5. Create a MATLAB Script to do a linear, quadratic and exponential regression (using polyfit and Isqcurvefit MATLAB regression functions) and plot the points and the curve. Test it for the following data sets. a. x[123] and y = [148] b. x [-1012] and y = [ 1/3 139] x = [018 12 27] and y = [12345]
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- code by python with screenshot for code and outputDo fast and correct solution.Question 1: In class we went over the derivation for linear regression on the white board. Starting with = the step where you have E (Y – XW)¹ (Y – XW) write the derivation to get to the step W = (XTX)-¹XTY. Explain the math used in each step.
- Two engineers were independently testing a cubic polynomial regression model on the same dataset. The first engineer used the validation set approach, while the second one used 10-fold cross-validation to estimate test MSE. Both of them repeated the test 20 times, each time with a different set.seed() number. Then, each engineer calculated the mean and the standard deviation of his 20 estimated test MSE. Which of the following statement is most likely true? • The standard deviation of MSE from the first engineer will be greater than the standard deviation of MSE from the second engineer. • The mean MSE from the first engineer will be less than the mean MSE from the second engineer. • The mean MSE from the first engineer will be greater than the mean MSE from the second engineer. • The standard deviation of MSE from the first engineer will be less than the standard deviation of MSE from the second engineer.Represent a scatter plot, together with the first principal component and the regression line. IN R programming. provide Code template only!hello, please, I need to create the MARS algorithm Multivariate Adaptive Regression Splines (MARS) using machine learning with more than one column for time series, find the r2 score and split the data to train and test. and R2 for test data using python please
- This is a coding question. Now that you have worked out the gradient descent and the update rules. Try to progrum a Ridge regression. Please complete the coding. Note that here the data sct we use has just one explanatory variable and the Ridge regression we try to create here has just one variable (or feature). Now that you have finished the program. What are the observations and the corresponding predictions using Ridge? Now, make a plot to showoase how well your model predicts against the observations. Use scatter plot for observations, line plot for your model predictions. Observations are in color red. and prodictions are in color green. Add appropriate labeis to the x axis and y axis and a title to the plot You may also need to fine tune hyperparameters such as Icurning rate and the number ofliterations.This is a coding question. Try to progrum a Ridge regression. Please complete the coding. Note that here the data set we use has just one explanatory variable and the Ridge regression we try to create here has just one variable (or feature). Now that you have finished the program. What are the observations and the corresponding predictions using Ridge? Now, make a plot to showcase how well your model predicts against the observations. Use scatter plot tor observations, line plot for your model predictions. Observations are in color red, and predictions are in color green. Add appropriate labels to the x axis and y axis and a title to the plot. You may also need to fine tune hyperparameters such as leurning rate and the number of iterations.2. Write a Python program to create a joinplot and add regression and kernel density fits using "reg" to describe individual distributions on the same plot between Sepal length and Sepal width
- This is a coding question. Now that you have worked out the gradient descent and the update rules. "Try to progrum a Ridge regression. Please complete the coding. Note that here the data set we use has just one explanatory variable and the Ridge regression we try to create here has just one variable (or feature). Now that you have finished the program. What are the observations and the corresponding predictions using Ridge? Now, make a plot to showease how well your model predicts against the observations. Use spatter plot for observations, line plot for your model predictions. Observations are in color red. and predictions are in color green. Add appropriate labels to the x axis and y axis and a title to the plot You may also nood to fine tune hyperparameters such as leurning rate and the number of'aterations.PCA tried to find new basis vectors (axes) that maximize the variance of the instances. Is True or False?Explain what autocorrelation indicates . What are the main problems that autocorrelation creates for OLS estimation results ? Give two ways to detect autocorrelation problem and the hypothesis that are tested ?