When.com Web Search

Search results

1. Results From The WOW.Com Content Network

2. Oversampling and undersampling in data analysis - Wikipedia

   en.wikipedia.org/wiki/Oversampling_and_under...

   Overabundance of already collected data became an issue only in the "Big Data" era, and the reasons to use undersampling are mainly practical and related to resource costs. Specifically, while one needs a suitably large sample size to draw valid statistical conclusions, the data must be cleaned before it can be used. Cleansing typically ...

3. Sample size determination - Wikipedia

   en.wikipedia.org/wiki/Sample_size_determination

   The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample. In practice, the sample size used in a study is usually determined based on the cost, time, or convenience of collecting the data, and the need for it to offer sufficient statistical power. In complex studies ...

4. Data analysis - Wikipedia

   en.wikipedia.org/wiki/Data_analysis

   Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]

5. Sampling (statistics) - Wikipedia

   en.wikipedia.org/wiki/Sampling_(statistics)

   A cheaper method would be to use a stratified sample with urban and rural strata. The rural sample could be under-represented in the sample, but weighted up appropriately in the analysis to compensate. More generally, data should usually be weighted if the sample design does not give each individual an equal chance of being selected.

6. Bootstrapping (statistics) - Wikipedia

   en.wikipedia.org/wiki/Bootstrapping_(statistics)

   This pre-aggregated data set becomes the new sample data over which to draw samples with replacement. This method is similar to the Block Bootstrap, but the motivations and definitions of the blocks are very different. Under certain assumptions, the sample distribution should approximate the full bootstrapped scenario.

7. Exploratory data analysis - Wikipedia

   en.wikipedia.org/wiki/Exploratory_data_analysis

   Exploratory data analysis is an analysis technique to analyze and investigate the data set and summarize the main characteristics of the dataset. Main advantage of EDA is providing the data visualization of data after conducting the analysis.