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# Prob.Stat. Ch.1/14

### Ch.1 14.1 14.2 Margin of Error

TermDefinition
Variable a characteristic or attribute that can assume different
Data the values the variables assume
Random Variable 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 occurring
Population all the subjects that are being studied
Sample a group of subjects selected from a population
Hypothesis Testing decision-making process for evalutating claims about a population
Statisitics the science of conducting studies to collect, organize, summarize, analyze, and draw conclusions from data
3 examples of how statistics is used in everyday life gambling, sports, public health
3 reasons to study statistics Read and understand the various statistical studies performed in your fields You may be called on to conduct research in your field Can become better consumers and citizens
Descriptive consists of collection, organization, summarization, and presentation of data Ex. U.S. population senses
Inferential generalizing from samples to populations, performing estimations and hypothesis tests, determining relationships among variables, and making predictions. Ex. gambling
Quantitative numerical and can be ordered or ranked Ex. age, height, weight
Qualitative can be placed into distinct categories but not ranked Ex. gender, color, religion
Discrete quantitative- variables that assume values that can be counted Ex. # of children, # of students
Continuous quantitative- variables that can assume an infinite number values between any 2 specific values. Ex. capacity
Boundaries Ex. 18 17.5 - 18.5 4.68 4.675 - 4.685
Nominal no order or rank can be imposed on the data Ex. gender, zip code
Ordinal precise differences between ranks do not exist Ex. rating public speakers, rating floats (1st, 2nd, 3rd)
Interval precise differences between units of measure; no meaningful zero Ex. IQ test, Temperature
Ratio same as interval but with a meaningful zero Ex. height, weight
2 purposes of data collection to describe situations or events to help people make better decisions before acting
3 ways to collect data surveys, survey records, direct observation
Random Sampling putting random numbers in a hat and having people pull them out randomly
Systematic Sampling every fourth person being selected from a group
Stratified Sampling splitting people into groups by gender and only taking 10 from each group
Cluster Sampling characterizing people into 5 groups and taking all the people of 2 of these groups
Telephone survey advantage-people can be more candid disadvantage- not all people can be surveyed
Mailed survey advantage- can cover a wider geographic area disadvantage- low number of responses
Observational- Examples Age of motorcycle owners
Experimental- Examples Type of instruction affects the number of sit-ups
Observational- advantages and disadvantages Natural setting; can be done in dangerous situations; can be done using variable that can't be manipulated Definite cause and effect can't be found; expensive and time consuming; may have inaccuracies in measurements
Experimental- advantages and disadvantages Researcher can decide how to select and group subjects; can control the manipulated variable Unnatural setting; Hawthorne Effect; Confounding variable
Independent Variables the variable that is being manipulated by the researcher
Dependent Variables the variable that is being studied to see if it changes due to the manipulated
5 uses of statistics To describe data; To compare two or more data sets; To determine if variables are related; To test hypothesis; To make estimates about population variances
7 misuses of statistics Suspect samples; Ambiguous averages; Changing the subject; Detached statistics; Implied connections; Misleading graph; Faulty survey
Things that make bad questions Biased questions; Confusing words; Double-barreled questions; Double negatives in a question; Improper ordering of questions
Problems of getting random samples and systematic being able to number the whole population
true vs. quasi experiments True- sample selected randomly Quasi- using intact group because separation is not possible
Confounding Varibles a variable that influences the results of dependent but cannot be separated from the independent
Hawthorne Effect the subject knows that they are participating and purposely change their behavior in ways that it affects the results
Control Group the group that does not receive the treatment
Treatment group the group that receives specific treatment
Margin of Error +/- 1/(sq rt n)
MoE Interval The percent +/- the margin of error
Biased question a question that leads people to respond a certain way changing the results of the study
Sequential Sampling used in quality control
Double sampling Giving a huge population a questionnaire to find out who is qualified for the actual study
Multistage sampling uses multiple types sampling
Created by: ATocci96