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Basic Statistics
  • Level Foundation
  • المدة 27 ساعات hours
  • الطبع بواسطة University of Amsterdam
  • Offered by

عن

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.

الوحدات

Course introduction

    • 1 Videos
    • 3 Readings
Show info about module content

1 Videos

  • Welcome to Basic Statistics!

3 Readings

  • Hi there!
  • How to navigate this course
  • How to contribute

What to expect from this course

    • 8 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Use of your data for research

8 Readings

  • 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

Data and visualisation

    • 3 Videos
    • 1 Readings
Show info about module content

3 Videos

  • 1.01 Cases, variables and levels of measurement
  • 1.02 Data matrix and frequency table
  • 1.03 Graphs and shapes of distributions

1 Readings

  • Data and visualisation

Measures of central tendency and dispersion

    • 3 Videos
    • 1 Readings
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3 Videos

  • 1.04 Mode, median and mean
  • 1.05 Range, interquartile range and box plot
  • 1.06 Variance and standard deviation

1 Readings

  • Measures of central tendency and dispersion

Z-scores and example

    • 2 Videos
    • 1 Readings
Show info about module content

2 Videos

  • 1.07 Z-scores
  • 1.08 Example

1 Readings

  • Z-scores and example

Review

    • 2 Readings
    • 3 ExternalTool
    • 1 Assignment
Show info about module content

1 Assignment

  • Exploring Data

3 External Tool

  • R lab - Getting started (part 1)
  • R lab - Getting started (part 2)
  • R lab - Exploring data

2 Readings

  • Transcripts - Exploring data
  • About the R labs

Correlation

    • 2 Videos
    • 1 Readings
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2 Videos

  • 2.01 Crosstabs and scatterplots
  • 2.02 Pearson's r

1 Readings

  • Correlation

Regression

    • 3 Videos
    • 2 Readings
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3 Videos

  • 2.03 Regression - Finding the line
  • 2.04 Regression - Describing the line
  • 2.05 Regression - How good is the line?

2 Readings

  • Regression
  • Reference

Caveats & examples

    • 3 Videos
    • 2 Readings
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3 Videos

  • 2.06 Correlation is not causation
  • 2.07 Example contingency table
  • 2.08 Example Pearson's r and regression

2 Readings

  • Caveats and examples
  • Reference

Review

    • 1 Readings
    • 1 ExternalTool
    • 1 Assignment
Show info about module content

1 Assignment

  • Correlation and Regression

1 External Tool

  • R lab - Correlation and Regression

1 Readings

  • Transcripts - Correlation and regression

Probability & Randomness

    • 2 Videos
    • 1 Readings
Show info about module content

2 Videos

  • 3.01 Randomness
  • 3.02 Probability

1 Readings

  • Probability & randomness

Sample space, events & tree diagrams

    • 2 Videos
    • 1 Readings
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2 Videos

  • 3.03 Sample space, event, probability of event and tree diagram
  • 3.04 Quantifying probabilities with tree diagram

1 Readings

  • Sample space, events & tree diagrams

Probability & sets

    • 3 Videos
    • 1 Readings
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3 Videos

  • 3.05 Basic set-theoretic concepts
  • 3.06 Practice with sets
  • 3.07 Union

1 Readings

  • Probability & sets

Conditional probability & Independence

    • 4 Videos
    • 1 Readings
Show info about module content

4 Videos

  • 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

1 Readings

  • Conditional probability & independence

Review

    • 1 Readings
    • 1 ExternalTool
    • 1 Assignment
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1 Assignment

  • Probability

1 External Tool

  • R lab - Probability

1 Readings

  • Transcripts - Probability

Probability distributions

    • 2 Videos
    • 1 Readings
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2 Videos

  • 4.01 Random variables and probability distributions
  • 4.02 Cumulative probability distributions

1 Readings

  • Probability distributions

Mean and variance of a random variable

    • 2 Videos
    • 1 Readings
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2 Videos

  • 4.03 The mean of a random variable
  • 4.04 Variance of a random variable

1 Readings

  • Mean and variance of a random variable

The normal distribution

    • 3 Videos
    • 1 Readings
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3 Videos

  • 4.05 Functional form of the normal distribution
  • 4.06 The normal distribution: probability calculations
  • 4.07 The standard normal distribution

1 Readings

  • The normal distribution

The binomial distribution

    • 1 Videos
    • 1 Readings
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1 Videos

  • 4.08 The binomial distribution

1 Readings

  • The binomial distribution

Review

    • 1 Readings
    • 1 ExternalTool
    • 1 Assignment
Show info about module content

1 Assignment

  • Probability distributions

1 External Tool

  • R lab - Probability distributions

1 Readings

  • Transcripts - Probability distributions

Sample and sampling

    • 2 Videos
    • 1 Readings
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2 Videos

  • 5.01 Sample and population
  • 5.02 Sampling

1 Readings

  • Sample and sampling

Sampling distribution of sample mean and central limit theorem

    • 3 Videos
    • 2 Readings
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3 Videos

  • 5.03 The sampling distribution
  • 5.04 The central limit theorem
  • 5.05 Three distributions

2 Readings

  • Sampling distribution of sample mean and central limit theorem
  • Reference

Sampling distribution of sample proportion and example

    • 2 Videos
    • 1 Readings
Show info about module content

2 Videos

  • 5.06 Sampling distribution proportion
  • 5.07 Example

1 Readings

  • Sampling distribution of sample proportion and example

Review

    • 1 Readings
    • 1 ExternalTool
    • 1 Assignment
Show info about module content

1 Assignment

  • Sampling distributions

1 External Tool

  • R lab - Sampling distributions

1 Readings

  • Transcripts - Sampling distributions

Inference and confidence interval for mean

    • 3 Videos
    • 1 Readings
Show info about module content

3 Videos

  • 6.01 Statistical inference
  • 6.02 CI for mean with known population sd
  • 6.03 CI for mean with unknown population sd

1 Readings

  • Inference and confidence interval for mean

Confidence interval for proportion and confidence levels

    • 2 Videos
    • 1 Readings
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2 Videos

  • 6.04 CI for proportion
  • 6.05 Confidence levels

1 Readings

  • Confidence interval for proportion and confidence levels

Sample size and example

    • 2 Videos
    • 1 Readings
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2 Videos

  • 6.06 Choosing the sample size
  • 6.07 Example

1 Readings

  • Sample size and example

Review

    • 1 Readings
    • 1 ExternalTool
    • 1 Assignment
Show info about module content

1 Assignment

  • Confidence intervals

1 External Tool

  • R lab - Confidence intervals

1 Readings

  • Transcripts - Confidence intervals

Hypotheses and significance tests

    • 3 Videos
    • 1 Readings
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3 Videos

  • 7.01 Hypotheses
  • 7.02 Test about proportion
  • 7.03 Test about mean

1 Readings

  • Hypotheses and significance tests

Step-by-step plan and confidence interval

    • 2 Videos
    • 1 Readings
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2 Videos

  • 7.04 Step-by-step plan
  • 7.05 Significance test and confidence interval

1 Readings

  • Step-by-step plan and confidence interval

Type I and Type II errors and example

    • 2 Videos
    • 1 Readings
Show info about module content

2 Videos

  • 7.06 Type I and Type II errors
  • 7.07 Example

1 Readings

  • Type I and Type II errors and example

Review

    • 1 Readings
    • 1 ExternalTool
    • 1 Assignment
Show info about module content

1 Assignment

  • Significance tests

1 External Tool

  • R lab - Significance tests

1 Readings

  • Transcripts - Significance tests

Final exam

    • 1 Assignment
Show info about module content

1 Assignment

  • Final Exam

Auto Summary

Discover the essentials of statistics in this foundational course designed for Data Science & AI enthusiasts. Led by expert instructors on Coursera, you'll explore descriptive statistics, probability basics, and introductory inferential statistics over 1620 minutes. Ideal for beginners, the course includes practical training with free statistical software. Subscription options include Starter, Professional, and Paid tiers. Perfect for those looking to understand and evaluate statistical research in social and behavioral sciences.

Matthijs Rooduijn
Instructors

Matthijs Rooduijn

Emiel van Loon
Instructors

Emiel van Loon