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 |

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