in Bayesian inference. They can make it easier to exploit parallel computing resources. They can reduce the dimensionality of the data. They can help push more computation to an "offline" phase of the analysis.

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.

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

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…

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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explains how the two methods of parameter estimation, namely the method of parameter estimation and the method of moments and percentile matching, are used to fit distributions to data for actuarial calculations.

The subject is Engineering Data Analysis p.s. please make clear the solution :(( please answer my question. Thank you so much

Q.49 A person operates a machine that makes capacitors. He has been advised to keep the track of the percentage defectives at his work center. He knows that this type of process, they expect about 4% defectives plus or minus some chance variation. He wishes to initially construct a control chart for defectives. He has prepared ten daily samples of hundred capacitors each. Sample No. 1 2 3456 789 10 % defectives 4 3 36 195 12 4 3 ) Construct the relevent control chart. What are upper and lower control limits?

4) If two variables are highly correlated, does this imply that changes in one cause changes in the other? If not, give at least one example from the real world-that illustrates what else could cause a high correlation.

I am trying to compute the probability of collision with the monte carlo method in MATLAB. But I get an error in teh following code. The error is the matrix P_01 must be semi definete and P_02 must be symmetric. How should I fix this problem?   % Initial Mean and Covariance mu_01 = [153446.180; 41874155.872; 0; 3066.875; -11.374; 0] / 1000; P_01 = [6494.080 -376.139 0 0.0160 -0.494 0; -376.139 22.560 0 -9.883e-4 0.0286 0; 0 0 1.205 0 0 -6.071e-5; 0.0160 -9.883e-4 0 4.437e-8 -1.212e-6 0; -0.494 0.0286 0 -1.212e-6 3.762e-5 0; 0 0 -6.071e-5 0 0 3.390e-9] * 1e-6;   mu_02 = [153446.679; 41874156.372; 5.000; 3066.865; -11.374; -1.358e-6] / 1000; P_02 = [6494.224 -376.156 -4.492e-5 0.0160 -0.494 -5.902e-8; -376.156 22.561 2.550e-6 -9.885e-3 0.0286 3.419e-9; -4.492e-5 2.550e-6 1.205 -1.180e-10 3.419e-9 -6.072e-5; 0.0160 -9.883e-3 -1.180e-10 4.438e-8 -1.212e-6 -1.448e-13; -0.494 0.0286 3.419e-9 -1.212e-6 3.762e-5 4.492e-12; -5.902e-8 3.419e-9 -6.072e-5 -1.448e-13 4.492e-12 3.392e-9] * 1e-6;…

The subject is Engineering Data Analysis

The subject is Engineering Data Analysis p.s please answer my question. Please thank you so much

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.

11. In step of FEM a discrete model is generated from a mathematical model. (a) Preprocessing (b) Discretization(c) Validation(d)interpolationif not sure skip may downvote if wrong

An engineer measures the diameter of 20 tubes selected at random from a large shipment. The sample mean is 48.5 mm and S- 9.4 mm. The manufacturer of the tubes claims that 2 = 42.1 mm. Is this claim supported by data set? for 90% probability? where a' is the true or the estimate value O No O yes

Complete the following in MatLAB: A thermocouple held in room-temperature air is suddenly immersed into a beaker of cold water. Its temperature asa function of time is recorded. Determine the thermocouple's time constant by plotting the data listed in Table attached, assuming that the thermocouple behaves as a first-order system. A more accurate method of determining the time constant is by performing a least-squares linear regression analysis after transforming the temperatures into their appropriate non-dimensional variables. Time(ms) Temperature (°C) 0 24.8 40 22.4 120 19.1 200 15.5 240 13.1 400 9.76 520 8.15 800 6.95 970 6.55 1100 6.15 1400 5.75 1800 5.30 2000 5.20 2200 5.00 3000 4.95 4000 4.95 5000 4.95 6000 4.95 7000 4.95

In Matlab, use the ISS and generate optical measurements with a measurement error of 2 arcsecondsstandard deviation ((2 arcseconds)2 covariance) observing from lat = 40.43113 and long = -86.91499.

What's the correlation coefficient of the following data set? x = [-3 3 4 2 1 1 4 5] and y = [-3 2 8 -2 0 3 7 8] Type your answer...

The rapid progress of engineering design and information technology has caused difficulties in analyzing system reliability. Because of the increased complexity in system reliability structure (component/subsystem interfaces), many unexpected failure modes could occur, and their behaviors are interdependent. At a system’s design and development stage, the main challenge in analyzing a complex system is the failure uncertainty introduced by the incomplete knowledge of the system. This makes it hard to decompose system reliability into subsystem/component reliability in a deterministic manner, such as series or parallel systems. As a result, some common reliability analysis tools such as fault tree (FT) and reliability block diagram (RBD) become inadequate. Do you agree, why or why not? Are there any other approaches to system reliability assessment beside these tools at the early system’s design and development stage (what are these approaches)?

For a positively skewed di stribution, mean is always: (a) Less than the median (b) less than the mode (c) greater than the mode (d) difficult to tell

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The answer is one of the options below please solve carefully and circle the correct option Please write clear .

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