Take screenshots of functions running in cLISP,
Let x1, x2, ...xN be a set of input features. A linear function that represent those features, will have a following form:
Fw(x1, x2, .. xn)= w0 + w1 * x1 + w2 * x2 +... + wn*xn
Also, in order to implement simple linear regression, it requires that we calculate statistical properties from the data such as mean, variance and covariance.
Please implement a simple linear regression model by providing a following:
1. Implement the functions Mean and Variance that calculate mean and variance
2. Use the data that is returned by the Mean and Variance function and implement a function Covariance, that will calculate covariance
3. Use the all previous implemented functions, and develop a function called CalculateCoefficients. Function CalculateCoefficients will take the dataset as an argument and returns the coefficients.
4. Implement a function SimpleLinearRegression that implements the prediction equation to make predictions on a test dataset. To make predictions, use The coefficients prepared from the training data.