EdX

Introductory Statistics : Basic Ideas and Instruments for Statistical Inference (edX)

Introductory Statistics : Basic Ideas and Instruments for Statistical Inference (edX)

This course utilizes real-life applications of Statistics in an exploration of the Statistical Inferenceprocess. Statistical Inference is the process by which data is used to draw a conclusionoruncover ascientific truthabout a population from asample. This course aims to familiarize the student with several ideas and instruments for statistical inference. It covers fundamental concepts and properties of probability.

Class Deals by MOOC List - Click here and see EdX's Active Discounts, Deals, and Promo Codes.

Specifically, youwill learn to work with sequences of successes and failures using the binomial formula. You will also learn how the law of averages can be used, as well as how to understand stochastic processes by applying box models. The normal approximation for probability histogram, which is an essential idea for statistical inference, will be discussed in depth in this course.

What you'll learn

  • Probability
  • The Binomial Formula
  • The Law of Averages
  • The Expected Value and Standard Error
  • The Normal Approximation for Probability Histograms

Syllabus

Week 1

  • Probability
  • Examples of Probability Calculation

Week 2

  • The Binomial Formula
  • The Law of Averages
  • The Expected Value and Standard Error

Week 3

  • The Normal Approximation for Probability Histograms
Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

R Data Science Capstone Project (edX) EdX
IBM

R Data Science Capstone Project (edX)

Apply various data analysis and visualization skills and techniques you have learned by taking on the role of a data scientist working with real-world data sets. In this capstone course, you will apply various data science skills and techniques that you have learned as part of the previous courses in the IBM Data Science with R or IBM Data Analytics with Excel and R Professional Certificate Programs.

Self Paced
Self-Paced
Probability and Statistics IV: Confidence Intervals and Hypothesis Tests (edX) EdX
Georgia Institute of Technology,GTx

Probability and Statistics IV: Confidence Intervals and Hypothesis Tests (edX)

This course covers two important methodologies in statistics – confidence intervals and hypothesis testing. Confidence intervals allow us to make probabilistic statements such as: “We are 95% sure that Candidate Smith’s popularity is 52% +/- 3%.” Hypothesis testing allows us to pose hypotheses and test their validity in a statistically rigorous way. For instance, “Does a new drug result in a higher cure rate than the old drug”?

Self Paced
Self-Paced
Data Science: Probability (edX) EdX
HarvardX,Harvard University

Data Science: Probability (edX)

Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008. In this course, you will learn valuable concepts in probability theory. The motivation for this course is the circumstances surrounding the financial crisis of 2007–2008. Part of what caused this financial crisis was that the risk of some securities sold by financial institutions was underestimated. To begin to understand this very complicated event, we need to understand the basics of probability.

Self Paced
Self-Paced
Probability Theory (edX) EdX
Delft University of Technology,DelftX

Probability Theory (edX)

This course provides an introduction to probability theory. You will encounter discrete and continuous random variables and learn in which situations they appear, what their properties are and how they interact. A strong foundation in mathematics is critical for success in all science and engineering disciplines. Whether you want to make a strong start to a master’s degree, prepare for more advanced courses, solidify your knowledge in a professional context or simply brush up on fundamentals, this course will get you up to speed.

Self Paced
Self-Paced
Advanced statistical physics (edX) EdX
École Polytechnique Fédérale de Lausanne,EPFLx

Advanced statistical physics (edX)

We explore statistical physics in both classical and open quantum systems. Additionally, we will cover probabilistic data analysis that is extremely useful in many applications. This course covers non-equilibrium statistical processes and the treatment of fluctuation dissipation relations by Einstein, Boltzmann and Kubo. Moreover, the fundamentals of Markov processes, stochastic differential and Fokker Planck equations, mesoscopic master equation, etc will be treated in detail. Prior knowledge of statistical physics is highly recommended but not required.

Self Paced
Self-Paced
Public Sector Debt Statistics (edX) EdX
International Monetary Fund - IMF,IMFx

Public Sector Debt Statistics (edX)

The course examines coverage and accounting rules for public sector debt, valuation, classification, important methodological issues, and the sources and methods used for compiling the statistics. This course, presented by IMF Statistics Department, covers the fundamentals needed to compile and disseminate comprehensive public sector debt statistics (PSDS) that are useful for policy- and decision-makers, as well as other users.

Self Paced
Self-Paced
Introduction to Probability (edX) EdX
HarvardX,Harvard University

Introduction to Probability (edX)

Learn probability, an essential language and set of tools for understanding data, randomness, and uncertainty. Probability and statistics help to bring logic to a world replete with randomness and uncertainty. This course will give you tools needed to understand data, science, philosophy, engineering, economics, and finance.

Self Paced
Self-Paced
Probability and Statistics in Data Science using Python (edX) EdX
University of California, San Diego,UC San DiegoX

Probability and Statistics in Data Science using Python (edX)

Using Python, learn statistical and probabilistic approaches to understand and gain insights from data. The job of a data scientist is to glean knowledge from complex and noisy datasets. Reasoning about uncertainty is inherent in the analysis of noisy data. Probability and Statistics provide the mathematical foundation for such reasoning.

Self Paced
Self-Paced