RINNAM
WGU C207 · DATA-DRIVEN DECISION MAKING
Study Guide
2026/2027
100 Practice Questions with Answers & Step-by-Step Explanations + 61
Worked Problems (Descriptive Stats, Probability, Hypothesis Tests,
Regression, Decision Analysis)
61 100
worked problems and scenarios, original practice questions with
every calculation step shown full written solutions
1 page 81
quick reference, plus formula glossary terms, plus an exam-
tables and a test chooser day checklist and memory tricks
Covers: Analytics Types & NOIR · Descriptive Stats · Charts · Probability · Sampling & CI ·
Hypothesis Tests · ANOVA & Chi-Square · Regression · Decision Trees & EVPI · DMAIC & Quality
Independent study guide. Written and published by Rinnam. Not affiliated with, sponsored by or endorsed by Western
Governors University (WGU). "WGU" and "C207" are used only to identify the course this guide is designed to support. The
guide contains no WGU course materials, task directions or assessment questions. All companies, scenarios, numbers and
practice questions are original and fictional. For study purposes only; always check your official course resources.
, Contents
One-Page Quick Reference ........................................3 10. ANOVA and Chi-Square ...................................... 22
Preview: Sample Scenario and Question .................... 4 Worked Scenarios 10.1–10.4 ................................................. 23
1. How to Use This Guide ............................................ 5 11. Correlation and Simple Linear Regression ............. 24
What this means for how you study · How this guide relates to the Worked Scenarios 11.1–11.6................................................... 25
PAs (without doing them for you) · A one-week study rhythm
12. Multiple Regression and Dummy Variables ........... 26
2. Data-Driven Decisions and Analytics Types .............6 Worked Scenarios 12.1–12.3 ................................................. 27
Four analytics flavors
13. Decision Analysis: Payoffs, Trees, and EVPI ......... 28
Worked Scenarios 2.1–2.3 .......................................................7
Worked Scenarios 13.1–13.6 ................................................. 28
3. Measurement Scales, Quality, and Honest Graphs ....8
Ethics and misleading pictures 14. Quality Tools, DMAIC, and Performance Measures
Worked Scenarios 3.1–3.4 ...................................................... 9 ..............................................................................30
Worked Scenarios 14.1–14.3 .................................................. 31
4. Descriptive Statistics ........................................... 10
Worked Scenarios 4.1–4.6 ...................................................... 11 15. Decision Map: Matching the Tool to the Question .. 32
Worked Scenarios 15.1–15.2 ................................................. 32
5. Visualizations Managers Actually Use ................... 13
Worked Scenarios 5.1–5.3 ..................................................... 13 16. Practice Questions ............................................. 35
Questions grouped by chapter, 100 in total
6. Probability and Expected Value ............................. 14
Worked Scenarios 6.1–6.5 ..................................................... 15 17. Answer Key and Worked Solutions .......................46
Quick answer grid ................................................................ 46
7. Sampling, Sampling Distributions, and the CLT ....... 16
Full solutions ......................................................................... 46
Worked Scenarios 7.1–7.4 ...................................................... 16
18. Exam-Day Checklist and Glossary ....................... 58
8. Confidence Intervals............................................ 17
Exam-day checklist .............................................................. 58
Worked Scenarios 8.1–8.5 ..................................................... 18
Glossary of 81 key terms ...................................................... 58
9. Hypothesis Testing .............................................. 19
Worked Scenarios 9.1–9.7 .................................................... 20
Rinnam WGU C207 Data-Driven Decision Making Study Guide · Independent, not affiliated with or endorsed by WGU 2
, One-Page Quick Reference
ANALYTICS AND FRAMING INFERENCE
Descriptive What happened? SE (mean) s/sqrtn
Diagnostic Why did it happen? CI mean x +/- z*·SE
Predictive What is likely next? CI proportion p +/- z*·sqrt(p(1−p)/n)
Prescriptive What should we do? z* 95% ~ 1.96
Davenport–Kim Frame -> Solve -> Communicate p <= α Reject H0
NOIR SCALES TESTS AND MODELS
Nominal Categories, no order t / z means One or two samples
Ordinal Order, unequal gaps ANOVA 3+ group means (F)
Interval Equal gaps, no true zero Chi-square Categorical counts
Ratio Equal gaps + true zero Regression y-hat = b0 + b1x; R²
Reliable vs valid Consistent vs measuring the right thing
Correlation != cause Association only without design
DESCRIPTIVE FORMULAS
DECISIONS AND QUALITY
Mean / median / mode Average / middle / most frequent
Maximax / maximin Best / best worst-case
Sample variance Σ(x−x)²/(n−1)
EMV Σ payoff x probability
z-score (x−μ)/σ
EVPI EVwPI − best EMV
Empirical rule ~68/95/99.7% within 1/2/3 σ
DMAIC Define Measure Analyze Improve Control
IQR Q3 − Q1
Series / parallel R Π ri / 1−Π(1−ri)
CHARTS
Histogram Shape of one numeric
Boxplot Median/IQR by group
Scatter Two numerics
Pareto Vital few categories
Control chart Process vs limits
NOIR = Nominal, Ordinal, Interval, Ratio; SE = standard error; CI = confidence interval; EMV = expected monetary value; EVPI =
expected value of perfect information; DMAIC = Define, Measure, Analyze, Improve, Control. Every formula is worked in its chapter
with original numbers checked in Python.
Rinnam WGU C207 Data-Driven Decision Making Study Guide · Independent, not affiliated with or endorsed by WGU 3
, Preview: What Every Chapter Looks Like
Every calculation gets a worked scenario like the one below: the facts, each step with the numbers
shown, and an answer box. Then you test yourself with original practice questions and check a full
written solution that explains every option.
SA M P L E WO R K E D S C E N A R I O · C H A P T E R 1 3
Worked Scenario 13.3 EVPI calculation
Using the same table and P(Strong)=0.4, compute EV with perfect information and EVPI.
SOLUTION
1. Best if Strong: Expand 80; best if Weak: Cut 12.
2. EVwPI = 0.4·80 + 0.6·12 = 39.2.
3. Best EMV without info = 22.0.
4. EVPI = 39.2 − 22.0 = 17.2.
A NSWER EVwPI=39.2; EVPI=17.2.
WHY IT MATTERS Do not pay more for a study than EVPI (perfect-info upper bound).
SA M P L E M E M O RY T R I C K A N D C O M M O N - M I S TA K E S B OX · H Y P OT H E S I S T E S T I N G
M EM ORY TRICK p small -> reject null. Compare the p-value to α. Do not say p is the probability the
null is true, and do not equate a tiny p with a huge practical effect.
C OM M ON M ISTAK ES
• Saying correlation proves causation.
• Using n instead of sqrtn in the standard error of the mean.
• Calling a forecast "prescriptive" before an action is recommended.
SA M P L E P R AC T I C E Q U E S T I O N · C H A P T E R 1 6
71. EVPI equals:
A R-squared times profit
B EV with perfect information minus the best EMV without information
C Maximax minus maximin
D Best EMV plus alpha
The full solution to this question is in Chapter 17 (question 71, page 54).
Independent study guide. Written and published by Rinnam. Not affiliated with, sponsored by or endorsed by Western Governors
University (WGU). "WGU" and "C207" are used only to identify the course this guide is designed to support. The guide contains no
WGU course materials, task directions or assessment questions. All companies, scenarios, numbers and practice questions are
original and fictional. For study purposes only; always check your official course resources.
Rinnam WGU C207 Data-Driven Decision Making Study Guide · Independent, not affiliated with or endorsed by WGU 4
WGU C207 · DATA-DRIVEN DECISION MAKING
Study Guide
2026/2027
100 Practice Questions with Answers & Step-by-Step Explanations + 61
Worked Problems (Descriptive Stats, Probability, Hypothesis Tests,
Regression, Decision Analysis)
61 100
worked problems and scenarios, original practice questions with
every calculation step shown full written solutions
1 page 81
quick reference, plus formula glossary terms, plus an exam-
tables and a test chooser day checklist and memory tricks
Covers: Analytics Types & NOIR · Descriptive Stats · Charts · Probability · Sampling & CI ·
Hypothesis Tests · ANOVA & Chi-Square · Regression · Decision Trees & EVPI · DMAIC & Quality
Independent study guide. Written and published by Rinnam. Not affiliated with, sponsored by or endorsed by Western
Governors University (WGU). "WGU" and "C207" are used only to identify the course this guide is designed to support. The
guide contains no WGU course materials, task directions or assessment questions. All companies, scenarios, numbers and
practice questions are original and fictional. For study purposes only; always check your official course resources.
, Contents
One-Page Quick Reference ........................................3 10. ANOVA and Chi-Square ...................................... 22
Preview: Sample Scenario and Question .................... 4 Worked Scenarios 10.1–10.4 ................................................. 23
1. How to Use This Guide ............................................ 5 11. Correlation and Simple Linear Regression ............. 24
What this means for how you study · How this guide relates to the Worked Scenarios 11.1–11.6................................................... 25
PAs (without doing them for you) · A one-week study rhythm
12. Multiple Regression and Dummy Variables ........... 26
2. Data-Driven Decisions and Analytics Types .............6 Worked Scenarios 12.1–12.3 ................................................. 27
Four analytics flavors
13. Decision Analysis: Payoffs, Trees, and EVPI ......... 28
Worked Scenarios 2.1–2.3 .......................................................7
Worked Scenarios 13.1–13.6 ................................................. 28
3. Measurement Scales, Quality, and Honest Graphs ....8
Ethics and misleading pictures 14. Quality Tools, DMAIC, and Performance Measures
Worked Scenarios 3.1–3.4 ...................................................... 9 ..............................................................................30
Worked Scenarios 14.1–14.3 .................................................. 31
4. Descriptive Statistics ........................................... 10
Worked Scenarios 4.1–4.6 ...................................................... 11 15. Decision Map: Matching the Tool to the Question .. 32
Worked Scenarios 15.1–15.2 ................................................. 32
5. Visualizations Managers Actually Use ................... 13
Worked Scenarios 5.1–5.3 ..................................................... 13 16. Practice Questions ............................................. 35
Questions grouped by chapter, 100 in total
6. Probability and Expected Value ............................. 14
Worked Scenarios 6.1–6.5 ..................................................... 15 17. Answer Key and Worked Solutions .......................46
Quick answer grid ................................................................ 46
7. Sampling, Sampling Distributions, and the CLT ....... 16
Full solutions ......................................................................... 46
Worked Scenarios 7.1–7.4 ...................................................... 16
18. Exam-Day Checklist and Glossary ....................... 58
8. Confidence Intervals............................................ 17
Exam-day checklist .............................................................. 58
Worked Scenarios 8.1–8.5 ..................................................... 18
Glossary of 81 key terms ...................................................... 58
9. Hypothesis Testing .............................................. 19
Worked Scenarios 9.1–9.7 .................................................... 20
Rinnam WGU C207 Data-Driven Decision Making Study Guide · Independent, not affiliated with or endorsed by WGU 2
, One-Page Quick Reference
ANALYTICS AND FRAMING INFERENCE
Descriptive What happened? SE (mean) s/sqrtn
Diagnostic Why did it happen? CI mean x +/- z*·SE
Predictive What is likely next? CI proportion p +/- z*·sqrt(p(1−p)/n)
Prescriptive What should we do? z* 95% ~ 1.96
Davenport–Kim Frame -> Solve -> Communicate p <= α Reject H0
NOIR SCALES TESTS AND MODELS
Nominal Categories, no order t / z means One or two samples
Ordinal Order, unequal gaps ANOVA 3+ group means (F)
Interval Equal gaps, no true zero Chi-square Categorical counts
Ratio Equal gaps + true zero Regression y-hat = b0 + b1x; R²
Reliable vs valid Consistent vs measuring the right thing
Correlation != cause Association only without design
DESCRIPTIVE FORMULAS
DECISIONS AND QUALITY
Mean / median / mode Average / middle / most frequent
Maximax / maximin Best / best worst-case
Sample variance Σ(x−x)²/(n−1)
EMV Σ payoff x probability
z-score (x−μ)/σ
EVPI EVwPI − best EMV
Empirical rule ~68/95/99.7% within 1/2/3 σ
DMAIC Define Measure Analyze Improve Control
IQR Q3 − Q1
Series / parallel R Π ri / 1−Π(1−ri)
CHARTS
Histogram Shape of one numeric
Boxplot Median/IQR by group
Scatter Two numerics
Pareto Vital few categories
Control chart Process vs limits
NOIR = Nominal, Ordinal, Interval, Ratio; SE = standard error; CI = confidence interval; EMV = expected monetary value; EVPI =
expected value of perfect information; DMAIC = Define, Measure, Analyze, Improve, Control. Every formula is worked in its chapter
with original numbers checked in Python.
Rinnam WGU C207 Data-Driven Decision Making Study Guide · Independent, not affiliated with or endorsed by WGU 3
, Preview: What Every Chapter Looks Like
Every calculation gets a worked scenario like the one below: the facts, each step with the numbers
shown, and an answer box. Then you test yourself with original practice questions and check a full
written solution that explains every option.
SA M P L E WO R K E D S C E N A R I O · C H A P T E R 1 3
Worked Scenario 13.3 EVPI calculation
Using the same table and P(Strong)=0.4, compute EV with perfect information and EVPI.
SOLUTION
1. Best if Strong: Expand 80; best if Weak: Cut 12.
2. EVwPI = 0.4·80 + 0.6·12 = 39.2.
3. Best EMV without info = 22.0.
4. EVPI = 39.2 − 22.0 = 17.2.
A NSWER EVwPI=39.2; EVPI=17.2.
WHY IT MATTERS Do not pay more for a study than EVPI (perfect-info upper bound).
SA M P L E M E M O RY T R I C K A N D C O M M O N - M I S TA K E S B OX · H Y P OT H E S I S T E S T I N G
M EM ORY TRICK p small -> reject null. Compare the p-value to α. Do not say p is the probability the
null is true, and do not equate a tiny p with a huge practical effect.
C OM M ON M ISTAK ES
• Saying correlation proves causation.
• Using n instead of sqrtn in the standard error of the mean.
• Calling a forecast "prescriptive" before an action is recommended.
SA M P L E P R AC T I C E Q U E S T I O N · C H A P T E R 1 6
71. EVPI equals:
A R-squared times profit
B EV with perfect information minus the best EMV without information
C Maximax minus maximin
D Best EMV plus alpha
The full solution to this question is in Chapter 17 (question 71, page 54).
Independent study guide. Written and published by Rinnam. Not affiliated with, sponsored by or endorsed by Western Governors
University (WGU). "WGU" and "C207" are used only to identify the course this guide is designed to support. The guide contains no
WGU course materials, task directions or assessment questions. All companies, scenarios, numbers and practice questions are
original and fictional. For study purposes only; always check your official course resources.
Rinnam WGU C207 Data-Driven Decision Making Study Guide · Independent, not affiliated with or endorsed by WGU 4
