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Articles by Nikitasha Shrivastava
163 articles
Show Normal Inverse Gaussian Distribution in Statistics using Python
The Normal Inverse Gaussian (NIG) distribution is a continuous probability distribution commonly used in finance and risk management. It's characterized as a normal variance-mean mixture with the inverse Gaussian distribution as the mixing density. Understanding NIG Distribution The NIG distribution has four parameters: alpha (α) − Controls the steepness of the distribution beta (β) − Controls the asymmetry (skewness) mu (μ) − Location parameter (mean) delta (δ) − Scale parameter Implementing NIG Distribution We'll use Python's scipy.stats.norminvgauss to create and visualize the distribution ? import numpy as np import matplotlib.pyplot as ...
Read MoreShow Normal Distribution in Statistics using Python
Normal distribution, also known as Gaussian distribution, is a fundamental probability distribution in statistics with a characteristic bell-shaped curve. Python provides powerful libraries to visualize and work with normal distributions effectively. What is Normal Distribution in Statistics? Normal distribution is a continuous probability distribution that is symmetric around its mean. It has several key properties: Bell-shaped curve − The distribution forms a symmetric bell shape Mean, median, and mode − All three are equal and located at the center 68-95-99.7 rule − Approximately 68% of data falls within 1 standard deviation, 95% within 2, and 99.7% ...
Read MoreShow Non-Central F-Distribution in Statistics using Python
In statistics, the Non-Central F-Distribution is a probability distribution used for analyzing variance in data when the null hypothesis is false. Unlike the central F-distribution, it includes a non-centrality parameter that shifts the distribution, making it useful for power analysis and hypothesis testing. Understanding the Non-Central F-Distribution The Non-Central F-Distribution extends the central F-distribution by adding a non-centrality parameter (λ). This distribution is characterized by three parameters: Numerator degrees of freedom (dfn) Denominator degrees of freedom (dfd) Non-centrality parameter (nc) The non-centrality parameter determines how much the distribution deviates from the central F-distribution. When ...
Read MoreShow Non-Central Chi-squared Distribution in Statistics using Python
The non-central chi-squared distribution is a probability distribution used in statistical power analysis and hypothesis testing. Unlike the standard chi-squared distribution, it includes a non-centrality parameter that shifts the distribution, making it useful for modeling scenarios with non-zero effects. Understanding the Non-Central Chi-squared Distribution The non-central chi-squared distribution generalizes the standard chi-squared distribution by adding a non-centrality parameter. It has two key parameters: df − degrees of freedom (controls the shape) nc − non-centrality parameter (controls the location shift) This distribution appears in signal processing, wireless communication, and statistical power analysis where you need ...
Read MoreShow Negative Binomial Discrete Distribution in Statistics using Python
The Negative Binomial Distribution represents the number of failures that occur before achieving a fixed number of successes in a series of independent trials. We can visualize this distribution using Python's NumPy and Matplotlib libraries. What is Negative Binomial Distribution? The Negative Binomial Distribution models scenarios where we count failures before reaching a target number of successes. For example, how many coin flips result in tails before getting 5 heads? The distribution has two key parameters: r − Number of successes required p − Probability of success on each trial Basic Example ...
Read MoreShow Nakagami Distribution in Statistics using Python
The Nakagami distribution is a probability distribution commonly used in wireless communications to model signal fading. Python's scipy.stats module provides tools to work with this distribution, allowing us to calculate probability density functions and visualize the results. What is Nakagami Distribution? The Nakagami distribution is a continuous probability distribution with two parameters: shape (ν) and scale (Ω). It's particularly useful in modeling multipath fading in wireless communication systems, where signals reach the receiver through multiple paths. Parameters The Nakagami distribution has two key parameters ? Shape parameter (ν) − Controls the shape of the ...
Read MoreShow Moyal Distribution in Statistics using Python
The Moyal distribution is a continuous probability distribution that appears in high-energy physics and statistics. Python's NumPy and Matplotlib libraries provide an excellent way to generate and visualize this distribution. What is Moyal Distribution? The Moyal distribution is a probability distribution used to model energy loss of fast charged particles passing through matter. It's characterized by an asymmetric shape with a long tail on the positive side. Understanding the Mathematical Foundation The Moyal distribution can be generated using the difference of two exponential random variables. If U₁ and U₂ are uniform random variables, then: ...
Read MorePython - Occurrence counter in List of Records
In this article we will explain how to count the occurrences or repetition of elements in the given list of records using Python. Sometimes we need to make a count for the repeated number of items in the given dataset so this article will be helpful to solve these kinds of problems. Understanding the Problem The problem we have is to count the repeated items in the given list of records using the Python programming language. So basically we have to show the result of counts of the same or identical items in the given list of records. ...
Read MorePython - Numeric Sort in Mixed Pair String List
Sometimes we need to sort lists containing mixed data types, particularly strings that include numeric values. Python provides several approaches to perform numeric sorting on mixed pair string lists where each element contains both text and numbers. Understanding the Problem We have a list where each element is a string containing a name and a number (like "Daniel 4", "Oliver 7"). The goal is to sort this list based on the numeric values rather than alphabetically. For example ? Input List: ['Daniel 4', 'Oliver 7', 'Jack 3', 'Henry 9'] ...
Read MorePython - N Random Tuples list
The problem statement is to generate N random tuples using Python's random module. This is useful in applications that require random data generation, simulations, or testing scenarios. Understanding the Problem We need to generate a specified number of tuples, where each tuple contains random integers within a given range. Here's what we need ? Number of tuples = 5 Minimum value = 0 Maximum value = 10 Generated list = [(4, 1), (2, 10), (5, 3), (6, 3), (1, 7)] Algorithm Step 1 − Import the random module for generating random integers. ...
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