IDS 270 Midterm – UIC Sparks –
Comprehensive Study Guide with Key Terms
and Examples
Standard deviation of a population or probability model. Greek letter sigma (lower case) σ
Residual Observed minus predicted (observed is the actual piece of data, predicted is the
prediction from the regression line)
Obervational Study We observe the behavior or ask people questions as they are going
about their day to day lives. Ex. Call people and ask them how often they eat out.
Experiement We impose some treatment on people and then observe their behavior or ask
questions. Ex. Give people a drug and then do chemical analysis of their blood.
Confounding variables Two variables where the effect of one on the dependent variable
cannot be separated from the effect of the other. Ex. If you take drugs and drink alcohol and then
have a car accident, the effect of the drugs vs. the alcohol cannot be separated from each other as
the cause of the accident.
Population The set of everything. Ex. All the people in America
Sample A small group pulled from the population. Ex. Sample 40 voters from the
population of America
, Voluntary Response Sampling People volunteer to give data for a study. Ex. people text to
a phone number if they agree with a statement and text to a different one if they disagree
Convenience Sampling People are selected to be in a sample because of convenience or
low expense. Ex. Mall intercepts, or asking the people on the floor of your dorm to fill out a
survey.
Bias Systematically favoring a certain outcome from research
Simple Random Sampling Label all the people in the population with a number. Have a
computer select a set of random numbers in that range. People labeled with the number are
selected for the sample. Therefore, every person or sub-group of people has an equal chance of
being in the sample.
Stratified Sampling First breaking the population into natural segments (called strata) and
then using random sampling within the strata. Ex. Radio stations or types of colleges
(community colleges, public universities, private universities, stand-alone liberal arts colleges)
Multi-Stage sampling Basically practicing stratified sampling repeatedly to get to a final
sample.
Under Coverage Where some members of the population cannot be selected for the
sample. Ex. People without phones if one is collecting data over the phone.
Comprehensive Study Guide with Key Terms
and Examples
Standard deviation of a population or probability model. Greek letter sigma (lower case) σ
Residual Observed minus predicted (observed is the actual piece of data, predicted is the
prediction from the regression line)
Obervational Study We observe the behavior or ask people questions as they are going
about their day to day lives. Ex. Call people and ask them how often they eat out.
Experiement We impose some treatment on people and then observe their behavior or ask
questions. Ex. Give people a drug and then do chemical analysis of their blood.
Confounding variables Two variables where the effect of one on the dependent variable
cannot be separated from the effect of the other. Ex. If you take drugs and drink alcohol and then
have a car accident, the effect of the drugs vs. the alcohol cannot be separated from each other as
the cause of the accident.
Population The set of everything. Ex. All the people in America
Sample A small group pulled from the population. Ex. Sample 40 voters from the
population of America
, Voluntary Response Sampling People volunteer to give data for a study. Ex. people text to
a phone number if they agree with a statement and text to a different one if they disagree
Convenience Sampling People are selected to be in a sample because of convenience or
low expense. Ex. Mall intercepts, or asking the people on the floor of your dorm to fill out a
survey.
Bias Systematically favoring a certain outcome from research
Simple Random Sampling Label all the people in the population with a number. Have a
computer select a set of random numbers in that range. People labeled with the number are
selected for the sample. Therefore, every person or sub-group of people has an equal chance of
being in the sample.
Stratified Sampling First breaking the population into natural segments (called strata) and
then using random sampling within the strata. Ex. Radio stations or types of colleges
(community colleges, public universities, private universities, stand-alone liberal arts colleges)
Multi-Stage sampling Basically practicing stratified sampling repeatedly to get to a final
sample.
Under Coverage Where some members of the population cannot be selected for the
sample. Ex. People without phones if one is collecting data over the phone.
