Quantitative Techniques
Learn statistical concepts and their application, including data analysis, probability trees, decision making, and making valid inferences.
Modules/Weeks
Weekly Effort
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Course Description
- Understand statistical concepts and apply them through valid inference-making, data exploration, and measurement analysis.
- Learn about probability trees, the law of large numbers, and decision-making processes.
- Master the logic of hypothesis testing, constructing confidence intervals, and accounting for uncertainty in statistical inferences.
- Develop analytical and critical thinking skills to conduct successful data analyses and make informed decisions in real-world scenarios.
What You Will Learn
By the end of this course, learners will be able to:
Develop a comprehensive understanding of statistical concepts and their practical application.
Make valid inferences about a population based on both random and non-random samples.
Explore data effectively to gain insights about the world and draw meaningful conclusions.
Master measurement techniques and linear regression analysis for data interpretation and prediction.
Acquire knowledge of probability trees, the law of large numbers, and decision making processes.
Grasp the logic behind hypothesis testing, construct confidence intervals, and appreciate the significance of accounting for uncertainty in statistical inferences.
Apply their knowledge to conduct robust data analyses that generate accurate and reliable insights for informed decision making.
Course Outline
Module 1: Sampling and Adjustment
Module 2: Learning from Data; Exploratory Data Analysis
Module 3: Measurement
Module 4: Introduction to Linear Regression
Module 5: Understanding Linear Regression
Module 6: Multiple Regression
Module 7: Causal Identification
Module 8: Uncertainty and the Scientific Process
Module 9: Probability Trees
Module 10: Law of Large Numbers
Module 11: Decision Making
Module 12: Putting it all Together
Instructors
Andrew Gelman, a professor of statistics and political science at Columbia University, has received several awards and is a renowned author in the field of statistics. His books cover a wide range of topics from Bayesian Data Analysis to Regression and Other Stories. He has conducted extensive research in fields such as the effects of incumbency and redistricting, police stops in New York City, and medical imaging. Gelman is recognized for his contribution to statistical analysis in social sciences, specifically on the topics of probability and estimation of small effects. He is known for his work on campaign polls, social network structures, and statistical challenges in surveys, experimental design, inference, computation, and graphics.
Please note that there are no instructors or course assistants actively monitoring this course.