Return the fit (parameter vector b, quality of fit rSquared)
Show the flaw by printing the error message.
Show the flaw by printing the error message.
the method where the error occurred
the error message
Predict the value of y = f(z) by evaluating the formula y = b dot z for each row of matrix z.
Predict the value of y = f(z) by evaluating the formula y = b dot z for each row of matrix z.
the new matrix to predict
Predict the value of y = f(z) by evaluating the formula y = b dot z, i.
Predict the value of y = f(z) by evaluating the formula y = b dot z, i.e., (b0, b1) dot (1., z1).
the new vector to predict
Given a new discrete data vector z, predict the y-value of f(z).
Given a new discrete data vector z, predict the y-value of f(z).
the vector to use for prediction
Train the predictor by fitting the parameter vector (b-vector) in the simple regression equation y = b dot x + e = (b0, b1) dot (1.
Train the predictor by fitting the parameter vector (b-vector) in the simple regression equation y = b dot x + e = (b0, b1) dot (1., x1) + e using the least squares method.
http://www.analyzemath.com/statistics/linear_regression.html
The SimpleRegression class supports simple linear regression. In this case, x is one-dimensional (1, x1). Fit the parameter vector b in the regression equation y = b dot x + e = (b0, b1) dot (1., x1) + e = b0 + b1 * x1 + e where e represents the residuals (the part not explained by the model).