Chapter 1, statistics

Statistics

The science of conducting studies to collect, organize analyze, and draw conclusions from data

Descriptive statistics

Generalizing from samples to population, preforming estimation and hypothesis test, determining relationships among variables and making prediction.

Using a smaller group of people than make a statement about the population

Population

All subject, that are being studied (Human or otherwise).

Any group being studied, not just humans, but the entire group

Sample

A group of subjects selected, or taken from a population.

Representative

Represents the characteristics of the population as closely as possible

Random sample

Select from population randomly that each individual member has equal chance of selection. Example, like choosing a name from a hat

Systematic sample

Starting point and then select every kth Element in population. Example in a pattern.

Stratified Sample

Divide population into least two groups, so that they share same characteristics. Then, draw sample from each group. Example they break them in sections. Like age,Genders, grade level.

Cluster sample

The whole population first divided into geographical group called cluster, and each representative of population

Variabl

Can be taken different values from Different individuals. Any characteristics of an individual. Example numbers of pets. We have, numbers of backpacks, we have, height, weight.

Qualitative data (quantity)

Different categories are distinguished in some non-numerical characteristic. Also called categorical data.

* You don’t really measure number

Example, favorite ice cream, brand of shoes you wear, Eye color

Quantitive data (Quantity)

Consist of numbers responding counts of measurements

• you can measure you can measure

• Example, height, weight, salary

Discrete data

Countable

Values that can be counted also can be finite. Whole numbers or accountable things

Think… Would be terrible to cut in half example cars, pets, classes, pillows

Continuous data

Numerical

Things can break down infinity small. You can break down two small decimal

Example, time, temperature, weight, height

nominal

Think name…

Categories in which no order or ranking can be imposed on data.

Anything that is no ranking and no measurement. Just list of stuff.

Example, college majors, miracle steps, political party

Ordinal

Classifies data into categories that can be ranked but no number to it. No number, but there’s ranking.

Example college degree, grade levels Military ranking, corporate.

Interval

Data does not have a Natural zero starting point. Zero doesn’t mean the absence of what is being measured.

Example temperature in Celsius, altitude Or elevation sea levels is zero

Ratio

True 0. The ratio scale contains a true ratio between values.

Example, height, weight, area, number cells Received