Final answer:
In data science and statistics, the process involves steps like examining data shape to choose a measure of center, identifying outliers, and classifying data as qualitative, quantitative discrete, or quantitative continuous.
Step-by-step explanation:
In the context of data science and statistics, the questions related to the application process such as Imputation and Encoding, Aggregation and Filtering, Outlier Detection and Scaling, and Validation and Imputation assist in understanding and preparing the data for further analysis. To tackle these aspects, certain steps are followed:
- Examine the shape of the data to decide on the most appropriate measure of center: mean, median, or mode for the data.
- Identify outliers using numerical tests such as the Interquartile Range (IQR) and consider whether data more than two standard deviations from the mean should be considered unusual, especially if the data is mound-shaped and symmetric.
- To comprehend the nature of the data, identify whether it is qualitative, quantitative discrete, or quantitative continuous.
- Analyze if any potential outliers exist and determine the appropriate action to take regarding these outliers based on the context.
When working with different types of data, like the number of times per week a park is used, the data type would be quantitative discrete. Systematic sampling is represented by interviewing every eighth house in a neighborhood. The duration of residents using a park is an example of quantitative continuous data, whereas the colors of houses are qualitative data.