ISDS exam 1 complete exam 2023 solution
descriptive statistics - collecting, organizing, and presenting the data
inferential statistics - drawing conclusions about a population based on sample
data from that population
population - consists of all items of interest
sample - a subset of the population
sample statistic - calculated from the sample data and is used to make inferences about
the population parameter
cross-sectional data - data collected by a characteristic of many subjects at the same
point in time, or without regard to differences in time
time series data - data collected by recording a characteristic of a subject over
several time periods
variable - the general characteristic being observed on an object of interest
qualitative - gender, race, political affiliation
quantitative - test scores, age, weight (discrete, continuous)
discrete - variable assumes a countable number of distinct values (ex. number
of children in a family, number of points scored in a basketball game)
continuous - variable can assume an infinite number of values within some interval (ex.
weight, height, investment return)
nominal scale - data are simply categories for grouping the data
ordinal scale - there is no objective way to interpret the difference between
instructor quality (ex. instructors are evaluated by excellent, good, fair, poor)
interval scale - no absolute 0 or starting point defined
ratio scale - data may be categorized and ranked, there is an absolute 0 (ex.
weight, time, distance)
frequency distribution - for qualitative data, groups data into categories and records
how many observations fall into each category
relative frequency - calculate by dividing each category's frequency by the sample size
descriptive statistics - collecting, organizing, and presenting the data
inferential statistics - drawing conclusions about a population based on sample
data from that population
population - consists of all items of interest
sample - a subset of the population
sample statistic - calculated from the sample data and is used to make inferences about
the population parameter
cross-sectional data - data collected by a characteristic of many subjects at the same
point in time, or without regard to differences in time
time series data - data collected by recording a characteristic of a subject over
several time periods
variable - the general characteristic being observed on an object of interest
qualitative - gender, race, political affiliation
quantitative - test scores, age, weight (discrete, continuous)
discrete - variable assumes a countable number of distinct values (ex. number
of children in a family, number of points scored in a basketball game)
continuous - variable can assume an infinite number of values within some interval (ex.
weight, height, investment return)
nominal scale - data are simply categories for grouping the data
ordinal scale - there is no objective way to interpret the difference between
instructor quality (ex. instructors are evaluated by excellent, good, fair, poor)
interval scale - no absolute 0 or starting point defined
ratio scale - data may be categorized and ranked, there is an absolute 0 (ex.
weight, time, distance)
frequency distribution - for qualitative data, groups data into categories and records
how many observations fall into each category
relative frequency - calculate by dividing each category's frequency by the sample size
