Basics of Statistics
Now-a-days, everybody is talking about machine learning, AI and data science where statistics is very under-rated and people are less interested in learning about it. Statistics is the key component in the world of data science which provides essential tools and techniques for extracting insights and knowledge from data.
Statistics is a branch of mathematics that deals with collecting, analyzing, and interpreting data. It plays a vital role in many fields, including science, medicine, business, and social sciences. In this blog post, we will provide an overview of some basic concepts of statistics.
Population and Sample:
In statistics, a population refers to the entire group of individuals, objects, or events that we are interested in studying. For example, if we want to study the average height of all people in a country, the population would be all the people living in that country. However, studying the entire population is often impractical or impossible, so we often use a sample, which is a smaller group of individuals or objects that we select from the population. The goal is to use the sample to draw conclusions about the population.
Descriptive Statistics:
Descriptive statistics are used to describe and summarize data. They can be used to describe the central tendency, variability, and distribution of a dataset. Measures of central tendency include the mean, median, and mode, which are used to describe the average or typical value of a dataset. Measures of variability include the range, variance, and standard deviation, which describe the spread of the data. The distribution of a dataset can be described using a histogram, frequency table, or box plot.
Inferential Statistics:
Inferential statistics are used to make inferences about a population based on a sample. This involves using probability theory to estimate the likelihood of observing a particular sample given a specific population. Inferential statistics also involve hypothesis testing, which is used to determine whether an observed difference between two groups is statistically significant or just due to chance.
Probability:
Probability is a fundamental concept in statistics that is used to describe the likelihood of an event occurring. It is usually expressed as a number between 0 and 1, where 0 means the event is impossible, and 1 means the event is certain. For example, the probability of flipping a coin and getting heads is 0.5.
Correlation and Regression:
Correlation and regression are statistical techniques used to measure the relationship between two variables. Correlation measures the strength and direction of the relationship between two variables. Regression is used to predict the value of one variable based on the value of another variable. For example, we might use regression to predict a person’s weight based on their height.
The definition of Regression comes under supervised Machine learning.
In conclusion, statistics is a complex and important field that plays a vital role in many areas of our lives. The concepts we have discussed in this blog post provide a basic understanding of some of the fundamental ideas in statistics. By using these concepts, we can analyze and interpret data to make informed decisions and draw meaningful conclusions about the world around us.
References -
https://online.stat.psu.edu/statprogram/reviews
