Using PHP to make Web data analysis more advanced

Designing your data analysis and performing more effective and multi-level analysis on Web data than simply counting original data is a key factor for the survival of Web enterprises, the design (and decision-making) of data analysis tests is usually the system administrator and internal

Design your data analysis to do more than simply counting the original

Effective and multi-level analysis of Web data is a key factor for the survival of many Web-oriented enterprises. data analysis and testing design (and decision-making) it is usually the work of system administrators and internal exploitation of Program Design staff, who may not know much about statistics except for being able to make the original count into a table. In this article, Paul Meagher taught Web developers the skills and concepts required to use inference statistics to use Web data streams.

Dynamic websites constantly generate a large volume of data-visiting logs, *** quizzes and survey results, customer summary information, orders, and others, the work of Web developers is not only to create the exploitation programs of these data, but also to develop the exploitation programs and methods that make these data streams meaningful.

Generally, Web developers are not enough to cope with the increasing data analysis requirements generated by governance sites. In general, in addition to reporting various descriptive statistics, Web developers do not have any better way to reflect the characteristics of data streams. There are many statistical inference steps (the method for estimating the overall parameters based on sample data) that can be fully utilized, but they are not currently used.

For example, Web access statistics (compiled according to the current situation) are only counted by frequency of grouping in various ways. The results of tests and surveys are common with raw counts and percentages.

It may be enough for developers to process the statistical analysis of data streams in a relatively simple way. we will see too much during the failover period. After all, there are professionals engaged in more complex data flow analysis; they are statisticians and trained analysts. When organizations require more than descriptive statistics, they can participate.

However, another response is to acknowledge that the increasingly deep understanding of inference statistics is becoming part of the work description of Web developers. Dynamic websites are generating more and more data. it turns out that it is the obligation of Web developers and system administrators to turn these data into useful knowledge.

I advocate the latter approach. This article aims to help Web developers and system administrators learn (or review, if knowledge is forgotten) design and analysis techniques required to apply inferential statistics to Web data streams.


Link Web data with lab design

Applying inferential statistics to Web data streams requires not only learning mathematical knowledge as the basis for various statistical tests. It is equally important to associate the data collection process with the key differences in the experiment design: What is the measurement scale? How is the sample representative? What is overall? What are the assumptions being tested?

To apply inference statistics to Web data streams, we need to first regard the results as inherent in the experiment design, and then select the analysis process for the experiment design. Even if you may think that taking Web *** quizzes and visiting log data as the outcome of an experiment is an extra step, it is important to do so. Why?

1. this will help you select an appropriate statistical testing method.
2. this will help you draw appropriate conclusions from the collected data.