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.

This project presents a simulation study of business performance using probability distributions. The number of products sold per day is modeled using a Poisson distribution with an average of 100 units per day to represent count data. Based on this simulation, daily income is modeled using a Normal distribution with a mean of 500,000 rupiah and a standard deviation of 50,000 rupiah to represent continuous revenue variation. The simulation is conducted over 365 days to analyze average performance and estimate probabilities of extreme sales and income outcomes. This approach demonstrates the application of discrete and continuous random variables in real-world business modeling.

This file contain the basic implementation and example of simulating and statistics modelling