Statistics is basically a science that involves data collection, data interpretation and finally, data validation. Statistical data analysis is a procedure of performing various statistical operations. It is a kind of quantitative research, which seeks to quantify the data. Quantitative data basically involves descriptive data, such as survey data and observational data.
There are various software packages to perform statistical data analysis, such as Intellectus Statistics, Statistical Analysis System (SAS), Statistical Package for the Social Sciences (SPSS), Stat soft, etc.
Data in statistical data analysis consists of variable(s). Sometimes the data is univariate or multivariate. Depending upon the number of variables, the researcher performs different statistical techniques.
If the data consists of multiple variables, researchers can perform several multivariate analyses. These are factor analysis, discriminant analysis, etc. Similarly, if the data is singular, researchers perform univariate analysis. This includes t test for significance, z test, f test, ANOVA one way, etc.
The data here, is basically of 2 types, namely, continuous data and discreet data. Continuous data refers to data that you cannot count. For example, you can measure the intensity of light, but you cannot count it. Discrete data refers to data that you can count. For example, you can count the number of bulbs.
It distributes continuous data under the continuous distribution function, also called the probability density function, or simply pdf.
It distributes discrete data under the discrete distribution function, also called the probability mass function, or simply pmf. We use the word ‘density’ in continuous data analysis because we cannot count density, but we can measure it. We use the word ‘mass’ in discrete data analysis because we cannot count mass.
There are various pdf’s and pmf’s in statistical data analysis. For example, Poisson distribution is the commonly known pmf, and normal distribution is the commonly known pdf.
These distributions in statistical data analysis help us to understand which data falls under which distribution. If the data is about the intensity of a bulb, then the data would be falling in Poisson distribution.
There is a major task in statistical data analysis, which comprises of statistical inference. The statistical inference is mainly comprised of two parts: estimation and tests of hypothesis.
Estimation in statistical data analysis mainly involves parametric data—the data that consists of parameters. On the other hand, tests of hypothesis in statistical data analysis mainly involve non parametric data— the data that consists of no parameters.