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Descriptive Statistics

This article mainly shows the use of descriptive analysis in the researches, where the researchers and data analysts are widely using descriptive analysis for analysing the trends. Descriptive statistics is a summary statistic, which quantitatively describes or summarizes the features from a collection of information, where the researchers use descriptive statistics as the process of using and analysing those statistics.

What is Descriptive Statistics?

The researchers choose descriptive analysis for the researches in different fields including market research, operational activities, finance and management as well as health and social care to analyse the quantitative data and information through statistical consulting, evaluation and description. There are graphical and pictorial methods under descriptive analysis which is widely utilized for describing the data and information, gathered from the participants. The graphs and charts are utilised to show the trends and represent the data in a graphical and tabular format so that the researchers and readers can understand the simple explanation of the gathered data and information through graphics and pictorial expression.

The main purpose of utilised descriptive analysis is to highlight the potential relationships between the variables and to provide basic information about the variables in a dataset. Additionally, the three most common measures of descriptive statistics are such as central tendency, dispersion, and association. Histograms, Scatter plots, Geographical Information Systems (GIS) and Sociograms are the common ways to represent the statistical data and conduct descriptive analysis to analyse the internal relationship between the variables. The scatter plots display the relationship between two quantitative or numeric variables by plotting one variable against the value of another variable. It is beneficial to identify the distribution of the variables and analyse the relationship between the dependent and independent variables in the data set. For example, one axis of a scatter plot could represent height and the other could represent the weight of the individual. Each person in the data would receive one data point on the scatter plot that corresponds to his or her height and weight.

Measures of central tendency are the most basic and it is often the most informative description of a population's characteristics. The three measures of central tendency are mean, median and mode. The sum of a variable's values divided by the total number of values is the mean value and on the other hand, the median represents the middle value of a variable. The value that occurs most often is known as a model. Moreover, the measures of dispersion provide authentic information about the spread of a variable's values. There are four key measures of dispersion, such as Range, Analysis of variance (ANOVA), Standard Deviation and Skew. The range is simply the difference between the smallest and largest values in the data set where the differences are measured between the values at 75% and 25% of the data. Variance is commonly utilised for dispersion and it is mainly calculated by taking the averages of the squared differences between each value and the mean. Standard deviation is just the square root of variance. Skew represents whether some values are different from the majority of the values. For example, the variables are positively skewed when the extreme values are higher than the majority of the values in the data set and the variable is negatively skewed if the extreme values are lower than the majority of the values.

Descriptive statistics are utilised to describe or summarise data in ways that are meaningful and useful. The quantitative data are represented in a graphical, tabular or pictorial way to represent the data and interpret it simply. On the other hand, the mean, standard deviations, as well as variance, are also utilised in the descriptive statistics where the researchers can utilise an Excel spreadsheet or a for conducting a descriptive analysis.

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