Implement a simple linear regression model using Python without using any machine learning libraries like scikit-learn. Your model should take a dataset of input features X and corresponding target values y, and it should output the coefficients w and b for the linear equation y =wX + b

We use machine learning to solve problems if they satisfy three conditions. What are these conditions?

In R, write a function that produces plots of statistical power versus sample size for simple linear regression. The function should be of the form LinRegPower(N,B,A,sd,nrep), where N is a vector/list of sample sizes, B is the true slope, A is the true intercept, sd is the true standard deviation of the residuals, and nrep is the number of simulation replicates. The function should conduct simulations and then produce a plot of statistical power versus the sample sizes in N for the hypothesis test of whether the slope is different than zero. B and A can be vectors/lists of equal length. In this case, the plot should have separate lines for each pair of A and B values (A[1] with B[1], A[2] with B[2], etc). The function should produce an informative error message if A and B are not the same length. It should also give an informative error message if N only has a single value. Demonstrate your function with some sample plots. Find some cases where power varies from close to zero to near…

We all know that when the temperature of a metal increases, it begins to expand. So, we experimented with exposing a metal rod to different temperatures and recorded its length as follows: 20 Length Temp 25 30 35 40 45 50 55 60 65 0.5 1.8 5 6.2 6.5 7.8 9.4 9.8 10.9 Required: 1. Implement and plot a simple linear regression for the above data, where the temperature is “x", and the length is "y" 2. Implement and plot a multiple linear regression "Polynomial regression" with different degrees. For example, Degree of 3: Y = w,x' + w2x? + W3x³ + W4 Where w4 represents bias. *you can use normal equation to calculate 'W' as follow: W (X".X)².(X".Y) Then calculate Y, Where Y = X.WT 3. Try degree of 2, 3, 5, and 8

Write a python program to construct a Bayesian network considering medical data. Use this model to demonstrate the diagnosis of heart patients using standard Heart Disease Data Set.

Write a program in Scilab that samples f (x )=3e^−x sinx at x=0,1,2,3,4,5 and then, on the same graph, plots nearest-neighbor, linear and spline interpolations of these data using the interp1 function. The interpolation plots should have 200 samples uniformly spaced over 0≤x≤5 .

Consider a plot of a model of the form Y i = B 0 +B1T i + B2(X 1i-C) + e i. Which of the following is true?   A. B2 is the bump at the cutoff   B. B2 is the slope of the line   C. B1 is the slope of the line   D. B0 is the bump at the cutoff

write a code in matlab to solve this question and take screenshot contains the code and result after running

A simulation experiment consisting of 4 replications returned the 90% confidence interval (54,68) for the monthly return of a financial portfolio (in thousands of dollars). Specify an approximate distribution for the monthly return with appropriate parameters.

d'T Plot- versus T-T, and using your knowledge of curve fitting find the value of dt the constant k. Write and implement necessary code. dT Hint: Model == kTkT is analogous to y-ax+b dt