C207 – Data Driven Decision Making – exam review for OA
(Lecture Notes Revised module 1-6) 2025 Western Governors
University
Contents
Module 1: The Case for Quantitative Analysis ............................................................................... 2
Module 2: Statistics as a Managerial Tool...................................................................................... 5
Module 3: Quantitative Decision Tools .......................................................................................... 12
Module 4: Quality Management Basics......................................................................................... 17
Module 5: Real World Data Driven Decisions ............................................................................. 25
Module 6: Improving Organizational Performance ....................................................................28
,Module 1: The Case for Quantitative Analysis
Analytics
Analytics is the extensive use of data, statistical and quantitative analysis,
explanatory and predictive models, and fact-based management to drive decisions
and add value.
Big Data refers to both structured and unstructured data in such large volumes
that it's difficult to process using traditional database and software techniques.
Data mining is the process of discovering patterns in large data sets. Data mining
is performed on big data to decipher patterns from these large databases.
Davenport-Kim Three-Stage Model
1. Framing the Problem
a. Problem Recognition
i. Identifying Stakeholders
ii. Focusing on decisions
iii. Identifying the kind of story
iv. Determining the scope of the problem
v. Getting specific about what data to analyze
b. Review of Previous Findings
, 2. Solving the Problem
a. Modeling Step
b. Data Collection Step
c. Data Analysis Step
3. Communicating Results
Levels of Measurement
Continuous Data – a data point can lay along any point in a range of data
• Interval Data – all objects are an equally interval apart, cannot have a
natural zero (time, dates, Fahrenheit temperature)
• Ratio Data – has a unique zero point, numbers can be compared as
multiples of another (age, height, income, stock price)
Discrete Data – can only take whole values and has clear boundaries (impossible
to own 3.4 cars)
• Nominal Data – called categorical data, used to label subjects in a study
(gender)
• Ordinal Data – places objects into an order according to some quality
(karate belts)
Reliability and Validity of Data
Reliable Data is both consistent and repeatable.
Valid Data is data resulting from a test that accurately measures what it is
intended to measure.
Random errors will not repeat over time, minimized by larger sample size.
Systematic errors are not due to chance and can be corrected. It repeats itself;
constant measurement error; measurement instruments
Correlation – the extent to which two variables are linearly related
Skewness – a measure of the degree to which data leans toward one side
Biased Data:
• Selection bias occurs when individuals or groups in a study differ
systematically from the population of interest leading to a systematic error in
an association or outcome.
• Measurement bias occurs when the measurement of study variables is
inaccurate. Can result from faulty measurement tools, misclassification of the
sample, or failing to correctly measure the right variable.
• Response bias is the result of study participants responding in a way that
may not reflect their true opinion. Commonly found in surveys.
Information Bias
, • Response Bias – Respondent says what they believe the questioner wants
to hear
• Conscious Bias – Surveyor is actively seeking a certain response
Data Management
Data management refers to cleaning and organizing a data set that has been
collected:
• Available
• Accurate
• Complete
• Relevant
• Timely
Relational database is a database structured to recognize relations among stored
items of information.
Omission Error – when relevant data is not included in study or action has not
been taken
Outlier – observation points (numbers) that are distant from other observations
Research Design
Observational Studies – when it’s impractical or impossible to control the
conditions of the study
• Cohort Study – observes people going forward in time from the time of their
entry into the study.
• Case Control Study - a study that compares two groups of people
Experimental Study – variable measurements and subjects are under the
researcher's control
• Experimental units – subjects or objects under observation
• Treatments – the procedures applied to each subject
• Responses – the effects of the experimental treatments
Experimental Studies: Explanatory Variable
Also known as the independent or predictor variable, it explains variations in the
response variable; in an experimental study, it is manipulated by the researcher
Experimental Studies: Response Variable
Also known as the dependent or outcome variable, its value is predicted, or its
variation is explained by the explanatory variable; in an experimental study, this is
the outcome of study
(Lecture Notes Revised module 1-6) 2025 Western Governors
University
Contents
Module 1: The Case for Quantitative Analysis ............................................................................... 2
Module 2: Statistics as a Managerial Tool...................................................................................... 5
Module 3: Quantitative Decision Tools .......................................................................................... 12
Module 4: Quality Management Basics......................................................................................... 17
Module 5: Real World Data Driven Decisions ............................................................................. 25
Module 6: Improving Organizational Performance ....................................................................28
,Module 1: The Case for Quantitative Analysis
Analytics
Analytics is the extensive use of data, statistical and quantitative analysis,
explanatory and predictive models, and fact-based management to drive decisions
and add value.
Big Data refers to both structured and unstructured data in such large volumes
that it's difficult to process using traditional database and software techniques.
Data mining is the process of discovering patterns in large data sets. Data mining
is performed on big data to decipher patterns from these large databases.
Davenport-Kim Three-Stage Model
1. Framing the Problem
a. Problem Recognition
i. Identifying Stakeholders
ii. Focusing on decisions
iii. Identifying the kind of story
iv. Determining the scope of the problem
v. Getting specific about what data to analyze
b. Review of Previous Findings
, 2. Solving the Problem
a. Modeling Step
b. Data Collection Step
c. Data Analysis Step
3. Communicating Results
Levels of Measurement
Continuous Data – a data point can lay along any point in a range of data
• Interval Data – all objects are an equally interval apart, cannot have a
natural zero (time, dates, Fahrenheit temperature)
• Ratio Data – has a unique zero point, numbers can be compared as
multiples of another (age, height, income, stock price)
Discrete Data – can only take whole values and has clear boundaries (impossible
to own 3.4 cars)
• Nominal Data – called categorical data, used to label subjects in a study
(gender)
• Ordinal Data – places objects into an order according to some quality
(karate belts)
Reliability and Validity of Data
Reliable Data is both consistent and repeatable.
Valid Data is data resulting from a test that accurately measures what it is
intended to measure.
Random errors will not repeat over time, minimized by larger sample size.
Systematic errors are not due to chance and can be corrected. It repeats itself;
constant measurement error; measurement instruments
Correlation – the extent to which two variables are linearly related
Skewness – a measure of the degree to which data leans toward one side
Biased Data:
• Selection bias occurs when individuals or groups in a study differ
systematically from the population of interest leading to a systematic error in
an association or outcome.
• Measurement bias occurs when the measurement of study variables is
inaccurate. Can result from faulty measurement tools, misclassification of the
sample, or failing to correctly measure the right variable.
• Response bias is the result of study participants responding in a way that
may not reflect their true opinion. Commonly found in surveys.
Information Bias
, • Response Bias – Respondent says what they believe the questioner wants
to hear
• Conscious Bias – Surveyor is actively seeking a certain response
Data Management
Data management refers to cleaning and organizing a data set that has been
collected:
• Available
• Accurate
• Complete
• Relevant
• Timely
Relational database is a database structured to recognize relations among stored
items of information.
Omission Error – when relevant data is not included in study or action has not
been taken
Outlier – observation points (numbers) that are distant from other observations
Research Design
Observational Studies – when it’s impractical or impossible to control the
conditions of the study
• Cohort Study – observes people going forward in time from the time of their
entry into the study.
• Case Control Study - a study that compares two groups of people
Experimental Study – variable measurements and subjects are under the
researcher's control
• Experimental units – subjects or objects under observation
• Treatments – the procedures applied to each subject
• Responses – the effects of the experimental treatments
Experimental Studies: Explanatory Variable
Also known as the independent or predictor variable, it explains variations in the
response variable; in an experimental study, it is manipulated by the researcher
Experimental Studies: Response Variable
Also known as the dependent or outcome variable, its value is predicted, or its
variation is explained by the explanatory variable; in an experimental study, this is
the outcome of study
