Data Analysis and Interpretation
Statistical analysis is fundamental for interpreting sensor data and making informed engineering decisions. Key concepts include understanding populations and samples, employing descriptive statistics, and recognizing the importance of data reduction and signal processing techniques. The module provides essential tools for civil engineers to turn raw measurement data into actionable insights for safety and performance evaluation.
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What we have learnt
- Statistical analysis aids in interpreting and making decisions based on sensor data.
- Data reduction techniques simplify large datasets while retaining critical information.
- Understanding sensors and data types is crucial for reliable monitoring in civil engineering.
Key Concepts
- -- Population and Sample
- Population refers to the entire dataset, whereas a sample is a subset used for analysis.
- -- Descriptive Statistics
- Summarize or describe features of data sets, helping to understand the underlying data better.
- -- Probability Distributions
- Describe the likelihood of variable values, with the normal distribution being common in measurement data.
- -- SignaltoNoise Ratio (SNR)
- Measures the relative strength of useful signals versus noise; a higher SNR indicates clearer signals.
- -- Mean
- The average of observations, central tendency of data.
- -- Standard Deviation (SD)
- Measures the amount by which each measurement differs from the mean, indicating data spread.
- -- Data Reduction
- The process of simplifying large volumes of data into meaningful summaries without losing critical information.
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