Quantitative data analysis of a large collection of data is made possible using certain numerical computations that give an understanding of the nature of the data collected and make it easier to interpret their trend. Descriptive statistics and inferential statistics are the two methods used for this purpose.
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Descriptive statistics describes or summarizes the basic features or characteristics of the data. It assigns numerical values to describe the trend of the samples collected. It converts large volumes of data and presents it in a simpler, more meaningful format that is easier to understand and interpret. It is paired with graphs and tables; descriptive statistics offer a clear summary of the data’s complete collection.
Descriptive statistics indicate that interpretation is the primary purpose, while inferential statistics make future predictions for a larger set of data based on descriptive values obtained. Hence, descriptive statistics form the first step and the basis of quantitative data analysis.
There are four major types of descriptive statistics used to measure a given set of data characteristics.
This measures how often a particular variable occurs in the distribution. It can be measured in numbers or percentages and shows how frequently a response or variable occurs.
Measures of central tendency indicate the average or the most common variable in the data set. They identify certain points by computing the mean, median, and mode.
This shows how spread out the responses in the data set are. It helps identify the gap between the highest and lowest values and how far apart individual values are from the mean or the average. Measures of variation are calculated using the range, standard deviation, and variance.
This measures how individual values are positioned with one another. This method of calculation relies on a standardized value. Percentiles and quartile ranks indicate the measures of position.
The various descriptive statistics methods used to arrive at the characteristics of the data set include:
Mean is the average of all the values and can be calculated by adding up all the values and dividing the total sum by the number of values.
Mean = Sum of values/Number of values
The median of the set is the value that is at the exact center of the set. If there are two values at the center, their mean is calculated to find the median.
The mode is the value that appears most frequently in the set. Arranging the values in order from lowest to highest helps identify the mode. Any data set can have no mode, one mode, or multiple modes.
The range is the difference between the highest value of the data set and the lowest value. It can be calculated by subtracting the lowest value from the highest value. The range indicates how far apart the values are.
Standard deviation measures the average variability of the values in the data set or how far individual values are from the mean. A large value of the standard deviation indicates high variability. Standard deviation is calculated using six steps:
Variance measures the degree of spread in the data set and is the average of squared deviations from the mean. A squared standard deviation gives the variance.
These methods can be used for univariate analysis, bivariate analysis, or multivariate analysis as needed.
The univariate analysis considers only one variable at a particular time. This allows the examination of each variable in the data set using different measures of frequency, variation, and central tendency.
The bivariate analysis identifies any available relationship between two different variables. The frequency and variability of the two variables are measured together to see if they vary together. The measure of central tendency can also be taken during bivariate analysis.
Multivariate analysis is similar to bivariate analysis within the exception that it takes more than two variables into account to identify any relationship between them.
The most important reason for the wide use of descriptive statistics is that it makes a complex set of data easier to interpret by giving a convenient summary. Here are some examples where descriptive statistics help:
Various descriptive statistics tools can be called on for specific scenarios. Choosing the right tool depends entirely on the objective of the analysis and the type and number of variables at hand.
There are two categories of tools in descriptive statistics:
Descriptive statistics is the basis of any quantitative data analysis process. It gives a simplified picture of the data set, no matter how wide or complex the data, and enables easy interpretation. It is the first step to describing the data and its features. The importance of descriptive statistics lies in its fundamentals as the measures and values obtained through descriptive statistics are essential for any advanced statistical analysis.
Descriptive analytics forms the foundation of quantitative analysis of any set of data. While a single indicator for a large set of data may distort the specifics of the values, it still delivers a convenient and usable summary that indicates the relationship between the variables and allows for essential comparisons.
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