# Statistics

### I. Data analysis

#### I.1. Normal distribution

*Most processes are described fairly accurately by the normal distribution. The normal distribution is used in chemistry, physics, and many other fields of science*

#### I.2. Poisson distribution

*When we operate with the value of the number of events in a time interval, for example, three sandwiches a day, then we use the Poisson distribution*

#### I.3. Distribution law of a random variable

*How can we mathematically describe that on average we drink two cups of tea a day? What is likely to be +20 in June? That the gasoline consumption will be 8L/100km? Distribution law is a mathematical model of events*

#### I.4. Parameters of the discrete distribution law

*The main parameters of the distribution law: mean, quantile, deviation, confidence interval*

#### I.5. Statistical hypothesis

*With a probability of 30%, the milk will sour until tomorrow. And tomorrow we need to sell three cups of milk. Should I buy a new bottle? What if I don't buy it, and the milk turns sour? The statistical hypothesis will give the answer in numbers*

#### I.7. Normality of the distribution

*Some statements are based on the premise that the distribution is normal, but how do you know how normal the real distribution is?*

#### I.8. Analysis of variance

*It happens that we conduct a number of experiments, but we cannot find a pattern. Why is the tea delicious, then not? ANOVA is a method for finding the reason for changing the result. Is it the quality of the tea or my mood?*

#### I.9. Correlation analysis

*Is there a connection between the changes in values? Two numbers change, but in order to draw a conclusion about the linear dependence of the change, it is necessary to resort to correlation analysis*

### II. Forecasting

#### II.1. Linear regression

*From the article you will learn the basics of regression analysis: how to choose a regression model, what regression models there are and why this model is needed at all. Also, what methods are used to determine the quality of the model*

#### II.2. Time series analysis +1

*What can the growth schedule of visitors be divided into? Seasonality, trend and noise are the three main indicators of the time series. The selection of components allows you to analyze the influence of various factors, for example, weather*

#### II.3. The Monte Carlo method

*The first step is to put forward a hypothesis, the second step is to simulate processes and obtain statistical justification. The Monte Carlo method conducts tests based on a hypothesis and answers the question: will the hypothesis fail or is it true*