Data Analysis and Statistical Inference

The Coursera course, Data Analysis and Statistical Inference has been revised and is now offered as part of Coursera Specialization “Statistics with R”. This Specialization consists of 4 courses and a capstone project. The courses can be taken separately:

• Introduction to Probability and Data (began in April 2016)
  
• Inferential Statistics (begins in May 2016)
  
• Linear Regression and Modeling (begins in June 2016)
  
• Bayesian Statistics (begins in July 2016) A completely new course, with additional faculty!
  
• Statistics Capstone Project (August 2016) (for learners who have passed the 4 previous courses, and earned certificate)

You may enroll in a single course, or all of them, but each requires the knowledge and techniques from the previous courses. The assignments in these courses have suggested but not required deadlines, so you can work at your own schedule. Please check the Specialization page for other answers to your questions, and peek at the first course. We hope to see you in our new courses. The Statistics with R team.
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The goals of this course are as follows:

1. Recognize the importance of data collection, identify limitations in data collection methods, and determine how they affect the scope of inference.
2. Use statistical software (R) to summarize data numerically and visually, and to perform data analysis.
3. Have a conceptual understanding of the unified nature of statistical inference.
4. 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.
5. Model and investigate relationships between two or more variables within a regression framework.
6. Interpret results correctly, effectively, and in context without relying on statistical jargon.
7. Critique data-based claims and evaluate data-based decisions.
8. Complete a research project that employs simple statistical inference and modeling techniques.

Week 1: Unit 1 - Introduction to data

• Part 1 – Designing studies
• Part 2 – Exploratory data analysis
• Part 3 – Introduction to inference via simulation

Week 2: Unit 2 - Probability and distributions

• Part 1 – Defining probability
• Part 2 – Conditional probability
• Part 3 – Normal distribution
• Part 4 – Binomial distribution

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 – t-inference
  
• Part 2 – Power
• Part 3 – Comparing three or more means (ANOVA)
• Part 4 – Simulation based inference for means

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 – Outliers in linear regression
• Part 4 – Inference for linear regression

Week 8: Unit 7 - Multiple linear regression

• Part 1 – Regression with multiple predictors
• Part 2 – Inference for multiple linear regression
• Part 3 – Model selection
• Part 4 – Model diagnostics

Week 9: Review / catch-up week

• Bayesian vs. frequentist inference

Week 10: Final exam