Business Analytics Exam quizzes
and answers graded A+
Data warehouses - ANS✅✅where data are recorded and stored electronically
Big data - ANS✅✅data sets so large that traditional methods of storage and analysis are
inadequate
Transactional data - ANS✅✅data collected for recording the companies' transactions
Data mining - ANS✅✅the process of using transactional data to make other decisions and
predictions, sometimes called predictive analytics
business analytics - ANS✅✅describes any use of statistical analysis to drive business decisions
from data
observations - ANS✅✅information collected regarding some subject. who, what, when, where, (if
possible) why, and how. sometimes called data values
case - ANS✅✅an individual row of a data table corresponding to one set of data, identifies about
whom we record some characteristics, sometimes called records
respondents - ANS✅✅individuals who answer a survey
subjects - ANS✅✅people in an experiment, sometimes called participants
experimental units - ANS✅✅animals, plants, websites, or other inanimate objects
variables - ANS✅✅the characteristics recorded about each individual or case, identify what has
been measured
metadata - ANS✅✅contains information about how, when, where, and possibly why the data
were collected; who each case represents; and the definitions of all the variables
, relational database - ANS✅✅when two or more separate data tables are linked together so that
information can be merged across them. each data table included in the database is a relation
because it as about a specific set of cases with information about each of these cases for all the
variables
qualitative variable - ANS✅✅when a variable names categories and answers questions about how
cases fall into those categories, also called categorical variable.
quantitative variable - ANS✅✅when a variable has measured numerical values with units and the
variable tells us about the quantity of what is measured
units - ANS✅✅how each value has been measured, the corresponding scale of measurement, how
much of something we have, how far apart two values are
identifier variable - ANS✅✅a unique identifier assigned to each individual or item in a group. do
not have units, useful in combining data from different sources, not variables to be analyzed. Ex:
social security number, student ID number, tracking number, transaction number
nominal variables - ANS✅✅categorical variables used only to name categories that don't have
order
ordinal values - ANS✅✅when data values can be ordered. Ex: employees can be ranked according
to the number of months employed
time series data - ANS✅✅variables that are measured at regular intervals over time, typical
measuring points are months, quarters, or years
cross-sectional data - ANS✅✅when several variables are all measured at the same time point
frequency table - ANS✅✅organizes data by recording totals and category names, the names of the
categories label each row, report counts or percentages or both
three rules of data analysis - ANS✅✅make a picture, make a picture, make a picture - they reveal
things that can't be seen in a table of numbers, show important features and patterns in the data,
and provide an excellent means for reporting findings to others
and answers graded A+
Data warehouses - ANS✅✅where data are recorded and stored electronically
Big data - ANS✅✅data sets so large that traditional methods of storage and analysis are
inadequate
Transactional data - ANS✅✅data collected for recording the companies' transactions
Data mining - ANS✅✅the process of using transactional data to make other decisions and
predictions, sometimes called predictive analytics
business analytics - ANS✅✅describes any use of statistical analysis to drive business decisions
from data
observations - ANS✅✅information collected regarding some subject. who, what, when, where, (if
possible) why, and how. sometimes called data values
case - ANS✅✅an individual row of a data table corresponding to one set of data, identifies about
whom we record some characteristics, sometimes called records
respondents - ANS✅✅individuals who answer a survey
subjects - ANS✅✅people in an experiment, sometimes called participants
experimental units - ANS✅✅animals, plants, websites, or other inanimate objects
variables - ANS✅✅the characteristics recorded about each individual or case, identify what has
been measured
metadata - ANS✅✅contains information about how, when, where, and possibly why the data
were collected; who each case represents; and the definitions of all the variables
, relational database - ANS✅✅when two or more separate data tables are linked together so that
information can be merged across them. each data table included in the database is a relation
because it as about a specific set of cases with information about each of these cases for all the
variables
qualitative variable - ANS✅✅when a variable names categories and answers questions about how
cases fall into those categories, also called categorical variable.
quantitative variable - ANS✅✅when a variable has measured numerical values with units and the
variable tells us about the quantity of what is measured
units - ANS✅✅how each value has been measured, the corresponding scale of measurement, how
much of something we have, how far apart two values are
identifier variable - ANS✅✅a unique identifier assigned to each individual or item in a group. do
not have units, useful in combining data from different sources, not variables to be analyzed. Ex:
social security number, student ID number, tracking number, transaction number
nominal variables - ANS✅✅categorical variables used only to name categories that don't have
order
ordinal values - ANS✅✅when data values can be ordered. Ex: employees can be ranked according
to the number of months employed
time series data - ANS✅✅variables that are measured at regular intervals over time, typical
measuring points are months, quarters, or years
cross-sectional data - ANS✅✅when several variables are all measured at the same time point
frequency table - ANS✅✅organizes data by recording totals and category names, the names of the
categories label each row, report counts or percentages or both
three rules of data analysis - ANS✅✅make a picture, make a picture, make a picture - they reveal
things that can't be seen in a table of numbers, show important features and patterns in the data,
and provide an excellent means for reporting findings to others
