# Intro Flashcards

What infographic do you never use

Pie charts

Statistics

Statistics is the branch of mathematics that examines ways to process and analyse data. Statistics provides procedures to collect and transform data in ways that are useful to business decision makers. To understand anything about statistics, you first need to understand the meaning of a variable.

4 fundamental terms of statistics

Population

Sample

Parameter

Statistic

Population

A population consists of all the members of a group about which you want to

draw a conclusion.

Sample

A sample is the portion of the population selected for analysis

Parameter

A parameter is a numerical measure that describes a characteristic of a

population (measures used to describe a population) GREEK LETTERS REFER

TO A PARAMETER

Statistic

A statistic is a numerical measure that describes a characteristic of a sample

(measures calculated from sample data) ROMAN LETTERS REFER TO

STATISTICS

2 types of statistics

Descriptive statistics

Inferential statistics

Descriptive statistics

Collecting, summarising and presenting data

Inferential statistics

Drawing conclusions about a population based on sample

data/results (i.e. estimating a parameter based on a statistic

3 steps of descriptive statistics

Collect data

Present data

Characterise data

Collect data example

Survey

Present data example

Tables and graphs

Characterise data example

Sample mean

Steps of inferential statistics

Estimation

Hypothesis Testing

Estimation example

Estimate the population mean weight (parameter) using the

sample mean weight (statistic)

Hypothesis testing example

Test the claim that the population mean weight is 100 kilos

4 important sources when collecting data

Data distributed by organisation or individual

Designed experiment

Survey

Observational study

2 classifications of data sources

Primary

Secondary

2 types of data

Categorical (defined categories)

Numerical (quantitative)

2 types of numerical variables

Discrete (counted items)

Continuous (measured characteristics)

Categorical data

Simply classifies data into categories (e.g. marital status, hair

colour, gender)

Numerical discrete data e.g.

Counted items – finite number of items (e.g. number of

children, number of people who have type-O blood

Numerical continuous data e.g.

Measured characteristics – infinite number of items

e.g. weight, height

4 levels of Measurement and Measurement Scales from highest to lowest

Ratio data

Interval data

Ordinal data

Nominal data

Ratio data

Differences between measurements are meaningful and a true zero

exists

Interval data

Differences between measurements are meaningful but no true zero

exists

Ordinal data

Ordered categories (rankings, order or scaling)

Nominal data

Categories (no ordering or direction)

Ratio data eg

Height, weight, age, weekly food spending

Interval data eg

Temperature in degrees Celsius, standardised exam score

Ordinal data eg

Rankings in a tennis tournament, student letter grades, Likert

scales