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29-01-2015, 00:04

Coursera - Data Analysis and Statistical Inference (2014) [84 mp4]

Category: Tutorials

Coursera - Data Analysis and Statistical Inference (2014) [84 mp4]

Coursera - Data Analysis and Statistical Inference (2014) [84 mp4]
English | Size: 1.28 GB (1,376,028,414 bytes )
Category: Tutorial




This course introduces you to the discipline of statistics as a science of understanding and analyzing data. You will learn how to effectively make use of data in the face of uncertainty: how to collect data, how to analyze data, and how to use data to make inferences and conclusions about real world phenomena.

Instructor: Mine Cetinkaya-Rundel
Released: Sept. 1, 2014

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
Part 4 - Inference for other estimators
Part 5 - Decision errors, significance, and confidence

Week 4: 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 5: 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 6: 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 7: 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


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