Data analysis

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With the data clearer to visualize, it is time to analyze the information you have.

Here it will be possible to understand graphical anomalies or line trends through the use of statistical knowledge .

In predictive analysis, if the fact to be analyzed is a recurring action, as in the example already cited in this text, it is possible to perform anomaly analysis on the generated graph.

This is the time to understand, for example, the month in which you have the highest rate of plan cancellations, in order to come up with a more effective action plan.

If you want to understand a trend, such as the growth cameroon phone data of your blog traffic, you can use basic statistical strategies to understand what the future holds for your data.

Chart with a basic trend line, based on the valleys (bottom) of the graphic lines.

Statistics
When we talk about statistics in predictive analysis, we take into consideration two well-known techniques in the area: Descriptive Statistics and Inferential Statistics.

Descriptive Statistics aims to summarize and describe a large set of data. From there, it is possible to create measures of central tendency and measures of variability or dispersion.

Inferential Statistics is the study of a sample group to draw conclusions about a larger group. Population surveys are a great example of this technique.

Statistics are essential to have a predictive analysis running correctly.

Modeling
When we bring together all the information acquired so far from predictive modeling techniques , we create the model.

Predictive modeling is the moment when the first ideas about possible future events begin to emerge.

Mathematical and statistical techniques are combined with the data obtained in your company, creating a model to be observed, in which the main answers you want will be easily accessible visually , updating and improving each new information generated.

Model monitoring
After carrying out all the previous steps, it is necessary to maintain monitoring so that the processed data and mainly the information obtained from the modeling continue to be reliable .

In addition to having the answers you need more quickly and objectively to further optimize and improve your process and/or product.