 Wanna become a data scientist within 3 months, and get a guaranteed job? Then you need to check this out !

## Statistical analysis

is a technique for drawing conclusions from data. The process uses a variety of formal and mathematical techniques. Statistical analysis methods can be applied in a wide range of fields by scientists to analyse the evidence for their hypothesis, in order to help businesses, make predictions and decisions about their products and services.

Statistical analysis methods are used to extract accurate information from numerical data. There are various statistical techniques which can be used for analysing the collected data. The most popular and widely-used statistical analysis methods include:

### Some of the widely-used statistical analysis methods include:

1. Mean: This is one of the most popular statistical analysis technique in many fields. The mean is calculated by finding t\he sum of all values and dividing it by the number of values. The most common use of a mean is in a set of data that has an equal distribution or distribution in the shape of a bell curve. One characteristic feature of this distribution is that there are no extreme values.
2. Regression: This is a statistical method for analysing the relationships among variables.The idea behind regression is to study the relationship between the cause and an effect. There are many types of regression models, but one of the most commonly used is linear regression.
3. Hypothesis Testing: This technique is mainly applied for continuous variables, by making a decision about the probability that the population parameter equals a specified value, or that it does not equal that specified value.
4. Confidence Intervals: This technique is mainly applied for interval variables, by providing an interval which has a specified level of confidence such as 95%, 99% or 100%.
5. Sample size determination: This is the process of determining the number of observations or replicates to include in a statistical sample. There are two important considerations when determining sample size: the level of precision desired and the level of accuracy needed. The first consideration is that if you want to have high precision, then you need a larger sample size. When you want to have high accuracy, then you need a smaller sample size. For example, if you are trying to measure the weight of an item with an accurate measurement to 1 gram, then it would require a very small sample size. Whereas if your goal is to measure the weight of an item with a precise measurement to 1 milligram, then it would require a much larger sample size.

Wanna become a data scientist within 3 months, and get a guaranteed job? Then you need to check this out !

Categories: Data science