CALC.forecast(x, known_y_values, known_x_values)
CALC.binomdist(number_s, trials, probability_s, cumulative)
CALC.expondist(x, lambda, cumulative)
CALC.forecast(x, known_y_values, known_x_values)
CALC.frequency(data_array, bins_array)
CALC.hypgeomdist(sample_s, number_sample, population_s, number_population)
CALC.intercept(arrayY, arrayX)
CALC.negbinomdist(number_f, number_s, probability_s)
CALC.percentrank(array, x, significance)
CALC.permut(number, number_chosen)
CALC.poisson(x, mean, cumulative)
CALC.prob(arrayx, arrayprob, lower_limit, upper_limit)
CALC.rank(number, array, order)
CALC.rsq(known_y_values, known_x_values)
Calculates, or predicts, a future value by using existing values. The predicted value is a y-value for a given x-value. The known values are existing x-values and y-values and the new value is predicted by using linear regression. X is the data point for which you want to predict a value. Known_y_values is the dependent array or range of data. Known_x_values is the independent array or range of data.
Example
CALC.forecast(30, [6,7,9,15,21],[20,28,31,38,40]);
// returns 10.6072
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