Statistics

📊 Online Statistics Course – Overview

Course Title: Introduction to Statistics
Format: Fully Online (self-paced or instructor-led)
Duration: 8–12 weeks
Level: Beginner to Intermediate
Prerequisites: Basic algebra and comfort with numbers

🎯 Course Purpose

This course introduces the foundational concepts of statistics and how to apply them in real-world contexts. Students will learn how to collect, organize, analyze, and interpret data using both manual methods and digital tools.

🧠 Learning Objectives

By the end of the course, students will be able to:

• Understand and apply key statistical concepts and terminology

• Summarize data using descriptive statistics

• Interpret and create various types of charts and graphs

• Understand probability and its applications

• Conduct statistical experiments and surveys

• Perform hypothesis testing and basic inferential analysis

• Use software tools like Excel, Google Sheets, or statistical packages

📝 Course Content Breakdown

Module 1: Introduction to Statistics

• What is statistics?

• Types of statistics: descriptive vs. inferential

• Types of data: qualitative vs. quantitative

• Levels of measurement: nominal, ordinal, interval, ratio

Module 2: Organizing and Displaying Data

• Frequency tables and histograms

• Bar charts, pie charts, line graphs

• Stem-and-leaf plots, dot plots

• Interpreting and comparing data sets

Module 3: Descriptive Statistics

• Measures of central tendency: mean, median, mode

• Measures of dispersion: range, variance, standard deviation

• Percentiles, quartiles, and interquartile range (IQR)

Module 4: Probability Concepts

• Basic probability rules

• Independent and dependent events

• Probability distributions

• Introduction to normal distribution

Module 5: Discrete and Continuous Distributions

• Binomial distribution

• Normal distribution

• Empirical rule and z-scores

• Using tables or software for probabilities

Module 6: Sampling and Surveys

• Types of sampling methods: random, stratified, cluster

• Bias and sampling errors

• Designing effective surveys and experiments

Module 7: Inferential Statistics

• Introduction to estimation and confidence intervals

• Hypothesis testing: null and alternative hypotheses

• p-values and significance levels

• One-sample z-tests and t-tests

Module 8: Correlation and Regression

• Scatterplots and correlation coefficients

• Simple linear regression

• Using trendlines and interpreting slope

• Predicting outcomes using linear models