# Tag Archives: statistics

## True Error vs Sample Error: Difference

Understanding the differences between true error and sample error is an important aspect of data science. In this blog post, we will be exploring the difference between these two common features of statistical inference. We’ll discuss what they are and how they differ from each other, as well as provide some examples of real-world scenarios where an understanding of both is important. By the end, you should have a better grasp of the differences between true error and sample error. In case you are a data scientist, you will want to understand the concept behind the true error and sample error. These concepts are key to understand for evaluating a …

## Confidence Intervals Formula, Examples

In this post, you will learn about the statistics concepts of confidence intervals in relation to machine learning models with the help of an example and Python code examples. You will learn about how to interpret confidence intervals, what are formulas for confidence intervals with the help of examples. When you get a hypothesis function by training a machine learning classification model, you evaluate the hypothesis/model by calculating the classification error. The classification error is calculated on the sample of the data used for training the model. However, does this classification error for the sample (sample error) also represent (same as) the classification error of the hypothesis/model for the entire …

## 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 …

## 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 …

## 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 …

## Central Limit Theorem: Concepts & Examples

The central limit theorem is one of the most important concepts in statistics. This theorem states that, given a large enough sample size, the distribution of sample averages will be approximately normal. This is a huge deal because it means that we can use the normal distribution to make predictions about populations based on samples. In this article, we’ll explore the central limit theorem in more detail and look at some examples of how it works. As data scientists, it is important to understand the central limit theorem so that we can apply it to real-world situations. What is the central limit theorem and why is it important? The central …

## Statistics – Random Variables, Types & Python Examples

Random variables are one of the most important concepts in statistics. In this blog post, we will discuss what they are, their different types, and how they are related to the probability distribution. We will also provide examples so that you can better understand this concept. As a data scientist, it is of utmost importance that you have a strong understanding of random variables and how to work with them. What is a random variable and what are some examples? A random variable is a variable that can take on random values. The key difference between a variable and a random variable is that the value of the random variable …