Statistical Methods (+2)

Statistical Methods

Z-score method (for normally distributed data):

z_scores = (df['value'] - df['value'].mean()) / df['value'].std()
outliers = df[abs(z_scores) > 3]  # More than 3 standard deviations

IQR method (robust to non-normal distributions):

Q1 = df['value'].quantile(0.25)
Q3 = df['value'].quantile(0.75)
IQR = Q3 - Q1
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR
outliers = df[(df['value'] < lower_bound) | (df['value'] > upper_bound)]

Percentile method (simplest):

outliers = df[(df['value'] < df['value'].quantile(0.01)) |
              (df['value'] > df['value'].quantile(0.99))]

Handling Outliers

Do NOT automatically remove outliers. Instead:

1. Investigate: Is this a data error, a genuine extreme value, or a different population?
2. Data errors: Fix or remove (e.g., negative ages, timestamps in year 1970)
3. Genuine extremes: Keep them but consider using robust statistics (median instead of mean)
4. Different population: Segment them out for separate analysis (e.g., enterprise vs. SMB customers)

Report what you did: "We excluded 47 records (0.3%) with transaction amounts >$50K, which represent bulk enterprise orders analyzed separately."

Time Series Anomaly Detection

For detecting unusual values in a time series:

1. Compute expected value (moving average or same-period-last-year)
2. Compute deviation from expected
3. Flag deviations beyond a threshold (typically 2-3 standard deviations of the residuals)
4. Distinguish between point anomalies (single unusual value) and change points (sustained shift)