As of 2021, China’s GDP per capita is only $12,539 vs. US’ $69,231, about 1/6 of the US GDP per capita. China’s GDP per capita in 2021 is about US’ level in 1980, or 41-years behind. Based on the growth trends during the past 17 years, it will take China until 2073 to reach the US level, or about 52 years later. See the growth forecast in the Chart below
GDP per cap Growth Forecast China vs. US
China’s total GDP in 2021 is $17,700 bil., vs. US’ $22,990 bil., or about the US level in 2014. China is 7 years behind the US. Based on the current GDP growth of both countries since 2005, China will catch the US in about 2038, or 16 years from 2022. All dollar figures are based on current value. See the forecast growth trends in the chart below.
GDP Growth Forecast China vs. US
Of course, the forecasts assume the future world economic outlook, trade, and the two countries’ social conditions for the years to come to stay comparable in the years between 2005-2021, and between 1980-2021 for the per-cap GDP. Disruptive events are very likely given the frightful dynamic shift many developed countries worked hard to stop. Nevertheless, the general demand from developing countries for better living standards will drive world growth in the long run. China as one of the poor developing countries will enjoy abundant catch-up work for the next three hundred years.
The following main skills are essential for researchers and technology innovators:
Data summarizing knowledge
Uncertainty and quantification of uncertainty
Design and analysis of experimental data
None of these are either trivial or easy. We will discuss in separate posts the above topics for practical application that will provide immediate benefits. Further study is always welcome such as through university courses or reading advanced texts. In each of the posts, we will first summarize the basic knowledge, then illustrate how this knowledge may be applied in the real world setting using one or multiple scientific and technological application examples.
1. Data Summarization Basics
Data Summarizing Knowledge is the basic skill for all data analysis methods. A good understanding of the data provides a foundation for locating the best method to tackle scientific and technological problems. To understand data, the first step would be to check on
Types of the data (numerical, categorical, or a mix of all)
Structure of the data (a series, multiple series such as in a table, unstructured such as texts or images)
For numerical data, to summarize the data we need to focus on
The center of the data (mean, median, mode, quantile)
The variation of the data (variance, max, min, range)
The distribution pattern (symmetric vs. tailed, the direction of skewness)
For categorical data, to summarize we need to check
The frequencies or relative frequencies of each category
If the data contains multiple series such as those usually appear in a table, in addition to the above actions on each of the individual series we need to check the statistical relationships between the series (columns or variables in a table) as well. The most common statistical relationship is the linear correlation. A linear correlation exists between numerical series, between numerical and categorical series, between categorical and categorical series. More about that will be described later. A complete correlation matrix helps us understand which two series are closely related. Note this is just to gain very basic knowledge, there are many relationships that are hidden quite deep, we will need more advanced methods to discover, which we will introduce later. Linear correlation paints a direct picture of the association between the series. Often it tells us how these series are related.