Descriptive Statistics
Descriptive statistics use graphical and numerical procedures to summarize and transform data into information whereas, Inferential statistics provide bases for forecasts, predictions, estimates and are used to transform information into knowledge. For example.
- Based on a survey, 25% of all men dislike football (Inferential Statistics)
- 65% of seniors at local high school applying to college and plan to major in business (Descriptive Statistics)
Mainly, the descriptive statistics gather, sort, summarize data from samples and Inferential statistics uses descriptive statistics to estimate population parameters.
Descriptive statistics is a starting point for knowledge discovery based on data. The knowledge discovery can be made in various ways, such as looking at the pictures, reading some books, etc. Similarly, the statistics also facilitate various tools to discover the knowledge that is contained inside the data. It provides graphical and analytical tools. In the graphical tools, there are various types of plots, such as:
- 2D and 3D plots
- Scatter diagram or plot
- Pie diagram
- Histogram
- Bar plot
- Stem and leaf plot
- Box plot
Whereas in the analytical tools, it contains various kinds of measures such as:
- To measure the central tendency of data using Mean, Median, Mode, Geometric mean, Harmonic mean, Quantiles, etc.
- To measure the dispersion in data using Variance, Standard deviation, Standard error, Mean deviation, Absolute deviation, Range, etc.
- To understand the structure of data using the properties such as symmetricity, skewness, kurtosis, etc.
- To understand the relationships in data using correlation coefficient, rank correlation, multiple correlation coefficient, partial correlation coefficient, correlation ratio, intraclass correlation, linear regression, non-linear regression, etc.
Reference
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