Data Science
27
Statistics
Home
1
Introduction
2
Schedule
R Programming
3
Installation
4
Introduction to R
5
Getting Started
6
IDEs
7
R packages
8
Basic operations
9
Data Types & Vectors
10
Data Structures
11
Indexing
12
Factors
13
Base Data I/O
14
Read and Write Excel Data
15
Vectorized Operations
16
Control Flow
17
Summarizing Data
18
Aggregate
19
Writing Functions
20
Function Scoping
21
The Apply Family
22
Base Graphics
Data Science
23
Terminal
24
Version Control with Git and GitHub
25
Demo Dataset
26
Probability
27
Statistics
28
Hypothesis Testing
29
Data Visualization
30
Introduction to Machine Learning
31
Clustering
32
Decomposition and Dimensionality Reduction
33
Supervised Learning
34
AI Assistants for Data Science
35
References
Table of contents
27.1
Learning objectives
27.2
Populations and samples
27.3
Descriptive statistics
27.4
Parameters and estimates
27.5
Sampling variability and standard errors
27.6
Confidence intervals
27.7
Study design and interpretation
27.8
Worked example
27.9
Hands-on practice
27.10
Checkpoint
27.11
Further study
Data Science
27
Statistics
27
Statistics
27.1
Learning objectives
Distinguish a population, a sample, a parameter, and an estimate.
Summarize a sample and describe uncertainty in an estimate.
Recognize how study design affects interpretation.
27.2
Populations and samples
27.3
Descriptive statistics
27.4
Parameters and estimates
27.5
Sampling variability and standard errors
27.6
Confidence intervals
27.7
Study design and interpretation
27.8
Worked example
27.9
Hands-on practice
27.10
Checkpoint
27.11
Further study
26
Probability
28
Hypothesis Testing