# 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.6. 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*