COMM 214 Business Analytics -
MIDTERM NOTES CHAPTER 1, 2 and 3
2025 Concordia University
CHAPTER 1, 2 and 3
Statistics is The science of • collecting, • organizing, • analyzing, • and interpreting
Data in order to make decisions.
Data Consist of information coming from observations, counts, measurements, or
responses. (row data, from individuals) (average data, of a sample)
Samples: we can't collect data from everybody so we sample and generalize.
Quantitative Data (numerical measurements):
discrete data: countable numbers like 0,1,2,3 etc
continuous data: infinitely many possible values that can be decimal. (ratio and interval).
Interval: no 0 starting point could also be negative (temperature, years).
Ratio: 0 starting point (height or prices (can’t be negative)
Qualitative data: non numerical entries, words for
example. (nominal and ordinal).
Nominal: data that consist of names, labels, or categories only, cannot be arranged in
an ordering scheme (none are better than the other).
Ordinal: that may be arranged in some order, but differences between data values either
cannot be determined or are meaningless. (ex: grades in letters in a class)
Descriptive Statistics Involves organizing, summarizing, and displaying data.
Inferential Statistics Involves using sample data to draw conclusions about a population.
,Descriptive Statistics
Collect data:
Observational study: An observational study observes individuals and measures
variables of interest but does not attempt to influence the responses.
Experiment: A treatment is applied to part of a population and responses are observed.
Simulation: Uses a mathematical or physical model to reproduce the conditions of a
situation or process.
Survey: An investigation of one or more characteristics of a population.
Sample data:
Simple Random Sample: Assign a number to each member of the population. Members
of the population that correspond to these numbers become members of the sample.
Stratified Sample: Divide a population into groups (strata) and select a random
sample from each group.
Cluster Sample: Divide the population into groups (clusters) and select all of the
members in one or more, but not all, of the clusters. (divide into zones)
Systematic Sample: Choose a starting value at random. Then choose every kth member
of the population.
Convenience Sample: Choose only members of the population that are easy to get (not
recommended)
, Presenting data:
Frequency Tables: lists classes (or categories) of values, along with frequencies (or
counts) of the number of values that fall into each class.
(Grouped Data)
Criteria for Building Classes: Classes must be mutually exclusive, Classes must be
all-inclusive, Classes should be of equal width, Empty classes should be avoided except
in the middle.
Histograms: A histogram is a bar chart with the bars drawn adjacent to each other
(usually without gaps).
MIDTERM NOTES CHAPTER 1, 2 and 3
2025 Concordia University
CHAPTER 1, 2 and 3
Statistics is The science of • collecting, • organizing, • analyzing, • and interpreting
Data in order to make decisions.
Data Consist of information coming from observations, counts, measurements, or
responses. (row data, from individuals) (average data, of a sample)
Samples: we can't collect data from everybody so we sample and generalize.
Quantitative Data (numerical measurements):
discrete data: countable numbers like 0,1,2,3 etc
continuous data: infinitely many possible values that can be decimal. (ratio and interval).
Interval: no 0 starting point could also be negative (temperature, years).
Ratio: 0 starting point (height or prices (can’t be negative)
Qualitative data: non numerical entries, words for
example. (nominal and ordinal).
Nominal: data that consist of names, labels, or categories only, cannot be arranged in
an ordering scheme (none are better than the other).
Ordinal: that may be arranged in some order, but differences between data values either
cannot be determined or are meaningless. (ex: grades in letters in a class)
Descriptive Statistics Involves organizing, summarizing, and displaying data.
Inferential Statistics Involves using sample data to draw conclusions about a population.
,Descriptive Statistics
Collect data:
Observational study: An observational study observes individuals and measures
variables of interest but does not attempt to influence the responses.
Experiment: A treatment is applied to part of a population and responses are observed.
Simulation: Uses a mathematical or physical model to reproduce the conditions of a
situation or process.
Survey: An investigation of one or more characteristics of a population.
Sample data:
Simple Random Sample: Assign a number to each member of the population. Members
of the population that correspond to these numbers become members of the sample.
Stratified Sample: Divide a population into groups (strata) and select a random
sample from each group.
Cluster Sample: Divide the population into groups (clusters) and select all of the
members in one or more, but not all, of the clusters. (divide into zones)
Systematic Sample: Choose a starting value at random. Then choose every kth member
of the population.
Convenience Sample: Choose only members of the population that are easy to get (not
recommended)
, Presenting data:
Frequency Tables: lists classes (or categories) of values, along with frequencies (or
counts) of the number of values that fall into each class.
(Grouped Data)
Criteria for Building Classes: Classes must be mutually exclusive, Classes must be
all-inclusive, Classes should be of equal width, Empty classes should be avoided except
in the middle.
Histograms: A histogram is a bar chart with the bars drawn adjacent to each other
(usually without gaps).
