Statistics and Data Analysis Techniques

MDM in Pharma

Hard data has nothing or little relevance without the proper tools and techniques to be able to analyze the data. One of the crucial principals of Data Management is the use of these techniques which bring out patterns / useful trends to help firms figure out which customers to target, which segments are likely to increase sales, which geography, which socio-economic class, which time of the year, which festivals / occasions to watch for and the list is endless. All this information can be obtained by using techniques essentially from a field of Mathematics called Statistical Analysis.

In the simplest definition, Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. We will look at two of the simples techniques covered in statistics that are integral to Data Management and Data Analysis.

Data Analytics: Basic Mathematical Techniques:

1. Frequency Distribution is a representation of the frequency of the various outcomes in a sample data collection. Frequency Distribution is one of the fundamental tools for (https://www.incedoinc.com/lifesciences) Data Management in Pharma, data mining and analytics. Frequency denotes the number of times the values of a parameter within a certain group occur. It therefore summarizes the distribution of values in the given data set. E.g. Number of people in a certain age group or the count of people belonging to a particular ethnicity etc. All this can be represented in Histograms, line charts, pie charts and bar charts. One can look at the histograms for the weekly stock returns or monthly returns to show you the trend.

2. Descriptive Statistics – another mathematical tool which is a simple yet potent method to provide a summary or, as the name suggests, “describe” a given data set. Descriptive Statistics is widely used by Data Management teams as a technique to measure the central tendency and dispersion of data. Mean, median and mode are the three measures of central tendency. We can also think of this as the centre of gravity of the data distribution.

 Mean is the average value of the data set. Mean = Sum of all observed values / number of instances of the observed values. It is one of the most popular measures of center of gravity of the data set when the data set does not have any outliers.  Median is the value in the middle when all the values are lined in order, increasing or decreasing order. If the data set has odd number of values then Median is the middle value and if there are even numbers of values, the median is the average of the two numbers in the middle. It is useful when the data set has outliers and values distribute unevenly.  Mode is useful when asked to find the most sought after item. In a ordered data set, the mode is the most frequently occurring value in the set.

Dispersion refers to the spread of the values around the central tendency. There are two basic measures of dispersion, the range and the standard deviation. The bigger the range and bigger the standard deviation, the more dispersed the values are.

 Range is the difference of the maximum value and the minimum value for the variables in a given data set.

 Standard deviation, again, is an accurate indicator of Dispersion or how much variation the value exits from the mean. Variance is the average of squared difference from the mean. Standard deviation is the square root of variance. The Standard Deviation is a more accurate and detailed estimate of dispersion than the Range since an outlier will exaggerate the range. The Standard Deviation shows the relation that set of scores has to the mean of the sample. Descriptive statistics provides a powerful summary that enable comparisons while also simplifying and reducing data into a manageable form. Data Management and Analysis companies extensively use these basic tools for analyzing and simplifying big sets of data.

At Incedo Inc, Data Management experts work with the clients to use these techniques and come out with the relevant signals from hard, raw data. Using Descriptive Statistics techniques, Data Management and (https://www.incedoinc.com/biandanalytics) BI & Analysis teams can describe or summarize data in an effective way so that meaningful patterns emerge from it. A combination of various tools and techniques available for Data management and analysis in the market help clients get a ROI and drive growth and revenue.

Comments? Questions? Email Here

© HowtoAdvice.com