#### 14.1A statistician is analyzing a dataset of 1,200 observations. She applies a new filtering method that removes 15% of the outliers, reducing the dataset size by that percentage. She then applies a second method that removes an additional 10% of the remaining observations as noise. How many observations remain after both filtering steps?

Tue, 05 May 2026 06:15:11 +0000

#### #### 14.1A statistician is analyzing a dataset of 1,200 observations. She identifies patterns and removes data noise with two precise filtering steps that reflect modern data hygiene practices. First, she applies a method that removes 15% of outliers—observations significantly diverging from the norm—streamlining the dataset to better reflect core trends. Then, a second layer filters out 10% of remaining noisy entries, reducing false signals caused by measurement errors or anomalies. This two-step process supports clearer analysis, particularly valuable in fields where data precision shapes decision-making. ### How Many Observations Remain?
Start with 1,200 data points. Removing 15% of observables—defined as outliers—is calculated as:
15% of 1,200 = 0.15 × 1,200 = 180
The dataset shrinks to 1,200 – 180 = **1,020** preliminary records.
Next, an additional 10% of the remaining 1,020 observations are filtered as noise:
10% of 1,020 = 0.10 × 1,020 = 102
The final count becomes 1,020 – 102 = **918 observations** remaining. ### Why This Analysis Matters in the US Data Landscape
In today’s data-driven environment across the United States, reliable datasets fuel accurate insights in research, business intelligence, and innovation. Analysts like the statistician referenced anchor large-scale data cleaning as a critical step before drawing meaningful conclusions. Outliers and noise often distort trends, especially in complex surveys or big data applications. By systematically reducing 15% then 10%, the method enhances analytical clarity—supporting trust in findings used for policy, product development, or strategic planning. This process reflects a broader shift toward more rigorous data stewardship in both public and private sectors. ### Common Questions About Dataset Filtering Steps
**Q: Why remove outliers and noise specifically?