- 4.1 Data Mining Tasks in
*Discovering Knowledge in Data* - 4.2 Statistical Approaches to Estimation and Prediction
- 4.3 Statistical Inference
- 4.4 How Confident are We in Our Estimates?
- 4.5 Confidence Interval Estimation of the Mean
- 4.6 How to Reduce the Margin of Error
- 4.7 Confidence Interval Estimation of the Proportion
- 4.8 Hypothesis Testing for the Mean
- 4.9 Assessing the Strength of Evidence Against the Null Hypothesis
- 4.10 Using Confidence Intervals to Perform Hypothesis Tests
- 4.11 Hypothesis Testing for the Proportion

In Chapter 1 we were introduced to the six data mining tasks:

- Description
- Estimation
- Prediction
- Classification
- Clustering
- Association

In the description task, analysts try to find ways to describe patterns and trends lying within the data. Descriptions of patterns and trends often suggest possible explanations for such patterns and trends, as well as possible recommendations for policy changes. This description task can be accomplished capably with exploratory data analysis (EDA), as we saw in Chapter 3. The description task may also be performed using descriptive statistics, such as the sample proportion or the regression equation, which we learn about in Chapters 4 and 5. Table 4.1 provides an outline of where in this book we learn about each of the data mining tasks.

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