# Fundamentals of Statistics

## Philippe Rigollet, Jan-Christian Hütter, Karene Chu, MITx

Develop a deep understanding of the principles that underpin statistical inference: estimation, hypothesis testing and prediction. -- Course 3 of 4 in the MITx MicroMasters program in Statistics and Data Science.

Statistics is the science of turning data into insights and ultimately decisions. Behind recent advances in machine learning, data science and artificial intelligence are fundamental statistical principles. The purpose of this class is to develop and understand these core ideas on firm mathematical grounds starting from the construction of estimators and tests, as well as an analysis of their asymptotic performance.

After developing basic tools to handle parametric models, we will explore how to answer more advanced questions, such as the following:

• How suitable is a given model for a particular dataset?
• How to select variables in linear regression?
• How to model nonlinear phenomena?
• How to visualize high-dimensional data?

Taking this class will allow you to expand your statistical knowledge to not only include a list of methods, but also the mathematical principles that link them together, equipping you with the tools you need to develop new ones.

This course is part of theMITx MicroMasters Program in Statistics and Data Science. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit https://micromasters.mit.edu/ds/.

### What will you learn

• Construct estimators using method of moments and maximum likelihood, and decide how to choose between them
• Quantify uncertainty using confidence intervals and hypothesis testing
• Choose between different models using goodness of fit test
• Make prediction using linear, nonlinear and generalized linear models
• Perform dimension reduction using principal component analysis (PCA)

• 2020年5月11日

• 免费:
• 收费:
• 证书:
• MOOC:
• 视频讲座:
• 音频讲座:
• Email-课程:
• 语言: 英语

### 反馈

Data Storage and Processing
Master the culture of data representation, interpretation and outcomes evaluation...
Sensor Fusion and Non-linear Filtering for Automotive Systems
Learn fundamental algorithms for sensor fusion and non-linear filtering with...
SU20: Introduction to Analytics Modeling
Learn essential analytics models and methods and how to appropriately apply...
FA20: Introduction to Analytics Modeling
Learn essential analytics models and methods and how to appropriately apply...
Manufacturing Process Control I
Learn how to model variations in manufacturing processes and develop methods...

Combinatorial Mathematics | 组合数学
Discover how to apply counting principles and combinatorics to solve problems...
How to Learn Math: For Students
How to Learn Math is a free self-paced class for learners of all levels of mathematics...
Calculus 1C: Coordinate Systems & Infinite Series
Master the calculus of curves and coordinate systems&mdash;approximate functions...
Statistical Learning
Learn some of the main tools used in statistical modeling and data science....
Visualizing Data with Python
Data visualization is the graphical representation of data in order to interactively...

Combinatorial Mathematics | 组合数学
Discover how to apply counting principles and combinatorics to solve problems...
Introduction to Mao Zedong Thought | 毛泽东思想概论
Mao Zedong founded the People's Republic of China in 1949, but who was he and...

In this introductory computer science course, explore geometry, develop geometric...
Flower Arrangements in China and Japan | 现代生活美学：花之道
Pursuing the philosophy of harmony between man and nature, covering the knowledgeof...