Скачать с ютуб Mastering Normal Distribution Real-World Examples & Live Demo Explained | AI ML Course | Srinivasan в хорошем качестве

Mastering Normal Distribution Real-World Examples & Live Demo Explained | AI ML Course | Srinivasan

#aiml #srinivasanramanujam #statistics Understanding Normal Distribution with Examples Normal Distribution (also known as Gaussian distribution) is one of the most fundamental probability distributions in statistics and data analysis. It is widely used in various fields because many natural phenomena tend to follow a normal distribution. Key Characteristics of Normal Distribution: Bell-Shaped Curve: The normal distribution has a symmetric, bell-shaped curve centered around the mean. The mean, median, and mode are all equal and located at the center of the distribution. Standard Deviation: The spread of the data around the mean is determined by the standard deviation. A smaller standard deviation results in a narrower peak, while a larger standard deviation leads to a wider curve. Empirical Rule (68-95-99.7 Rule): 68% of the data falls within one standard deviation ( 𝜎 σ) of the mean. 95% of the data falls within two standard deviations ( 2 𝜎 2σ) of the mean. 99.7% of the data falls within three standard deviations ( 3 𝜎 3σ) of the mean. Examples of Normal Distribution: Heights of People: The heights of adults of the same gender and age group typically follow a normal distribution, with most individuals clustered around the average height. IQ Scores: IQ scores in a population are often normally distributed with a mean of 100 and a standard deviation of 15. Measurement Errors: In experiments, random errors often follow a normal distribution, where the mean represents the true value and deviations represent the random errors. Exam Scores: In large classes, the distribution of students' scores in exams can often resemble a normal curve, with most students scoring around the average. Example: Live Demo for Problem Identification To help you grasp the concept visually, a live demo would involve plotting real-world data points to see if they form a normal distribution. For instance: Problem Statement: "Is the distribution of exam scores in a university math class normally distributed?" Live Demo Steps: Collect the data of students' scores. Plot a histogram of the scores. Overlay the normal distribution curve to see if it matches the data. Use statistical tests (e.g., Shapiro-Wilk test) to confirm normality.