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Flashcards in CH 1 Deck (39):
1

Statistics

Numerical summary of a sample

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Descriptive statistics

Organizing and summarizing data

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Inferential statistics

Methods that take a result form a sample, extend it to the population, and measure the reliability of the result.

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Parameter

Numerical summary of a population

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Popoulation

Entire group in study

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Sample

Subset of the population being studied

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Qualitative varible

Can be classified based on attribute or characteristic

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Quantitative variable

Numerical measure of an individual

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Discrete quantitative variable

Countable, finite number of possible values

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Continuous quantitative variable

Infinite number of possible values. Can take on every possible value between any two values.

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Nominal variable

Names, labels, or categorizes

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Ordinal variable

Nominal + must have level of ranking or specific orders

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Interval variable

Ordinal + differences in values of variables

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Ratio variable

Interval + clear definition of zero (none of something)

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Observational Study

Measures the value of the response variable without attempting to influence the value of either the response or explanatory variables.

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Experiment

Intentionally change the value of the explanatory variable to record the responses of each group.

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Cross-sectional studies

Collect information at a specific point in time, or over a very short period of time.

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Case-control studies

Retrospective. Look back in time or look at existing records. Individuals with certain characteristics are matched with those who do not have it.

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Cohort Studies

the cohort = the group to be studied. They are observed over a long period of time = Prospective study.

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Response variable

What is being studied

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Explanatory variable

What is affecting the response variable

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Confounding

when the effects of two or more explanatory variables are not separated. Could lead to different conclusions about the results.

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Lurking variable

An explanatory variable that was not considered in a study, but that affects the value of the response variable in the study. Typical related to explanatory variables.

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Random Sampling

Process of using chance to select individuals form a population to be included in the sample

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Sample without replacement

Individual who is selected is removed from the population and cannot be chosen again.

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Sample with replacement

Selected individual is placed back into the population and could be chosen a second time.

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Simple Random Sampling

A sample of size n from a population of size N is obtained through simple random sampling if every possible sample of size n has an equally likely chance of occurring.

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Stratified sample

Obtained by separating the population into non overlapping groups and then obtaining a simple random sample from each group

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Systematic sampling

Obtained by selecting every "k"th individual from the population (first individual is selected randomly from 1 to k)

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Cluster sampling

Obtained by selecting individuals within a randomly selected group of individuals.

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Convenience sampling

Sample in which the individuals are easily obtained

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Systematic formula when k is known

p + (n-1)k
p = first client
n = sample size
k = every "k"th individual

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How to find "k"th individual in systematic sample when N/n is known.

N/n
N = total population
n = desired sample size

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Sampling bias

When the technique used to sample favors one part of the population over another.

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Under coverage

proportion of one segment of the population is lower in a sample than the population

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Response bias

When answers on a survey do not reflect the true feelings of the respondent.

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Inherent bias

Inability to measure accurately and directly what one would wish to measure.

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Matched-pair design (experiment)

Experimental units are paired up. Where one individual will receive one treatment and the other receives a different one. aka Pre and post experiment

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Completely randomized design

Each experimental unit is randomly assigned to a treatment.