Statistics Vocab Chapter 1, 2, 3
Terms
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 Quantitative Variable
 age, height, weight, temperature
 Qualitative Variable
 Characteristics gender, religious preferance, geographic location
 Discrete Variables
 Can be counted
 Continuous Variables
 can assume all values between any 2 specific values obtained by measuring
 Data<qualitative or quantitative
 Quantitative<Discrete or Continuous
 Descriptive Statistics
 collection, organization, summarization, and presentation of data: censusaverage age, income, or other characteristics of US population
 Inferential Statistics

make inferences from sample to populations: uses probability
Generalizing from samples to populations, estimations and hypothesis tests, relationships and predictions  Nominal level measurement
 mutually exclusive exhausting categories in which no order or ranking can be imposed on the data: zip code, gender, eye color, political affiliation, major field, nationality
 Ordinal level measurement
 categories that can be ranked: grades, 1st 2nd place, rating scale(poor, good), ranking of tennis players
 Interval level measurement
 ranks data  no zero: SAT score, IQ, Temperature
 Ratio level measurement

lift twice as much, height weight, time, salary, age
true zero  Random Sample
 chance methods, random numbers, table of random numbers
 Systematic Sample
 numbering then selecting everty kth subject
 Stratified Sample
 dividing poplulation into groups then selecting from each group
 Cluster Sample
 intact group representative of the population
 Quasiexperimental study
 intact groups when random assignment is not possible
 Hawthorne Effect
 Participants change behavior because they knew they were being tested
 frequency distribution
 organization of raw data in table form using classes and frequencies
 3 types of frequency distribution
 categorical and grouped and ungrouped
 class midpoint
 X= lower boundary + upper boundary /2
 Histogram
 contigious vertical bars
 Frequency Polygon
 lines between midpoints
 Ogive
 graph that represents cumulative frequencies
 Relative Frequency graph
 proportions instead of raw data
 Pareto Chart
 represent frequency distribution for a categorical variable vertical bars highest to lowest
 Pie Graph
 sections according to percentage of frequencies