Lecture 1.1 & 1.2

Populaiton

It is the complete collection of all subjects that are being studied.

• Ex. survey on all Mac Students

Sample

A group of subjects selected from the population

• Survey for just Mcmaster stats students

Descriptive statistics

Consists of organizing, summarizing, and presenting data. Not usually the final goal.

• Finding average, making frequency table.

Inferential statistics

Consists of using a sample to draw conclusion about a population using probability. Estimation, need probability to make educated guess.

Parameter

A measurement describing some characteristic of a population. Uses population.

• Ex. average income of a Canadian.

Statistic

Measurement describing some characteristic of a sample.

• similar to parameter but for a sample group of population

• ex. average income in a sample of Canadians

Quantitative data

Consists of number representing counts or measurements. Data you measured to get

Qualitative data

Data cannot be measured, but can only be separated into different categories.

• Data you don’t measure, instead are categories

• Ex. eye colour, letter grades, etc

Nominal level

Measurement is characterized by data that consist names, labels, or categories only, where the different categories cannot be ordered.

• ex. eye colour, gender, martial status

Ordinal level

Measurement if they can be arranged in some order, but differences between data values either cannot be determined or are meaningless.

• Ex. letter grades, ranking of universities

Interval

Level of measurement is like the ordinal level, but differences between two values is meaningful. But there is not a natural zero starting points (can be negative). E.g. temperature in C

Ratio

level of measurement is the interval level but with the additional property that there is a natural zero starting point (no negatives). Differences and ratios are both meaningful.

• Ex. height, weight, volume