Difference between revisions of "Data Analysis"
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== Read First == | == Read First == | ||
* include here key points you want to make sure all readers understand | |||
== Preparing the Data Set for Analysis == | |||
In the cleaning section we do replace values that otherwise would bias the variable. The first part of Data Analysis is to edit the variables so that they fit into the statistical analysis models that we are using. | |||
1. [[Standardization]] - Convert all values in each variable into the same unit. If the values in one variable are different then errors like 1000 gram will be interpreted as one thousand times larger than 1 kg. | |||
1. [[Aggregation]] - We often collect variable disaggregated over categories (income collected as different income categories) or disaggregated over instances (harvest value over multiple crops). Disaggregated data collection is used to improve quality of data collected, but in the analysis we are often interested in the aggregated value. | |||
== Outputting the Result of the Analysis == | == Outputting the Result of the Analysis == | ||
Revision as of 14:07, 26 October 2017
Read First
- include here key points you want to make sure all readers understand
Preparing the Data Set for Analysis
In the cleaning section we do replace values that otherwise would bias the variable. The first part of Data Analysis is to edit the variables so that they fit into the statistical analysis models that we are using.
1. Standardization - Convert all values in each variable into the same unit. If the values in one variable are different then errors like 1000 gram will be interpreted as one thousand times larger than 1 kg. 1. Aggregation - We often collect variable disaggregated over categories (income collected as different income categories) or disaggregated over instances (harvest value over multiple crops). Disaggregated data collection is used to improve quality of data collected, but in the analysis we are often interested in the aggregated value.
Outputting the Result of the Analysis
Different Specific Types of Analysis
Principal Component Analysis
Principal Component Analysis (PCA) is an analytical tool looks to explain the maximum amount of variance with the fewest number of principal components.
Cost Effectiveness Analysis
One type is Cost-effectiveness Analysis