# Introduction to Probability and Data with R

en

80 时

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• 10 序列
• 等级 介绍

## 课程详情

### 教学大纲

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

### 讲师

• Mine Çetinkaya-Rundel - Department of Statistical Science

### 平台

Coursera是一家数字公司，提供由位于加利福尼亚州山景城的计算机教师Andrew Ng和达芙妮科勒斯坦福大学创建的大型开放式在线课程。

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