Skip to main content

Descriptive Statistics

  • Understand key measures of central tendency (mean, median, mode).
  • Learn about measures of dispersion (range, variance, standard deviation).
  • Explore data visualization techniques.

Measures of Central Tendency

Central tendency measures describe the center of a dataset. The most common measures are:

Measure Formula Definition
Mean (Arithmetic Average) \( \bar{x} = \frac{\sum x_i}{n} \) Sum of all values divided by the number of values.
Median Middle value when data is ordered If \( n \) is odd, the middle value; if even, the average of two middle values.
Mode Most frequently occurring value A dataset can have no mode, one mode, or multiple modes.

Measures of Dispersion

Dispersion measures how spread out the data points are. Key measures include:

Measure Formula Definition
Range \( \text{Range} = \max(x) - \min(x) \) Difference between the largest and smallest value.
Variance \( \sigma^2 = \frac{1}{n} \sum (x_i - \bar{x})^2 \) Measures the squared deviation of each value from the mean.
Standard Deviation \( \sigma = \sqrt{\sigma^2} \) Square root of the variance, indicating the typical deviation from the mean.

Exercises

  • Question 1: Find the mean, median, and mode of {5, 8, 9, 7, 6, 5, 5, 10}.
  • Question 2: Compute the range, variance, and standard deviation for {4, 5, 6, 7, 8, 9, 10}.
  • Question 3: Draw a histogram for the dataset {2, 3, 3, 4, 4, 4, 5, 5, 6}.
  • Answer 1: Mean = 6.875, Median = 6.5, Mode = 5.
  • Answer 2: Range = 6, Variance = 4.67, Standard Deviation ≈ 2.16.
  • Answer 3: The histogram below represents the dataset from Question 3:

This Week's Best Picks from Amazon

Please see more curated items that we picked from Amazon here .

Popular posts from this blog

Gaussian Elimination: A Step-by-Step Guide

Gaussian Elimination: A Step-by-Step Guide Gaussian Elimination is a systematic method for solving systems of linear equations. It works by transforming a given system into an equivalent one in row echelon form using a sequence of row operations. Once in this form, the system can be solved efficiently using back-substitution . What is Gaussian Elimination? Gaussian elimination consists of two main stages: Forward Elimination: Convert the system into an upper triangular form. Back-Substitution: Solve for unknowns starting from the last equation. Definition of a Pivot A pivot is the first nonzero entry in a row when moving from left to right. Pivots are used to eliminate the elements below them, transforming the system into an upper triangular form. Step-by-Step Example Consider the system of equations: \[ \begin{aligned} 2x + 3y - z &= 5 \\ 4x + y...

Singular Value Decomposition

Lesson Objectives ▼ Understand the concept of Singular Value Decomposition (SVD). Decompose a matrix into orthogonal matrices and singular values. Learn how to compute SVD step by step. Explore applications of SVD in data compression and machine learning. Lesson Outline ▼ Definition of SVD Computing SVD Step by Step Applications of SVD Examples Definition of Singular Value Decomposition The **Singular Value Decomposition (SVD)** of an \( m \times n \) matrix \( A \) is a factorization of the form: \[ A = U \Sigma V^T \] where: \( U \) is an \( m \times m \) orthogonal matrix (left singular vectors). \( \Sigma \) is an \( m \times n \) diagonal matrix with **singular values** on the diagonal. \( V \) is an \( n \times n \) orthogonal matrix (right singular vectors). Computing SVD Step by Step To compute \( A = U \Sigma V^T \): Find the eigenvalues and eige...

LU Decomposition

LU Decomposition: A Step-by-Step Guide LU Decomposition, also known as LU Factorization, is a method of decomposing a square matrix into two triangular matrices: a lower triangular matrix L and an upper triangular matrix U . This is useful for solving linear equations, computing determinants, and inverting matrices efficiently. What is LU Decomposition? LU Decomposition expresses a matrix A as: \[ A = LU \] where: L is a lower triangular matrix with ones on the diagonal. U is an upper triangular matrix. Step-by-Step Process Consider the matrix: \[ A = \begin{bmatrix} 2 & 3 & 1 \\ 4 & 7 & 3 \\ 6 & 18 & 5 \end{bmatrix} \] Step 1: Initialize L as an Identity Matrix Start with an identity matrix for \( L \): \[ L = \begin{bmatrix} 1 & 0 & 0 \\ 0 ...