Statistical Thinking and Data Analysis
Introduces a rigorous treatment of statistical data analysis while helping students develop a strong intuition for the strengths and limitations of various methods. Topics include statistical sampling and uncertainty, estimation, hypothesis testing, linear regression, classification, analysis of variation, and elements of data mining. Involves empirical use of hypothesis testing and other statistical methodologies in several domains, including the assessment of A-B experiments on the web and the identification of genes correlated with diseases.
Fall 2011
This course is an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and nonparametric statistics.
Prerequisites & course information
Same subject as IDS.013[J] Prereq: 6.3700 or 15.069 U (Spring) Not offered regularly; consult department 3-1-8 units. Institute LAB
Sources
- Course catalogue ↗2026–2027
- MIT OpenCourseWare ↗Fall 2011
- MIT OCW calendar ↗
- MIT OCW syllabus ↗
- Historical MIT OpenCourseWare offering. Named reading entries and schedule topics are automatically extracted from the linked source pages.