Statistical analysis of data

• Statistical data analysis is a procedure for performing various statistics operations. It is a kind of quantitative research, which seeks to quantify the data and typically, applies some form of statistical analysis. Quantitative data basically involves descriptive data, such as survey data and observational data.

• Statistical data analyses generally involve some form of statistical tools that a layman cannot perform without having any statistical knowledge. They are various software packages to perform statistical data analyses. This software includes Statistical Analyses System (SAS), Statistical Package for the Social Sciences (SPSS), Stat soft, etc. Data in statistical data analysis consists of the variable(s). Sometimes the data is univariate or multivariate. Depending upon the number of variables, the researcher performs different statistical techniques.

• If the data in statistical data analysis is multiple in numbers, then several multivariates can be performed. These are factor statistical data analyses discriminant statistical data analyses, etc. Similarly, if the data is singular in number, then the univariate statistical data analysis is performed. This includes a t-test for significance, z-test, f-test, ANOVA one way, etc.

• The data in statistical data analysis is basically if 2 types, namely, continuous data and discreet data. the continuous data is the one that cannot be counted. For example, intensity if a light can be measured but cannot be counted. The discreet data is the one that can be counted. For example, the number of bulbs van be counted.

• The continuous data in statistical data analysis is distributed under continuous distribution function, which can also be called the probability density function, or simply pdf.

• The discreet data in statistical data analyses are distributed under continuous distribution function, which can also be called the probability density function. or simple pmf.

• We use the word 'density' in continuous data on statistical data analyses because density cannot be counted, but can be measured. We use the word 'mass' in discreet data of statistical data analyses because mass cannot be counted.

•  There are various pdf's and pmf's in statistical data analysis that helps 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.

• These distributions in statistical data analyses 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 test 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-parameters data - the data that consists of no parameters.

• Traditional methods for statistical analysis - from sampling data to interpreting results - have been used by scientists for thousands of years. But today's data volumes make statistics ever more valuable and powerful. Affordable storage, powerful computers, and advanced algorithms have all led to increased use of computational statistics.

• Whether we are working with large data volumes or running multiple permutations of our calculations, statistical computing has become essential for today's statistician. Popular statistical computing practices include -

1. Statistical Programming - From traditional analysis of variance and linear regression to exact methods and statistical visualization techniques, statistical programming is essential for making data-based decisions in every field
2. Econometrics - Modeling, forecasting, and simulating business processes for improved strategic and tactical planning. This method applies statistics to economics to forecast future trends.
3. Operations Research - Identify the actions that will produce the best results - based on many possible options and outcomes. Scheduling simulation and related modeling processes are used to optimize business processes and management challenges.
4. Matrix Programming - Powerful computer techniques for implementing your own statistical methods and exploratory data analysis using row operation algorithms.
5. Statistical Visualization - Fast, interactive statistical analyses and exploratory capabilities in a visual interface can be used to understand data and build models.
6. Statistical Quality Improvement -  A mathematical approach to reviewing the quality and safety characteristics for all aspects of production.