UVA Amsterdam - Basic Statistics
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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
Other courses offered by Coursera
Student Forum
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