Use regression, by hand, to approximate the following data set x = [ 01 8 12 27] and y = [12345] and plot the results using., a. Linear fit b. Quadratic fit c. Exponential fit
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![Use regression, by hand, to approximate the following data set x = [ 01 8 12 27] and
y = [12345] and plot the results using.,
a. Linear fit
b. Quadratic fit
c. Exponential fit](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fc3d530e0-7619-4a07-8476-fb896b70039d%2F4183aa42-3e0c-4e98-b36c-bd8052ca02ac%2F9zs3bm_processed.png&w=3840&q=75)
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- In python, for a sample data with 4 columns and 60 rows how do you find the parameters for the regression with the feature map (see attached) where we consider the loss function to be the square of residuals. Once this is done, how do you compute the empirical risk? I've attached some of the data below, it would be sufficient to see how you get results for the question using the above dataset. 1 14 25 620 -1 69 29 625 0 83 27 850 0 28 25 1315 1 41 25 2120 -1 153 31 1315 0 55 25 2600 0 55 31 490 1 69 25 3110 1 83 25 3535Given the following values (200, 400, 800, 1000, 2000): Calculate their mean, variance, and standard deviation. Normalize them using min-max normalization with and . Normalize them using z-score.Write code to define a K-NN regression model with K=5 and the neighbors are weighted by the inverse of their distance. [ ] # Write your code here
- MatLab Create a function that calculates the coefficients of the least-squares regression for a 2nd order polynomial. Use LU Decomposition to find the coefficients. The function should have: Input: X and Y Output: a_0, a_1, a_2, r^2, r. Please test your function on the data below and plot the results. Be sure to save your function and include it in your homework submission!MatLab Load the data flu.mat (you can do this by typing load flu in your script). This data is the flu trends seen in the United States 2005-2006, divided by region. We will use regressions to look at the data during flu season in the Pacific region. Create your x data: have x equal to 1:30. These represent 30 weeks between Oct. 2005 and May 2006. Create your y data: have y equal to flu.Pac(1:30)’. This is the flu trend for each week. Make sure you have an apostrophe after the last parenthesis. Fit the data below with a straight line and with a 2nd order polynomial. Use least-squares regression. Calculate the coefficient of determination (r^2) and the correlation coefficient (r) for each regression. Plot the two regression curves against the data. Which regression is better? Is there a polynomial you think would work better? Describe the data – what does it mean to you?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.
- The following data were collected during a study of consumer buying patterns: Observation Observation y. 69 y. 18 19 81 2 26 81 9 14 75 41 85 10 10 76 4 35 77 11 25 83 56 95 12 21 86 46 93 13 29 90 33 86 Click here for the Excel Data File b. Obtain a linear regression line for the data (Round your intermediote calculations and final answers to 3 decimal places.) y3= c. What percentage of the variation is explained by the regression line? (Do not round intermediate calculations. Round your answer to the nearest whole percent.) Approximately of the varlation in the dependent varlable is explainud by the independent variable d. Use the equation determined in part b to predict the expected value of y for x 44. (Round your intermediate calculations and final answers to 3 decimal places.)Python Regression Model 1: train MSE = 0.423, test MSE = 0.978 Model 2: train MSE = 0.572, test MSE = 0.644 Model 3: train MSE = 0.218, test MSE = 1.103 Based on this information, which of these models generalises the best to unseen data?What Is The sample linear correlation?
- Linear Regression cannot not be applied on every dataset, it is prudent to apply linear regression if the correlation is greater than 0.5 or less than -0.5. True FalseFind the dissimilarity matrix of the following dataset: Student Test-1 (nominal) Test-2 (nominal) Test-3 (ordinal) Test-4 (numeric) 1 Code-A Code - I Excellent 80 Code-B Code - I Fail 20 3 Code-C Code - II Fail 100 4 Code-A Code - II Pass 60Write a python programming code for logistic regression and calculate these classification metrics: contusion matrix, accuracy, precision, recall, sensitivity, specificity, F1 score and matthew correlation coefficient.
