Key Concepts of Statistic Essential for Data Analysis

In data analysis, statistics plays a significant role in well-structured data from unstructured or we can say raw one. Statistical contains many techniques like identify the patterns in data, hypotheses test, summarize and make predictions for the future benefits. It is important to learn these techniques for every learner. So, if you are a student of data analytics training in Delhi or want to be one then this blog has got a lot for you. Let’s know about the key concepts of statistics essential for data analysis.

Statistics is the backbone of data analysis. It provides many techniques and tools needed to collect, interpret and analysis data. Here data can be in large dataset or small samples it works on both.

Statistical concepts provide accurate, meaningful and reliable information about the data so that we can study it and make future predictions or decisions for our future gains.

Statistics is the very essential process of collecting and analysing the data in order to find the pattern and make it easy and more understandable for the better research, planning survey or studies. This concept is useful for business analyst, data scientist and analyst.

Statistical Concepts for Data Analysis

Here’s the overview of the statistical concepts necessary and useful for data analysis.                      

Descriptive Statistics

Descriptive statistics involves collecting of data, summarizing and visualizing data to understand its central tendency. This converts into meaningful data, analysing and summarising it in the form of charts, graphs and tables. It simply makes the complex data into simpler one for easy to read and better understanding. These concepts form your basis. They help in understanding the general characteristics of the data. This is a crucial part to clear before conducting more advanced analyses.

Checkout a few main concepts of descriptive analysis:

1. Measures of Central Tendency — We take out mean, median and mode of data through this method. Or in other words, we find data values which lie in the middle of the distribution. Or it can be toward the end.
2. Measures of Dispersion– The variations of the items among an average. There are 2 types high and low dispersion.
3. If data points are far from the center called high dispersion.
4. If data points are close from the center called low dispersion.

Probability Theory

The probability theory is an integral way to make future predictions using present data. Using this function or method we find out the probability of possible different values of a variable. It is also useful to make decisions for gains.

We use this theory to define the populations of variables that are related to real life cases. For example, coin tosses or the weight of any object, etc.

• Random Variables: These are variables that follow probability distributions. They are denoted by ‘X’.

Probability Distributions- There are different types of distributions like:  normal, binomial, Poisson, etc. They are crucial for modeling data, making strategies, and estimating probabilities.

Types of Probability Distributions

1. Discrete probability distributions: These are categorical variables that have the probability of possible values only. It doesn’t include any values with the 0 probability.
2. Continuous probability distributions- probability distribution where the probability of a continuous random variable’s possible values.

Some conditions of probability are:

• The probability table has 2 tables one is value or class interval and another is probability.
• No 0 probability
• The sum would be 1

Sampling

• Population is the bunches of people by which data is to be collected for the research like gender population etc.
• Sample is a subset of a population.

Sample data is collected from the population data. This data is important for making conclusions about large sets of data with the help of small insights sets. Sampling can be done by many techniques like random sampling, cluster samplings, etc.

Hypotheses Testing

Statistical hypothesis testing is a process of statistical inference. We use it to determine whether the data is sufficiently effective for a particular hypothesis. It is a formal procedure for innovations of new Ideas about the world using statistics. This test has an integral part in statistics.

Stating a Hypothesis

Null Hypothesis

• It is a statistical hypothesis.
• It contains a statement of equality such as ≤,=,≥.
• Denoted H0 read as “sub-zero”.

Alternative Hypothesis

• A statement of strict inequality such a >,<,≠.
• Must be true if H0 is false.
• Denoted by Ha and read as “H sub-a”.

Inferential Statistics

Inferential statistics one of the fields of statistics. It utilizes analytical tools for drawing outcomes about a collection of data by some random sample.

Inferential statistics is better for utilization as well as cost-effectiveness. It is because, it gives inferences on the sample data without even collecting the whole insights.

Some inferential statistics examples are given below:

If we find out the mean marks of 100 students in a particular country then we can use this sample data to represent the marks of the whole school. It is possible because of inferential statistics.

Keys of inferential statistics are –

• Helps to get valuable conclusions from a given sample.
• We can find out whether the collect sample data is statistically significant to the whole population or not.
• It also tells to add or remove a variable. It further helps in improving the model’s feature selection.
• It makes inferences about a large group of individuals from the parameter given.
• We can easily compare models to identify the more statistically significant.

Conclusion

This analysis can make unstructured or unnecessary data convert into a meaningful collection of insights. We then use that data in many ways like:

• Making future predictions, business decision-making surveys, etc.

Thus, it gives better planning to us in the comparison of other analyses. So, no doubt it is very effective for collecting numerical data and using it in every field or survey.

Anyone can use this approach from businesses, sellers, researchers, or in any government survey. Even politicians can use this.

• The researchers used this to make their future predictions to maintain their business/ institutions/ or by government. For example, weather reports, and different collections of data like population, gender, etc.

The integral part is mainly integers or we can say it mainly works with integers.

So, we have seen key concepts of statistic essentials for data analytics. Also seen their types to find different ways to examine the collection of data for better understanding.