What is statistics?

Introduction to Statistics in Python

Maggie Matsui

Content Developer, DataCamp

What is statistics?

  • The field of statistics - the practice and study of collecting and analyzing data

  • A summary statistic - a fact about or summary of some data

Introduction to Statistics in Python

What can statistics do?

What is statistics?

  • The field of statistics - the practice and study of collecting and analyzing data

  • A summary statistic - a fact about or summary of some data

What can statistics do?

  • How likely is someone to purchase a product? Are people more likely to purchase it if they can use a different payment system?
  • How many occupants will your hotel have? How can you optimize occupancy?
  • How many sizes of jeans need to be manufactured so they can fit 95% of the population? Should the same number of each size be produced?
  • A/B tests: Which ad is more effective in getting people to purchase a product?
Introduction to Statistics in Python

What can't statistics do?

  • Why is Game of Thrones so popular?

Instead...

  • Are series with more violent scenes viewed by more people?

But...

  • Even so, this can't tell us if more violent scenes lead to more views
Introduction to Statistics in Python

Types of statistics

Descriptive statistics

  • Describe and summarize data

2 cars, 1 bus, 1 bike

  • 50% of friends drive to work
  • 25% take the bus
  • 25% bike

Inferential statistics

  • Use a sample of data to make inferences about a larger population

2 cars, 1 bus, 1 bike surrounded by more cars, buses, and bikes

What percent of people drive to work?

Introduction to Statistics in Python

Types of data

Numeric (Quantitative)

  • Continuous (Measured)
    • Airplane speed
    • Time spent waiting in line
  • Discrete (Counted)
    • Number of pets
    • Number of packages shipped

Categorical (Qualitative)

  • Nominal (Unordered)
    • Married/unmarried
    • Country of residence
  • Ordinal (Ordered)

strongly disagree/somewhat disagree/neither agree nor disagree/somewhat agree/strongly agree

Introduction to Statistics in Python

Categorical data can be represented as numbers

Nominal (Unordered)

  • Married/unmarried (1/0)
  • Country of residence (1, 2, ...)

Ordinal (Ordered)

  • Strongly disagree (1)
  • Somewhat disagree (2)
  • Neither agree nor disagree (3)
  • Somewhat agree (4)
  • Strongly agree (5)
Introduction to Statistics in Python

Why does data type matter?

Summary statistics
import numpy as np
np.mean(car_speeds['speed_mph'])

40.09062
Plots

scatterplot of car speed vs car weight

Introduction to Statistics in Python

Why does data type matter?

Summary statistics
demographics['marriage_status'].value_counts()

single      188
married     143
divorced    124
dtype: int64
Plots

bar plot of marriage status count

Introduction to Statistics in Python

Let's practice!

Introduction to Statistics in Python

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