# Category Archives: statistics

## Bayesian thinking & Real-life Examples

Bayesian thinking is a powerful way of looking at the world, and it can be useful in many real-life situations. Bayesian thinking involves using prior knowledge to make more accurate predictions about future events or outcomes. It is based on the Bayes theorem, which states that the probability of an event occurring is determined by its prior probability combined with new information as it becomes available. It is important for data scientists to learn about Bayesian thinking because it can help them make accurate predictions and draw more meaningful insights from data. In this blog post, we will discuss Bayesian thinking and provide some examples from everyday life to illustrate …

## Paired Sample T-Tests: Formula, Examples

Paired sample t-tests are a commonly used statistical procedure used to compare two populations that are related in some way. They are often used for comparing dependent groups, such as the before and after results of an experiment. Data scientists must have a thorough understanding of the concept of paired sample t-test in order to craft accurate and reliable results when analyzing data. In this blog post, we will explore the formula, assumptions, and examples of paired sample t-tests. What is Paired Sample T-Test and Why is it needed? Paired sample t-tests are used to test whether means of same or similar group different from each other under separate conditions …

## Pearson Correlation Coefficient & Statistical Significance

In this post, we will discuss what Pearson’s r represents, how it works mathematically, its interpretation, statistical significance, and importance for making decisions in real-world applications such as business forecasting or medical diagnosis. We will also explore some examples of using Pearson’s r with real data sets so you can see how this powerful statistic works in action. As a data scientist, it is very important to understand Pearson’s r and its implications for making decisions based on data. What is Pearson Correlation Coefficient? Pearson correlation coefficient is a statistical measure that describes the linear relationship between two variables. It is typically represented by the symbol ‘r’. Pearson correlation coefficient …

## Types & Uses of Moments in Statistics

In statistics, moments are measures of the shape and variability of a data set. They are used to describe the location and dispersion of the data. There are several types of moments that can be calculated, each providing different information about the data set. Let’s take a look at some of these moments and how they can be used in statistical analysis. What are moments in Statistics and what are their types? In statistics, moments are an important tool used to measure the characteristics of a distribution. Moments can provide useful information about the spread, shape, and center of a distribution. The following are different types of moments: First moment …

## Two independent samples t-tests: Formula & Examples

In statistics, the independent samples t-test, also known as unpaired two samples t-test, is a type of hypothesis test that can be used to determine whether the means of two independent groups are significantly different given the two samples are independent and have normal distributions. As data scientists, it is important to understand how to use the two sample t-test for independent samples so that you can correctly analyze your data. In this blog post, we will discuss the two samples t-test for independent samples in detail, including the formula and examples. What is independent-samples or unpaired two samples T-test? The independent samples T-test is defined as statistical hypothesis testing …

## Levene Test & Statistics: Concepts & Examples

The Levene test is used to test for equality of variance in a dataset. It is used in statistical analysis to determine if two or more samples have similar variances. If the results of the test indicate that the samples do not have similar variances, then it means that one sample has a higher variance than the other and should be treated as an outlier. In this blog post, we’ll take a look at what exactly the Levene test is, how it works, and provide some examples of how it can be applied. As data scientists, it will be important for us to understand the Levene test in order to …

## Logit vs Probit Models: Differences, Examples

Logit and probit models are statistical models that are used to model binary or dichotomous dependent variables. This means that the outcome of interest can only take on two possible values. In most cases, these models are used to predict whether or not something will happen. For example, a business might want to know if a particular advertising campaign will lead to an increase in sales. In this blog post, we will explain what logit and probit models are, and we will provide examples of how they can be used. As data scientists, it is important to understand the concepts of logit and probit models and when should they be …

## Categorical Data Visualization: Concepts, Examples

Everyone knows that data visualization is one of the most important tools for any data scientist or statistician. It helps us to better understand the relationships between variables and identify patterns in our data. There are specific types of visualization used to represent categorical data. This type of data visualization can be incredibly helpful when it comes to analyzing our data and making predictions about future trends. In this blog, we will dive into what categorical data visualization is, why it’s useful, and some examples of how it can be used. Types of Data Visualizations for Categorical Dataset When it comes to visualizing categorical data sets, there are primarily four …

## Types of Probability Distributions: Codes, Examples

In this post, you will learn the definition of 25 different types of probability distributions. Probability distributions play an important role in statistics and in many other fields, such as economics, engineering, and finance. They are used to model all sorts of real-world phenomena, from the weather to stock market prices. Before we get into understanding different types of probability distributions, let’s understand some fundamentals. If you are a data scientist, you would like to go through these distributions. This page could also be seen as a cheat sheet for probability distributions. What are Probability Distributions? Probability distributions are a way of describing how likely it is for a random …

## Data Variables Types & Uses in Data Science

In data science, variables are the building blocks of any analysis. They allow us to group, compare, and contrast data points to uncover trends and draw conclusions. But not all variables are created equal; there are different types of variables that have specific uses in data science. In this blog post, we’ll explore the different variable types and their uses in data science. The picture below represents different types of variables one can find when working on statistics / data science projects: Lets understand each types of variables in the following sections. Categorical / Qualitative Variables Categorical variables are a type of data that can be grouped into categories, based …

## Types of Frequency Distribution & Examples

Frequency distributions are an important tool for data scientists, statisticians, and other professionals who work with data. Frequency distributions help to organize and summarize data, making it easier to identify the behavior of the data including patterns and trends. Evaluating frequency distribution is one of the important technique of univariate descriptive statistics. In this article, we’ll take a look at the concepts of the frequency distribution, its different types and provide some examples of each. What is Frequency Distribution? Frequency distribution is a statistical tool used to represent the frequency with which different categories of a qualitative or quantitative variable occur. It provides an overview of the data and allows …

## Wilcoxon Rank Sum Test: Concepts, Examples

The Wilcoxon rank sum test is a statistical test used to compare two sets of data. This test is also known as the Mann-Whitney U test. It is a non-parametric statistical hypothesis test used to compare two samples. It is similar to the Student’s t-test, but does not require the assumption of normality. The test is appropriate for use with small sample sizes. What is Wilcoxon Rank Sum Test? The Wilcoxon rank sum test is a statistical test used to compare two independent samples. The test is used to compare the medians (location of medians) in the two samples. The null hypothesis is that the location of medians in two …

## Different Types of Statistical Tests: Concepts

Statistical tests are an important part of data analysis. They help us understand the data and make inferences about the population. They are used to examine relationships between variables and test hypotheses. They are a way of analyzing data to see if there is a significant difference between the two groups. In statistics, there are two main types of tests: parametric and non-parametric. Both types of tests are used to make inferences about a population based on a sample. The difference between the two types of tests lies in the assumptions that they make about the data. Parametric tests make certain assumptions about the data, while non-parametric tests do not make …

## Generate Random Numbers & Normal Distribution Plots

In this blog post, we’ll be discussing how to generate random numbers samples from normal distribution and create normal distribution plots in Python. We’ll go over the different techniques for random number generation from normal distribution available in the Python standard library such as SciPy, Numpy and Matplotlib. We’ll also create normal distribution plots from these numbers generated. Generate random numbers using Numpy random.randn Numpy is a Python library that contains built-in functions for generating random numbers. The numpy.random.randn function generates random numbers from a normal distribution. This function takes size N as in number of numbers to be generated as an input and returns an array of N random …

## Top Python Statistical Analysis Packages

As a data scientist, you know that one of the most important aspects of your job is statistical analysis. After all, without accurate data, it would be impossible to make sound decisions about your company’s direction. Thankfully, there are a number of excellent Python statistical analysis packages available that can make your job much easier. In this blog post, we’ll take a look at some of the most popular ones. SciPy SciPy is a Python-based ecosystem of open-source software for mathematics, science, and engineering. SciPy contains modules for statistics, optimization, linear algebra, integration, interpolation, special functions, Fourier transforms (FFT), signal and image processing, and other tasks common in science and …

## Covariance vs. Correlation vs. Variance: Python Examples

In the field of data science, it’s important to have a strong understanding of statistics and know the difference between related concepts. This is especially true when it comes to the concepts of covariance, correlation, and variance. Whether you’re a data scientist, statistician, or simply someone who wants to better understand the relationships between different variables, it’s important to know the difference between covariance, correlation, and variance. While these concepts may seem similar at first glance, they each have unique applications and serve different purposes. In this blog post, we’ll explore each of these concepts in more detail and provide concrete examples of how to calculate them using Python. What …