Data Analysis & Correlation
08 August 2024
Understanding Data Analysis and Correlation
Data analysis and correlation involves dissecting, transforming, computing, and visually inspecting data in form to view patterns, find useful information, make predictions, and create conclusions. Data analysis and correlation can be conducted in a variety of ways and has various name depending upon the subject matter that it is applied to. Data can usually be classified as quantitative (or numerical) or qualitative, which implies a characteristic defined by a short description.
Principles of Data Collection
It is important when collecting data that all of the data be included in the analysis – even if some of the data points which do not seem to correlate with the dataset. It should also be noted if any important data is missing or additional data needs to be collected. Once all of the data has been obtained, then data analysis and correlation should begin.
Initial Data Analysis Techniques
When you begin analyzing data, the most useful techniques are descriptive statistics and visualization techniques. It is relatively easy for a beginner to use a combination of these two techniques in order to begin seeing patterns in their dataset. Some common types of descriptive statistics include:
- mean
- standard deviation
- variance
- sum
- min
- max
- range
- percent
- interquartile range
- sum of squares
- skewness
- kurtosis
Visualization Techniques
Sometimes it is easier to visualize patterns in a dataset by graphing the data and grouping certain categories or characteristics together. Some of the graphical techniques used to visualize and assess data are as follows:
- scatter plot
- line plots
- bar charts
- pie charts
- area graphs
- histograms
- box plots
- contour plots
- 3D surface plots
- probability plots
There are many more graphical and statistical techniques that can be used to analyze a dataset.
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