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Single Variable Simple Regression

For a given data set of value pairs (x_i, y_i), where x_i is an independent variable, find the function y_i = f(x_i) + ε_i
such that the sum of squares of errors e_i is minimal.
To do that, first select the type of the function that you think corresponds the data set (x_i, y_i), and then enter in the table the values of the data set.

Please select the type of a function y_i = f(x_i) + ε_i you wish to fit:

Linear y = β x + α
^nd degree Polinomial y = a₂ x² + a₁ x + a₀
Logarithmic y = ln(β x + α)
Exponential y = e ^{β x + α}

Data Set:

x_i	y_i

Additional Statistics

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Slope β =
Intercept α =
Coefficient of Determination R^{2 =}
Degrees of Freedom =
Regression Sum of Squares *SS_reg* =
Residual Sum of Squares *SS_resid* =
Standard Error of Estimate *SE_v* =
F-Ratio F =
Standard Error of Slope SE₁ =
Standard Error of Intercept *SE_b* =