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Has your business's profit rate decreased? Did your newly opened shop bring in more income this season? Have your customers expressed their dissatisfaction lately? You can collect historical data and apply descriptive analysis to find out the reasons. It will help you to identify key factors about the event. It will show your good or weak sides.
In this article, you will see how you can benefit from descriptive analysis. It is not a complicated research method. So you shouldn't need to worry about it. But first, you need to know its definition, so start from the heading below.
What is descriptive analysis?
Descriptive analysis is a data analysis technique using historical data to describe and demonstrate a condition.
It reveals the patterns and relationships of data points in the simplest way. That's why it gives the quickest response to find out why an event happened. On the other hand, because of this simplicity, researchers sometimes need to make manual inputs. However, they can achieve productive results when they work systematically.
Descriptive statistical analysis is often called the most basic analysis for summarizing data. That's why it's frequently used as a stepping stone to other types of comprehensive analysis, such as diagnostic analysis, predictive analysis, and prescriptive analysis.
Descriptive analysis types
There are three types of descriptive analysis tools that you can utilize. Although these are generally associated with univariate analysis methods, you can also use them with statistics methods such as bivariate, multivariate, and time series.
Since a lot of calculations are required, it is used with many calculation programs.
For example, descriptive analysis in Excel allows users to calculate summary statistics of their dataset. Below, you can check each of the types with descriptive analysis examples to get a better understanding of the topic:
Types of descriptive analysis
1. Measure of frequency distribution
It can be the most basic summary of the dataset. It is used to show how often each value occurs in a dataset. However, you should remember that it is slightly different than the mode. It will be explained with an example.
Example: There are four different phone brands in the market. These are A, B, C and D brands. The daily sales amount of brand A phones is 3, B phones is 4, C phones is 5 and D phones is 6. These are, respectively, the frequency distribution. On the other hand, there is no mode in this dataset because there is no frequent number. With this simple process, you can identify patterns and then make informed decisions.
2. Measure of central tendency
It is used to find the average of a dataset. It also serves as a body for using other measures. There are three common measures you can use: mean, median, and mode.
- The mean is the mathematical average of values. You add all numbers together and divide them by the total number of values. You can apply the sample mean formula if you want to examine a specific group in your analysis.
- The median is the middle point in the dataset. You can use formulas based on odd and even numbers to find the median of a dataset.
- Lastly, the mode is the most frequent value. There is no rule that there will always be a mode. Sometimes, there are no modes, or there is more than one mode.
Example: Assume that you collected data about your customers’ ages. The average of their ages will show you the mean. For example, it is 32. Now, you analyze the mode and find its value to be 30. Now, you can use this data. You may take action to increase the satisfaction of your customers in their 30s by focusing on them, or you can consider this analysis as a start if you want to reach wider age ranges.
3. Measure of variability/dispersion
It finds the dispersion of data points by describing how far they are from the distribution point and each other. There are four common measures you can use: range, interquartile range, standard deviation, and variance.
- The range is the difference between the highest and the lowest values.
- The interquartile range is the middle half of a distribution.
- The standard deviation is the average distance from the mean.
- The variance is the average of squared distances from the mean.
Example: You are considering providing standard deviation to measure and prove your data quality. The data you have contains the product prices that customers prefer most. These are worth $20, $25, $30, $35, and $40, respectively. When you average it, it becomes 30. When you look at how much the other values deviate from the mean value, you find that the variance is 62.5, and the standard deviation is 7.9.
Frequently asked questions about descriptive analysis
Do you have more questions about descriptive analysis? Do you want to make yourself proficient in this type of analysis? You can check out the most common inquiries around it below.
Puede utilizar el análisis descriptivo en varios escenarios. Por ejemplo, puede analizar el tráfico del sitio web de su empresa para averiguar dónde le encuentra la gente en la red. Puede comprender las tendencias de los productos examinando los historiales de compra en su tienda. Así, puede tomar medidas para satisfacer a los clientes en función de la demanda.
Ambas son técnicas adecuadas para realizar análisis descriptivos. Sin embargo, algunos científicos de datos pueden considerar que el análisis descriptivo cuantitativo es más concreto y científico. Sin embargo, el hecho de que el análisis cualitativo sea adoptado incluso por grandes empresas demuestra que este tipo también es esencial.
En primer lugar, las medidas de frecuencia. Se trata de expresar la frecuencia de un acontecimiento generalmente en expresiones numéricas. En segundo lugar, las medidas de tendencia central. El objetivo es encontrar la tendencia general calculando la media, la mediana y la moda. En tercer lugar, las medidas de dispersión. Su objetivo es medir la distribución de los datos empleando el rango o la desviación típica. Y por último, medidas de posición. Trata de medir la relación de un valor con otros valores y con su entorno.
El análisis descriptivo es aplicable en todos los ámbitos en los que se disponga de datos. Por ejemplo, las empresas pueden utilizarlo para obtener información sobre sus clientes y mejorar el desarrollo de sus productos y sus estrategias de marketing. En la educación puede utilizarse para estrategias y desarrollos sobre estudiantes, exámenes y cursos. En finanzas, puede utilizarse para obtener información sobre acciones, inflación, beneficios empresariales, etc.
El análisis descriptivo es el tipo más sencillo y básico de análisis de datos. Lo es porque transmite lo que es tal cual, sin añadir añadidos ni interpretaciones. Por lo tanto, si necesita un diagnóstico rápido de lo ocurrido, el análisis descriptivo siempre estará a su disposición. La aportación humana es más significativa que lo que revela el análisis. Por lo tanto, el análisis descriptivo se utiliza a menudo para el análisis no exhaustivo de datos.
Key points to take away
Descriptive analysis is the fundamental analysis method for business data analysts. It is essential to use it in order to understand what the data is and to make evaluations accordingly. It is waiting to be used to unearth fundamental phenomena and assist businesses in historical data research.
In this article, descriptive analysis is explained, as well as its various types. Other important points are also mentioned in the FAQ section. After this, you can now use descriptive analysis to understand the data. You're ready to utilize historical data to make insightful decisions.
Atakan is a content writer at forms.app. He likes to research various fields like history, sociology, and psychology. He knows English and Korean. His expertise lies in data analysis, data types, and methods.
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