Every day we deal with large amount of data, qualitative and quantitative. Quantitative data could be discrete or continuous. The first step for quantitative data analysis is to evaluate their distribution that could be normal or non-normal. Based on data distribution we can describe data with their proper measures. Based on data distribution we can also choose the proper test for data comparison: parametric and non-parametric statistics. This chapter will describe quantitative data measures (mean, median), data dispersion measures (standard deviation and interquartile range), and tests for data comparison. Moreover appropriate graphs for data description and linear correlation statistics will be described.
Ceresoli, M., Nespoli, L. (2022). Analyzing Continuous Variables: Descriptive Statistics, Dispersion and Comparison. In M. Ceresoli, F.M. Abu-Zidan, K.L. Staudenmayer, F. Catena, F. Coccolini (a cura di), Statistics and Research Methods for Acute Care and General Surgeons (pp. 55-66). Springer Cham [10.1007/978-3-031-13818-8_5].
Analyzing Continuous Variables: Descriptive Statistics, Dispersion and Comparison
Marco Ceresoli
;Nespoli Luca
2022
Abstract
Every day we deal with large amount of data, qualitative and quantitative. Quantitative data could be discrete or continuous. The first step for quantitative data analysis is to evaluate their distribution that could be normal or non-normal. Based on data distribution we can describe data with their proper measures. Based on data distribution we can also choose the proper test for data comparison: parametric and non-parametric statistics. This chapter will describe quantitative data measures (mean, median), data dispersion measures (standard deviation and interquartile range), and tests for data comparison. Moreover appropriate graphs for data description and linear correlation statistics will be described.
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