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# Statistics

## Correlation – Connecting the Dots, the Role of Correlation in Data Analysis

Correlation is a fundamental concept in statistics and data science. It quantifies the degree to which two variables are related. But what does this mean, and how can we use it to our advantage in real-world scenarios? Let’s dive deep into understanding correlation, how to measure it, and its practical implications. In this Blog post …

## Hypothesis Testing – A Deep Dive into Hypothesis Testing, The Backbone of Statistical Inference

Explore the intricacies of hypothesis testing, a cornerstone of statistical analysis. Dive into methods, interpretations, and applications for making data-driven decisions. In this Blog post we will learn: What is Hypothesis Testing? Steps in Hypothesis Testing 2.1. Set up Hypotheses: Null and Alternative 2.2. Choose a Significance Level (α) 2.3. Calculate a test statistic and …

## Sampling and Sampling Distributions – A Comprehensive Guide on Sampling and Sampling Distributions

Explore the fundamentals of sampling and sampling distributions in statistics. Dive deep into various sampling methods, from simple random to stratified, and uncover the significance of sampling distributions in detail. In this blog post we will learn What is Sampling? Why Sample? Types of Sampling Methods 3.1. Simple Random Sampling (SRS) 3.2. Stratified Sampling 3.3. …

## Law of Large Numbers – A Deep Dive into the World of Statistics

The Law of Large Numbers (LLN) is a fundamental theorem in probability and statistics, serving as the basis for many concepts and practices in the field. If you’ve ever heard the saying “the more the better,” you can think of LLN as the mathematical rendition of this proverb. In this blog post, we’ll dive into …

## Central Limit Theorem – A Deep Dive into Central Limit Theorem and its Significance in Statistics

Statistics offers a vast array of principles and theorems that are foundational to how we understand data. Among them, the Central Limit Theorem (CLT) stands as one of the most important. Let’s dive deeper into the concept, ensuring that all points are covered and clarified. In this blog post we will learn: Simple Explanation of …

## Skewness and Kurtosis – Peaks and Tails, Understanding Data Through Skewness and Kurtosis”

Statistics has a variety of tools to help us understand and interpret data. Two such tools are skewness and kurtosis, which give us insights into the shape of a data distribution. Let’s dive deeper into these concepts and understand their significance. In this blog post we will learn Skewness 1.1. Types of Skewness: 1.2. Rules …

## Measures of Dispersion – Unlocking the Variability Diving Deep into Measures of Dispersion

Dive deep into the world of statistics and measures of dispersion, from understanding its essence to its practical application using Python. In this Blog post we will learn: What is Dispersion in Statistics? Advantages and Applications of Measures of Dispersion: Types of Measures of Dispersion 3.1. Absolute Measure of Dispersion 3.2. Relative Measure of Dispersion …

## Quantiles and Percentiles – Understanding Quantiles and Percentiles, A Deep Dive with Python Examples

Quantiles and percentiles are crucial statistical concepts that assist in understanding and interpreting data. They are essentially tools to help divide datasets into smaller parts or intervals based on the data’s distribution. Let’s delve deep into these concepts and see them in action with Python. In this blog post we will learn Quantiles Percentiles Why …

## Measures of Central Tendency – A Clear Guide with Examples on Measures of Central Tendency

When diving into the world of statistics, you’ll frequently come across the term “measures of central tendency”. But what exactly does it mean, and why is it so important? Let’s break it down, step by step, with practical examples to drive the point home. In this blog post we will learn: What Are Measures of …

## Types of Data in Statistics – A Comprehensive Guide

Statistics is a domain that revolves around the collection, analysis, interpretation, presentation, and organization of data. To appropriately utilize statistical methods and produce meaningful results, understanding the types of data is crucial. In this Blog post we will learn Qualitative Data (Categorical Data) 1.1. Nominal Data: 1.2. Ordinal Data: Quantitative Data (Numerical Data) 2.1. Discrete …

## Descriptive and Inferential Statistics – Deep Dive into Descriptive and Inferential Statistics

In statistics understanding the difference between descriptive and inferential statistics is crucial for anyone looking to make sense of data, whether it’s for academic research, business decision-making, or just general curiosity. Let’s dive into these core concepts. In this Blog post we will learn: What is Descriptive Statistics? What is Inferential Statistics? Difference Between Descriptive …

## Partial Correlation

What is Partial Correlation and it’s purpose Partial correlation is used to find the correlation between two variables (typically a dependent and an independent variable) with the effect of other influencing variables being controlled. For example, if there are three variables ‘A’, ‘B’, ‘Z’, If you want to find the relationship between ‘A’ and ‘B’ …

## Chi-Square test – How to test statistical significance for categorical data?

What is chi-square test and its purpose? Chi-square test was invented in the year ‘1900’ by the revered mathematician ‘Karl Pearson’. Chi-square test, also written as χ2 test is used to determine whether there is a statistically significant difference between the observed frequency and the expected frequency in one or more categories of the contingency …

## Brier Score – How to measure accuracy of probablistic predictions

Brier score is an evaluation metric that is used to check the goodness of a predicted probability score. This is very similar to the mean squared error, but only applied for prediction probability scores, whose values range between 0 and 1. Overview In this tutorial, you will understand: What is Brier score? How is Brier …

## One Sample T Test – Clearly Explained with Examples | ML+

One sample T-Test tests if the given sample of observations could have been generated from a population with a specified mean. If it is found from the test that the means are statistically different, we infer that the sample is unlikely to have come from the population. For example: If you want to test a …

## Standard Error in Statistics – Understanding the concept, formula and how to calculate

Standard error of the mean measures how spread out the means of the sample can be from the actual population mean. Standard error allows you to build a relationship between a sample statistic (computed from a smaller sample of the population) and the population’s actual parameter. Standard Error – A practical guide with examples. Photo …

## Confidence Interval in Statistics – Formula and Mathematical Calculation

Confidence interval is a measure to quantify the uncertainty in an estimated statistic (like the mean) when the true population parameter is unknown. Training Custom Text Classification Model in spaCy. Photo by Jessica Wong. You will know 1. What is Confidence Interval? 2. Two types of Confidence Intervals problems 3. Difference between Population parameter vs …

## T Test (Students T Test) – Understanding the math and how it works

T Test (Students T Test) is a statistical significance test that is used to compare the means of two groups and determine if the difference in means is statistically significant. In this one, you’ll understand when to use the T-Test, the different types of T-Test, math behind it, how to determine which test to choose …

## What is P-Value? – Understanding the meaning, math and methods

P Value is a probability score that is used in statistical tests to establish the statistical significance of an observed effect. Though p-values are commonly used, the definition and meaning is often not very clear even to experienced Statisticians and Data Scientists. In this post I will attempt to explain the intuition behind p-value as …

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