Making decisions and predictions often requires making inferences about the population in general from the limited set of data you have observed.
This process is often called inductive reasoning, which relies on the statistical process of drawing generalized conclusions based on an empirical sample.
We will explore and practice this inferential statistics process in the business context. We will explore topics such as:
Class Data Demos on Google Sheets
Complete assigned readings before next class meeting, which are yellow-highlighted
W2-1 Data Visualization: PPT
W2-1 Data Visualization: PPT
- NFF Chapter 7: Estimating unknown quantities from a sample (pdf)
- DA Worksheet #04: Visualization (Solution; Python Solution)
- Khan Academy module on Study Design (including Sampling)
- Khan Academy module on Central Limit Theorem
- Khan Academy module on Confidence Interval
- [MINI] Confidence Intervals by Data Skeptic podcast
- [MINI] Sample Sizes by Data Skeptic podcast
- [MINI] Selection Bias by Data Skeptic podcast
- [MINI] Overdispersion by Data Skeptic podcast
- NFF Chapter 8: Hypothesis testing (pdf)
- DA Worksheet #05B: Team Project (Solution)
- How logarithms work
- What is e: Euler's number
- One Sample T-Test on JASP
- Khan Academy module on Hypothesis Testing
- Khan Academy module on Z vs. T statistics
- NFF Chapter 11: Correlation and linear regression (pdf)
- DA Worksheet #05: Distributions (Solution)
- [MINI] p-values by Data Skeptic podcast
- P Values by StatQuest
- Defending the p-value by Data Skeptic podcast
- Fooled by Statistical Significance by Cassie Kozyrkov, Chief Decision Scientist, Google
- Lady Testing Tea
- Fischer's Exact Test (Lady Testing Tea)