Unit 1 stats Vicab

Characteristic change from one individual to another

Variable

Category + quantitive variable

Two type variables

Example of variable

People places things

Takes values that are category names/group labels

Categorical variable

Example of categorical variable

Age group

Always answer with a word + words describes individual

Categorical variable

Always answer with number w/average

Quantitive variable

Take numerical values for a measured/counted quality

Quantitive variable

Example of quantitive variable

Age of building

How much did you count in each category?

Frequency table

How to get proportion/relative frequency from frequency table

Number divided by total

Gives a proportional of cases falling into each category

Relative frequency table

What turns frequency from frequency table into percent

Relative frequency table

Are All the examples the same proportion

50% .50 ½ 5/10 50/100

Yes, same info just different format

Only shows relative frequency cannot show a frequency table

Pi chart

Display frequencies or relative frequencies for categorical variable only

Bar chart/Graph

When comparing two data sets, you should always compare what

Proportions

Two types of quantitive variable

Continuous + discrete quantitive

Take on accountable numbers of values

Discrete variable

Counting the number of wins a soccer team gets is an example of

Discrete variable

Many values can’t be counted. Between two numbers, Create an interval

Continuous random

A students height can be from 65 through 66, includes 65.6 etc. example of

Continuous random

Shows what values the variable took on plus how often it took on those values

Data distribution

What graph is best for discrete variables?

Dot plots

Using numbers as dots, numbers must be in order

Stem + leaf plot

Works best for continuous variables

Histogram

What creates bins that data falls into for histograms

Horizontal axis

True or false histograms don’t give you a lot of info only enough to see distribution

True

True or false the bins must be equal in size for histograms

True

Minutes seconds nanoseconds yato seconds are an example of

Continuous variable

4 key features of distribution

Shape, center, variability (spread), outliners

Distribution skewed right (positive) right tail longer than left

Skewed right

Distribution skewed left (Negative) left tail longer than right

Skewed left

Distribution is symmetric, Left half mirrors right

Symmetric

1 peak

Unimodel

2 peaks, must be separate

Bimodel

Same number of individuals in every single bin, no massive tail, not always perfect

Uniform

Looking for one value that best describes data

Center

At or below it

Percentile

75 percentile

Q3

25 percentile

Q1