1. Quantitative analysis and evaluation means that you should use specific mathematical variables (for example, I can use GDP and other indicators to measure the quantity involved in the topic (for example, a country's comprehensive national strength), which is a quantitative process.

2. Then you should build a suitable model to measure these quantities or evaluate them.

Let me give you a simple example.

We are asked to evaluate the teaching quality of a school. The quantity of teaching quality is a very abstract concept. What should we use to evaluate it? Of course, you will naturally think of the enrollment rate. This quantifies the quality of teaching

Then how to evaluate the enrollment rate?

First of all, let's calculate the average enrollment rate of this school in recent years. This can explain the overall situation of further education.

Then calculate the fluctuation of enrollment rate, which can explain the stability of enrollment rate.

Calculate other statistics, such as kurtosis, maximum enrollment rate and so on.

Also, we can build a probability model and look at the probability distribution of the enrollment rate.

For example, I think the enrollment rate of this school obeys the normal distribution, and then the parameters of the normal distribution can be estimated immediately through the average enrollment rate and fluctuation.

Then we have to do model tests, such as probability model tests, k-s, q-q bitmaps, etc., to see whether the enrollment rate obeys a normal distribution (possibly a T distribution).

There are still some steps behind, so I won't go into details.

This is the probability model, and there are many models, such as differential equation model, regression model, combinatorial optimization model and so on.

In fact, to play this kind of thing, it is better to have a math student in the team, and then it is better to have someone who can play programming.