# Statistics

## Free Course

The Science of Decisions

## Introduction to Programming

• ###### Estimated time

Approx. 4 Months

Beginner

• ###### Prerequisites
See prerequisites in detail
###### In collaboration with
• NOTE: This course has been divided into two courses: Descriptive and Inferential Statistics. If you are new to statistics, we recommend taking these courses instead.

We live in a time of unprecedented access to information...data. Whether researching the best school, job, or relationship, the Internet has thrown open the doors to vast pools of data. Statistics are simply objective and systematic methods for describing and interpreting information so that you may make the most informed decisions about life.

## What you will learn

1. ##### Introduction to Statistics and Methods
• Intro to statistical research methods
• Frequency Distributions & Visualizing data
2. ##### Describing Data
• Central Tendency
• Variability
3. ##### Normal Distribution Analysis
• Standardized Scores (z-scores)
• Probability and the Normal Distribution
• Sampling Distributions
4. ##### Foundations of Inferential Statistics
• Estimation
• Hypothesis Testing
5. ##### Comparing Means
• t-tests
• One-way ANOVA
6. ##### Correlation, Regression, and Non-Parametrics
• Correlation
• Regression
• Chi-Squared Tests

## Prerequisites and requirements

It sounds strange to say, but math is not the focus of this class. To do well, however, it is necessary to have a basic understanding of proportions (fractions, decimals, and percentages), negative numbers, basic algebra (solving equations), and exponents and square roots.

See the Technology Requirements for using Udacity.

## Why take this course?

• The applications of statistics to everyday life
• Methods for acquiring data through observation and experimentation
• To organize and describe quantitative and categorical forms of data
• Anticipating patterns using basic probability and sampling
• Statistical inference through estimation and hypothesis testing
• Correlation and simple regression
• Ways of describing the strength of relationships between variables

## Learn with the best.

• ##### Sean Laraway

Instructor

• ##### Ronald Rogers

Instructor

• Instructor