# UVA Amsterdam - Basic Statistics

- Offered byCoursera

## Basic Statistics at Coursera Overview

Duration | 27 hours |

Start from | Start Now |

Total fee | Free |

Mode of learning | Online |

Difficulty level | Beginner |

Official Website | Explore Free Course |

Credential | Certificate |

## Basic Statistics at Coursera Highlights

- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 3 of 5 in the Methods and Statistics in Social Sciences Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Beginner Level
- Approx. 27 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

## Basic Statistics at Coursera Course details

- 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.
- In the first part of the course we will discuss methods of descriptive statistics. You will learn what cases and variables are and how you can compute measures of central tendency (mean, median and mode) and dispersion (standard deviation and variance). Next, we discuss how to assess relationships between variables, and we introduce the concepts correlation and regression.
- The second part of the course is concerned with the basics of probability: calculating probabilities, probability distributions and sampling distributions. You need to know about these things in order to understand how inferential statistics work.
- The third part of the course consists of an introduction to methods of inferential statistics - methods that help us decide whether the patterns we see in our data are strong enough to draw conclusions about the underlying population we are interested in. We will discuss confidence intervals and significance tests.
- You will not only learn about all these statistical concepts, you will also be trained to calculate and generate these statistics yourself using freely available statistical software.

## Basic Statistics at Coursera Curriculum

**Before we get started...**

Welcome to Basic Statistics!

Hi there!

How to navigate this course

How to contribute

General info - What will I learn in this course?

Course format - How is this course structured?

Requirements - What resources do I need?

Grading - How do I pass this course?

Team - Who created this course?

Honor Code - Integrity in this course

Useful literature and documents

Research on Feedback

Use of your data for research

1.01 Cases, variables and levels of measurement

1.02 Data matrix and frequency table

1.03 Graphs and shapes of distributions

1.04 Mode, median and mean

1.05 Range, interquartile range and box plot

1.06 Variance and standard deviation

1.07 Z-scores

1.08 Example

Data and visualisation

Measures of central tendency and dispersion

Z-scores and example

Transcripts - Exploring data

About the R labs

Exploring Data

**Correlation and Regression**

2.01 Crosstabs and scatterplots

2.02 Pearson's r

2.03 Regression - Finding the line

2.04 Regression - Describing the line

2.05 Regression - How good is the line?

2.06 Correlation is not causation

2.07 Example contingency table

2.08 Example Pearson's r and regression

Correlation

Regression

Reference

Caveats and examples

Reference

Transcripts - Correlation and regression

Correlation and Regression

**Probability**

3.01 Randomness

3.02 Probability

3.03 Sample space, event, probability of event and tree diagram

3.04 Quantifying probabilities with tree diagram

3.05 Basic set-theoretic concepts

3.06 Practice with sets

3.07 Union

3.08 Joint and marginal probabilities

3.09 Conditional probability

3.10 Independence between random events

3.11 More conditional probability, decision trees and Bayes' Law

Probability & randomness

Sample space, events & tree diagrams

Probability & sets

Conditional probability & independence

Transcripts - Probability

Probability

**Probability Distributions**

4.01 Random variables and probability distributions

4.02 Cumulative probability distributions

4.03 The mean of a random variable

4.04 Variance of a random variable

4.05 Functional form of the normal distribution

4.06 The normal distribution: probability calculations

4.07 The standard normal distribution

4.08 The binomial distribution

Probability distributions

Mean and variance of a random variable

The normal distribution

The binomial distribution

Transcripts - Probability distributions

Probability distributions

**Sampling Distributions**

5.01 Sample and population

5.02 Sampling

5.03 The sampling distribution

5.04 The central limit theorem

5.05 Three distributions

5.06 Sampling distribution proportion

5.07 Example

Sample and sampling

Sampling distribution of sample mean and central limit theorem

Reference

Sampling distribution of sample proportion and example

Transcripts - Sampling distributions

Sampling distributions

**Confidence Intervals**

6.01 Statistical inference

6.02 CI for mean with known population sd

6.03 CI for mean with unknown population sd

6.04 CI for proportion

6.05 Confidence levels

6.06 Choosing the sample size

6.07 Example

Inference and confidence interval for mean

Confidence interval for proportion and confidence levels

Sample size and example

Transcripts - Confidence intervals

Confidence intervals

**Significance Tests**

7.01 Hypotheses

7.02 Test about proportion

7.03 Test about mean

7.04 Step-by-step plan

7.05 Significance test and confidence interval

7.06 Type I and Type II errors

7.07 Example

Hypotheses and significance tests

Step-by-step plan and confidence interval

Type I and Type II errors and example

Transcripts - Significance tests

Significance tests

**Exam time!**

Final Exam

## Basic Statistics at Coursera Admission Process

## Important Dates

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