Stats Vocabulary (Quarter 1)

One Variable Sats

Categorical Variable - A variable that can be classified into two or more categories; this variable does not have a quantity. Ex: yes/no, red/blue, made/missed, etc.

Individuals- items or subjects that are in a study

Subject- an individual that is a human

Qualitative Variable- an open response type question

Quantitative Variable- A variable that is measured based on anything that has to do with numbers; Ex: age, weight, a number scale, or even using money

Relative Frequency- the number divided by all the possible outcomes

Variable- what is being measured or used to measure (in future modules, we will see independent vs dependent variables and explanatory vs response variables)

Quantitative Distributions

Bimodal- having two peaks

Box Plot- plotting data using quartiles and outliers

Histogram- plot that summarizes how data is distributed; this kind of graph puts data into groups and graphs them like a bar graph (ex: 0-9, 10-19, 20-29, etc)

interquartile range- upper quartile mins the lower quartile

mean- the average of a set of numbers. average is when you add all the numbers together and divide by how many numbers there are

median- middle number of a set of numbers

outlier- a data point way beyond the borders of a data set

percentile- the percent at or below that score

quartile - splitting the data into top 25%, middle/upper 25%, middle/lower 25%, and lower 25%

stem plot- type of graph that separates the tens place from the ones place by a "stem" in order to organize the data. Ex: 2 I 5 = 25

standard deviation- a measure of how spread apart things are how far

symmetrical- exactly the same on both sides

trimodal- having three peaks

unimodal- having one peak

Sampling

census- a survey given to all of a population

cluster sampling- small even, and evenly mixed groups from a population that is picked by SRS and those groups will serve as the sample

non-response - the individuals that do not respond to a survey

parameter-the number part of the stats of a population, such as mean or median

population- who or what is being studied

sample- a small portion from a large population

simple random sample (SRS)- a random sample, but gives everyone an equal opportunity to be picked

strata- a “layer” of a population, can be divided because of different characteristics. Layer means that there is a group of people with the same type of characteristics for the survey, and each layer is different

stratified sample- a sample not from the populations itself but from certain strata of the population. You need to do an SRS from each strata (ex: a proportion from each grade)

systematic sample- first you estimate the population size, decide how many people you want to sample and then divide the two numbers to decide which every nth person you sample

undercoverage- no chance for the person to be surveyed, for example the person was gone the day of the survey

Confidence Intervals

bootstrapping- Used to find hypothesis tests and confidence intervals; it takes your set of data and uses it as the population and uses it to do a bunch of tests so that you get more results

Hypothesis Testing

Alternative Hypothesis- the answer that must be true if the null hypothesis is wrong

Null Hypothesis- assumed hypothesis

p- value- the probability of obtaining a statistic the same as the one that was observed, assuming that the null hypothesis is indeed true

type one error- when the null hypothesis is rejected when it is true

type two error- the null hypothesis is not rejected when it is false