Understanding statistics is essential to understand research in the social and behavioral sciences. In this course you will learn the basics of statistics; not just how to calculate them, but also how to evaluate them. This course will also prepare you for the next course in the specialization - the course Inferential Statistics.

This course is part of the Methods and Statistics in Social Sciences Specialization

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## About this Course

## Skills you will gain

- Statistics
- Confidence Interval
- Statistical Hypothesis Testing
- R Programming

## Offered by

### University of Amsterdam

A modern university with a rich history, the University of Amsterdam

## Syllabus - What you will learn from this course

**2 hours to complete**

## Before we get started...

In this module we'll consider the basics of statistics. But before we start, we'll give you a broad sense of what the course is about and how it's organized. Are you new to Coursera or still deciding whether this is the course for you? Then make sure to check out the 'Course introduction' and 'What to expect from this course' sections below, so you'll have the essential information you need to decide and to do well in this course! If you have any questions about the course format, deadlines or grading, you'll probably find the answers here. Are you a Coursera veteran and ready to get started? Then you might want to skip ahead to the first course topic: 'Exploring data'. You can always check the general information later. Veterans and newbies alike: Don't forget to introduce yourself in the 'meet and greet' forum!

**2 hours to complete**

**5 hours to complete**

## Exploring Data

In this first module, weâ€™ll introduce the basic concepts of descriptive statistics. Weâ€™ll talk about cases and variables, and weâ€™ll explain how you can order them in a so-called data matrix. Weâ€™ll discuss various levels of measurement and weâ€™ll show you how you can present your data by means of tables and graphs. Weâ€™ll also introduce measures of central tendency (like mode, median and mean) and dispersion (like range, interquartile range, variance and standard deviation). Weâ€™ll not only tell you how to interpret them; weâ€™ll also explain how you can compute them. Finally, weâ€™ll tell you more about z-scores. In this module weâ€™ll only discuss situations in which we analyze one single variable. This is what we call univariate analysis. In the next module we will also introduce studies in which more variables are involved.

**5 hours to complete**

**3 hours to complete**

## Correlation and Regression

In this second module weâ€™ll look at bivariate analyses: studies with two variables. First weâ€™ll introduce the concept of correlation. Weâ€™ll investigate contingency tables (when it comes to categorical variables) and scatterplots (regarding quantitative variables). Weâ€™ll also learn how to understand and compute one of the most frequently used measures of correlation: Pearson's r. In the next part of the module weâ€™ll introduce the method of OLS regression analysis. Weâ€™ll explain how you (or the computer) can find the regression line and how you can describe this line by means of an equation. Weâ€™ll show you that you can assess how well the regression line fits your data by means of the so-called r-squared. We conclude the module with a discussion of why you should always be very careful when interpreting the results of a regression analysis.

**3 hours to complete**

**3 hours to complete**

## Probability

This module introduces concepts from probability theory and the rules for calculating with probabilities. This is not only useful for answering various kinds of applied statistical questions but also to understand the statistical analyses that will be introduced in subsequent modules. We start by describing randomness, and explain how random events surround us. Next, we provide an intuitive definition of probability through an example and relate this to the concepts of events, sample space and random trials. A graphical tool to understand these concepts is introduced here as well, the tree-diagram.Thereafter a number of concepts from set theory are explained and related to probability calculations. Here the relation is made to tree-diagrams again, as well as contingency tables. We end with a lesson where conditional probabilities, independence and Bayes rule are explained. All in all, this is quite a theoretical module on a topic that is not always easy to grasp. That's why we have included as many intuitive examples as possible.

**3 hours to complete**

**3 hours to complete**

## Probability Distributions

Probability distributions form the core of many statistical calculations. They are used as mathematical models to represent some random phenomenon and subsequently answer statistical questions about that phenomenon. This module starts by explaining the basic properties of a probability distribution, highlighting how it quantifies a random variable and also pointing out how it differs between discrete and continuous random variables. Subsequently the cumulative probability distribution is introduced and its properties and usage are explained as well. In a next lecture it is shown how a random variable with its associated probability distribution can be characterized by statistics like a mean and variance, just like observational data. The effects of changing random variables by multiplication or addition on these statistics are explained as well.The lecture thereafter introduces the normal distribution, starting by explaining its functional form and some general properties. Next, the basic usage of the normal distribution to calculate probabilities is explained. And in a final lecture the binomial distribution, an important probability distribution for discrete data, is introduced and further explained. By the end of this module you have covered quite some ground and have a solid basis to answer the most frequently encountered statistical questions. Importantly, the fundamental knowledge about probability distributions that is presented here will also provide a solid basis to learn about inferential statistics in the next modules.

**3 hours to complete**

## Reviews

- 5 stars74.20%
- 4 stars19.11%
- 3 stars4.25%
- 2 stars1.01%
- 1 star1.41%

### TOP REVIEWS FROM BASIC STATISTICS

This is a nice course...thanks for providing such a great content from University of Amserdam.

Please allow us to complete the course as I have to wait till the session starts for week 2 lessions.

Essential to get started with statistics and/or machine learning. Explains basics is very easy way.

It would have been amazing to have examples and exercise in python languages as well.

took the free option. It has been good so far, learning a ton. I find the videos move quickly and after watching them I had to re-read the material, overall i would recommend this course.

Only the firs week of this course, but I can already tell that it's going to be incredibly useful to me. I've learned a lot and especially love the introduction to R through datacamp!

## About the Methods and Statistics in Social Sciences Specialization

Identify interesting questions, analyze data sets, and correctly interpret results to make solid, evidence-based decisions.

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