This file contain 3 keys to make conclusions about population only using sample which are, central limit theorem, unbiased predictor parameter, and confidence interval. 1. Central Limit Theorem The Central Limit Theorem states that as the sample size increases, the distribution of the sample mean will tend to follow a normal distribution, regardless of the shape of the original population distribution. 2. Unbiased Parameter Predictor An unbiased estimator is a statistic calculated from sample data that, on average, equals the true value of the population parameter. For example, the sample mean is an unbiased estimator of the population mean, and the sample variance with denominator n−1 is an unbiased estimator of the population variance. 3. Confidence Interval Confidence interval is where we trust how much the interval contain the actual parameter value. For example if we do 95% confidence interval, then we trust that 95% of the interval contain the actual value of parameter, if we take 100 confidence interval then it would likely 95 of them contain parameter and 5 of them arent. The bigger the percentage, itll make the interval wider. Cause it will try to contain more of the parameter. While lower percentage may make the interval narrower cause itll only take a part of the parameter.

Se utilizan las distancia más comunes para clasificación.

GPA Calculation