WGU C207 DATA-DRIVEN DECISION
MAKING: LAST-MINUTE REVISION GUIDE
(2027)
1. Key Definitions
● Descriptive Statistics: Methods of organizing, summarizing, and presenting
data using charts, graphs, and numerical summaries (mean, median, mode,
standard deviation).
● Inferential Statistics: Techniques used to make generalizations, estimations, or
hypotheses about a large population based on sample data.
● Coefficient of Variation (CV): A standardized measure of relative variability
(CV=xˉs×100), allowing comparison of dispersion between datasets with different
units or means.
● Central Limit Theorem (CLT): The statistical principle stating that the sampling
distribution of the sample mean approaches a normal distribution as the sample
size increases (n≥30), regardless of the population's underlying distribution.
● Type I Error (α): Rejecting a true null hypothesis (a "false positive"). The
probability of committing this error is set by the significance level.
● Type II Error (β): Failing to reject a false null hypothesis (a "false negative").
● p-value: The probability of obtaining test results at least as extreme as the
observed results, assuming the null hypothesis is true. If p≤α, reject H0.
● Coefficient of Determination (R2): The proportion of total variance in the
dependent variable (y) that is explained by the independent variable(s) (x) in a
regression model.
● Process Capability (Cpvs. Cpk): Cpmeasures potential capability based solely
on process spread (width), while Cpkaccounts for both spread and process
centering relative to specification limits.
2. Important Rules and Formulas
Descriptive & Probability Rules
● Interquartile Range (IQR): IQR=Q3−Q1(measures the spread of the middle
50% of data).
, ● General Addition Rule: P(A or B)=P(A)+P(B)−P(A and B).
● Multiplication Rule for Independent Events: P(A and B)=P(A)×P(B).
Inferential Statistics & Hypothesis Testing
● Confidence Interval Formula: Statistic±(Critical Value×Standard Error).
● Sample Size for Estimating a Mean: n=(Ez⋅σ)2 (always round up to the next
whole integer).
● Decision Rule (p-value approach):
○ If p≤α→ Reject H0.
○ If p>α→ Fail to Reject H0.
Regression & Forecasting
● Simple Linear Regression Equation: y^=b0+b1x (where b0is the y-intercept
and b1is the slope).
● Moving Average Forecast: Arithmetic mean of the most recent n periods.
3. Common Exam Traps
● Confusing Mean and Median under Skew: Remember that right-skewed data
pulls the mean upward (Mode<Median<Mean), whereas left-skewed data pulls
the mean downward. Always use the median and IQR for skewed distributions.
● Misinterpreting Cpand Cpk: A high Cponly means the process spread is
narrow enough to fit inside the limits; it does not mean the process is centered. If
Cpkis significantly lower than Cp, the process has drifted off-center.
● Misinterpreting the Null Hypothesis (H0): The null hypothesis always contains
a statement of equality (=,≤,≥). Never place a strict inequality (<,>) in H0.
● Assuming Correlation Equals Causation: A high R2 or strong correlation
coefficient indicates a linear relationship, but it does not prove that x causes y.
● Rounding Sample Size Down: When calculating minimum sample size n, any
decimal remainder requires rounding up to ensure the margin of error is not
exceeded.
4. Quick Quiz (15 Questions)
Question 1
Which measure of central tendency is most resistant to extreme outliers in a heavily
skewed dataset?
MAKING: LAST-MINUTE REVISION GUIDE
(2027)
1. Key Definitions
● Descriptive Statistics: Methods of organizing, summarizing, and presenting
data using charts, graphs, and numerical summaries (mean, median, mode,
standard deviation).
● Inferential Statistics: Techniques used to make generalizations, estimations, or
hypotheses about a large population based on sample data.
● Coefficient of Variation (CV): A standardized measure of relative variability
(CV=xˉs×100), allowing comparison of dispersion between datasets with different
units or means.
● Central Limit Theorem (CLT): The statistical principle stating that the sampling
distribution of the sample mean approaches a normal distribution as the sample
size increases (n≥30), regardless of the population's underlying distribution.
● Type I Error (α): Rejecting a true null hypothesis (a "false positive"). The
probability of committing this error is set by the significance level.
● Type II Error (β): Failing to reject a false null hypothesis (a "false negative").
● p-value: The probability of obtaining test results at least as extreme as the
observed results, assuming the null hypothesis is true. If p≤α, reject H0.
● Coefficient of Determination (R2): The proportion of total variance in the
dependent variable (y) that is explained by the independent variable(s) (x) in a
regression model.
● Process Capability (Cpvs. Cpk): Cpmeasures potential capability based solely
on process spread (width), while Cpkaccounts for both spread and process
centering relative to specification limits.
2. Important Rules and Formulas
Descriptive & Probability Rules
● Interquartile Range (IQR): IQR=Q3−Q1(measures the spread of the middle
50% of data).
, ● General Addition Rule: P(A or B)=P(A)+P(B)−P(A and B).
● Multiplication Rule for Independent Events: P(A and B)=P(A)×P(B).
Inferential Statistics & Hypothesis Testing
● Confidence Interval Formula: Statistic±(Critical Value×Standard Error).
● Sample Size for Estimating a Mean: n=(Ez⋅σ)2 (always round up to the next
whole integer).
● Decision Rule (p-value approach):
○ If p≤α→ Reject H0.
○ If p>α→ Fail to Reject H0.
Regression & Forecasting
● Simple Linear Regression Equation: y^=b0+b1x (where b0is the y-intercept
and b1is the slope).
● Moving Average Forecast: Arithmetic mean of the most recent n periods.
3. Common Exam Traps
● Confusing Mean and Median under Skew: Remember that right-skewed data
pulls the mean upward (Mode<Median<Mean), whereas left-skewed data pulls
the mean downward. Always use the median and IQR for skewed distributions.
● Misinterpreting Cpand Cpk: A high Cponly means the process spread is
narrow enough to fit inside the limits; it does not mean the process is centered. If
Cpkis significantly lower than Cp, the process has drifted off-center.
● Misinterpreting the Null Hypothesis (H0): The null hypothesis always contains
a statement of equality (=,≤,≥). Never place a strict inequality (<,>) in H0.
● Assuming Correlation Equals Causation: A high R2 or strong correlation
coefficient indicates a linear relationship, but it does not prove that x causes y.
● Rounding Sample Size Down: When calculating minimum sample size n, any
decimal remainder requires rounding up to ensure the margin of error is not
exceeded.
4. Quick Quiz (15 Questions)
Question 1
Which measure of central tendency is most resistant to extreme outliers in a heavily
skewed dataset?
