本帖最后由 Oleaa 于 2014-3-17 19:13 编辑 |
Duke University，软件平台用R 好开心>o<！
About the Course
The goals of this course are as follows:
- Recognize the importance of data collection, identify limitations in data collection methods, and determine how they affect the scope of inference.
- Use statistical software (R) to summarize data numerically and visually, and to perform data analysis.
- Have a conceptual understanding of the unified nature of statistical inference.
- Apply estimation and testing methods (confidence intervals and hypothesis tests) to analyze single variables and the relationship between two variables in order to understand natural phenomena and make data-based decisions.
- Model and investigate relationships between two or more variables within a regression framework.
- Interpret results correctly, effectively, and in context without relying on statistical jargon.
- Critique data-based claims and evaluate data-based decisions.
- Complete a research project that employs simple statistical inference and modeling techniques.
course website: https://class.coursera.org/statistics-001
Course SyllabusWeek 1: Unit 1 - Introduction to data
Week 2: Unit 2 - Probability and distributions
- Part 1 – Designing studies
- Part 2 – Exploratory data analysis
- Part 3 – Introduction to inference via simulation
- Week 1
- Part 1 – Defining probability
- Part 2 – Conditional probability
- Part 3 – Normal distribution
- Part 4 – Binomial distribution
- Week 2
Week 3: Unit 3 - Foundations for inference
- Part 1 – Variability in estimates and the Central Limit Theorem
- Part 2 – Confidence intervals
- Part 3 – Hypothesis tests
Week 4: Finish up Unit 3 + Midterm
- Part 4 – Inference for other estimators
- Part 5 - Decision errors, significance, and confidence
Week 5: Unit 4 - Inference for numerical variables
- Part 1 – Comparing two means
- Part 2 – Bootstrapping
- Part 3 – Inference with the t-distribution
- Part 4 – Comparing three or more means (ANOVA)
Week 6: Unit 5 - Inference for categorical variables
- Part 1 – Single proportion
- Part 2 – Comparing two proportions
- Part 3 – Inference for proportions via simulation
- Part 4 – Comparing three or more proportions (Chi-square)
Week 7: Unit 6 - Introduction to linear regression
- Part 1 – Relationship between two numerical variables
- Part 2 – Linear regression with a single predictor
- Part 3 – Inference for linear regression
Week 8: Unit 7 - Multiple linear regression
- Part 1 – Regression with multiple predictors
- Part 2 – Model selection
Week 9: Review / catch-up week
- Bayesian vs. frequentist inference
Week 10: Final exam