Statistics!

Today Check in – How is that proposal coming along…? Finish up material from Tuesday Statistics

Purpose for today and Tuesday – Familiarize you with statistical terms and concepts – Help you get a general sense of statistics What are they? Why do we use them? What are some basic statistics?

What are they Statistics are numbers that describe a sample Parameters are numbers that describe a population

What are statistics for? We use them to describe our variables – Descriptive statistics We use them to make inferences from samples to populations – Inferential statistics This is why sampling and bias are so very important

Basic descriptive statistics-frequencies Frequencies Remember: variables are divided into categories Frequencies tell us how many are in each type of category – Frequencies can refer to the raw number, or the percent

Frequency--example ScoresFrequency (rawPercent 1330% % % 4 4 4

Types of variables Nominal Ordinal Interval Ratio

Nominal “named” variables Can be represented with numbers but have no numerical qualities – There is no rank order E.g. Red, blue, green cars Male/female gender

Nominal blue red green

Ordinal Variables that have “order” We assign them a rank, and may use numbers We don’t actually know how much the ranks differ E.g. bad, worse, worst Some of the time, most of the time, all of the time

Ordinal 1 2 3

We should not manipulate ordinal variables numerically – Add, subtract, multiply Because we don’t know if the categories are exact But in practice ordinal variables are numerically manipulated all the time

Interval Interval data is rank ordered We know that the space from one to the next is “equal” E.g. temperature But interval data has “no true zero” – There can’t be a true absence of the thing being measured Like temperature, zero is “arbitrary” We decide what zero is

Interval Even more less than 0 Less than 0 “0”1234 “heat”

Ratio Data Like interval data It is ordered We know that the space from one rating to the next is “equal” It has a “true zero” There CAN be an absence of it E.g. length, weight – You can have “zero” weight

Ratio “Weight”

Useful terms Univariate—referring to a single variable Bivariate—two variables Multivariate—more than two variables Proportion—a percent