# About The Data Analysis and Statistical Inference Online Course

This Data Analysis and Statistical Inference course introduces you to the discipline of statistics analysis and inference 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 statistical inferences and conclusions about real world phenomena.

The goals of this Data Analysis and Statistical Inference 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.
##### -Introduction to data

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

• Part 1 – Defining probability •
• Part 2 – Conditional probability •
• Part 3 – Normal distribution •
• Part 4 – Binomial distribution
##### – 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
##### -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)
##### 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)
##### 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
##### Multiple linear regression

• Part 1 – Regression with multiple predictors •
• Part 2 – Inference for multiple linear regression •
• Part 3 – Model selection •
• Part 4 – Model diagnostics •
• Bayesian vs. frequentist inference

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