Chapter 1: Overview of Statistics

Question 1

Statistics (plural)

Answer 1

The science of collecting, organizing, interpreting, and presenting data

Question 2

Data science

Answer 2

A trilogy of taste involving data modeling, analysis and decision making. Some experts prefer to call statistics data science

Question 3

A Statistic (singular)

Answer 3

A single measure, reported as a number, used to summarize a data set

Question 4

Descriptive statistics

Answer 4

Refers to the collection, organization, presentation, and summary of data (either using charts and graphs or using a numerical summary)

Question 5

Inferential statistics

Answer 5

Refers to generalizing from a sample to a population, estimating unknown population parameters, drawing conclusions and making decisions

Question 6

Business analytics

Answer 6

Uses statistics, mathematics, and computational tools to extract information from data.

Three categories-
Descriptive
Predictive
Prescriptive

Question 7

Descriptive analytics

Answer 7

Tools to analyze historical data and help them identify trends and patterns (what happened?)

Question 8

Predictive analytics

Answer 8

Tools to predict probabilities of future events and help them forecast future behavior (what is likely to happen next?)

Question 9

Prescriptive analytics

Answer 9

Tools to help them make decisions on how to achieve objectives within real world constraints (what actions do we need to achieve our goals?)

Question 10

Machine learning

Answer 10

(ML) Refers to using observed data and algorithms to train computers to classify events and predict outcomes in a useful way without task specific rules

Question 11

Artificial intelligence

Answer 11

(AI) Refers to an area of computer science that seeks to create intelligent machines that can think and behave like humans to solve problems and act autonomously

Emulations of human capabilities

Question 12

Artificial neural networks

Answer 12

(ANN) Or neural nets are a key component of ML and AI. These are “black boxes” whose internal connections mimic the human brain, learning to process raw inputs and produce outputs or conclusions based on examples that are provided

Question 13

Critical thinking

Answer 13

Being able to evaluate evidence, to tell fact from opinion, to see holes in an argument, to tell whether cause and effect has been established and to spot illogic

Question 14

Empirical data

Answer 14

Data collected through observations and experiments

Question 15

Post hoc fallacy

Answer 15

The mistaken conclusion that of A precedes B, then A is the cause of B

Assuming causation anytime there is a statistical association between events

Question 16

Statistical generalizations

Answer 16

“Men are taller on average, yet many women are taller than men”

Instead of using generalized statements of men are taller than women, see how much of the overlap of population is being considered

Question 17

Statistical challenges

Answer 17

Imperfect data, practical constraints, and ethical dilemmas

Question 18

Statistical pitfalls

Answer 18

Non-random samples, incorrect sample size, and lack of causal links