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Prob & Stat

Ch 1/14.1,14.2

Statistics The science of conducting studies to collect, organize, summarize, analyze, and draw conclusions from data.
Variable A characteristic or attribute that can assume different values.
Data The values the variables assume.
Random Variables Variables whose values are determined by chance.
Data Set A collection of data values.
Data Value (Datum) Each value of the data set.
Probability The chance of an event occuring.
Population All subjects that are being studied.
Sample A group of subjects that are being studied.
Hypothesis Testing A decision-making process for evaluating claims about a population based on information from samples.
3 ways statistics is used in everyday life 1. used in fields of human endeavor-sports, public health, and education. 2. used to analyze the results of a survey. 3. Used as a tool in scientific research to make decisions based on controlled experiments.
3 reasons to study statistics 1. To be able to understand statistical studies. 2. To be able to conduct research, design experiments, make predictions and communicate results. 3. To become better consumers.
Descriptive statistics consists of the collection, organization, summarization, and presentation of data
Inferential statistics consists of generalizing from samples to populations, performing estimations and hypothesis tests, determining relationships among variables, and making predictions.
Quantitative variables numerical and can be ordered or ranked. ex. Age, height, weight
Qualitative variables variables that can be placed into distinct categories (like a characteristic or attribute) ex. gender, color, religious preferences
Discrete variables variables that assume values that can be counted ex. # of children, # of students
continuous variables variables that can assume an infinite number of values between any two specific values. They are obtained by measuring- often contain fractions.
Nominal level of measurement classifies data into mutually exclusive (non-overlapping) exhausting categories in which no order or ranking can be imposed on the data ex. gender, zip code, political party
Ordinal level of measurement classifies data into categories that can be ranked, however, precise differences between the ranks do not exist. ex. ranking guest speakers, classifying a person's build
Interval level of measurement ranks data and precise differences between units of measure do exist, however, there is no true zero. ex. IQ test, temp., SAT scores
Ratio level of measurement possesses all the characteristics of interval measurement and there exists a true zero. In addition, true ratios exist when the same variable is measured on two different numbers of the population. ex. height, weight, area, # phone calls, time, salary
2 purposes of data collection 1. To describe situations or events 2. To help people make better decisions before acting
3 ways to collect data 1. surveys 2. surveying records 3. direct observations
Created by: mlittle15