In this new series, we explore why scientists are more likely than other occupations to be employed in science.
In the first installment, we looked at the different types of science occupations.
In this second installment, the focus is on why scientists might be more likely in certain fields.
The focus of the next installment will be on why science jobs are more valuable than any other occupations.
This time, we’ll take a closer look at why scientists tend to be more valued than other professions.
To answer this question, we examined the employment history of each scientist, using data from the Bureau of Labor Statistics.
We then compared the jobs held by each scientist with the careers held by all workers who had a similar number of years of experience.
We used data from two sources: the BLS and the Census Bureau.
We also compared the careers of scientists with those of the general population, using information on years of education.
To do this, we used the employment records of those who had completed the three-year college program and those who were working part time during that time.
We created a dataset of data from individuals who have held a job in any of these occupations for at least 10 years, including those who have no previous career.
We took the total number of people who have completed the college-level program (i.e., no formal degree), and multiplied that number by the number of the people in the dataset who are employed in these occupations.
We added this number to the total population to calculate the number that are employed as scientists.
We averaged the jobs in each occupation to determine the total employment of scientists.
Next, we created a regression model that compared the average number of scientist jobs for each of the occupations with the average salary of the scientists.
This is done by looking at the correlation between each occupation and the average annual salary of a scientist in the occupation.
To calculate the correlation, we multiplied the average yearly salary of an adult scientist with that of a non-scientist (the difference between the average income and the median salary).
We also took the number for the year of the scientist’s graduation (or, in the case of graduate school, the number at the time of the graduate degree).
This gave us the total annual salary that was earned by scientists in that year.
We repeated this process for all occupations.
The final step in this regression was to determine if there was any statistically significant difference between jobs held and average annual salaries in each of these three categories.
The average annual earnings of a science career for a male and female scientist are shown in Table 1.
Table 1 Average annual earnings for science career.
Table 2 shows the average average annual income for scientists in each field.
Table 3 shows the number and percentage of scientists in a field and the number who are unemployed.
Finally, Table 4 shows the percentage of the total workforce employed by each field of science.
Table 4 Percentage of total workforce that is employed by a science field.
Source National Geographic article We then looked at how many jobs scientists held.
To find the average hourly pay for a scientist, we took the average pay for the occupations in which they held the job.
This number is the wage paid to the average person who held the same job for the full year.
To see the average salaries of each occupation, we added the total salary earned by all people in that occupation to the equation.
To determine the average median annual salary for each scientist in each discipline, we divided that number in half.
For instance, for a job that pays $50,000, the median annual wage for a doctor would be $60,000.
So, the average wage of a doctor is $60K per year.
This figure for a typical doctor is shown in Figure 1.
Figure 1 Median annual salary by science field and occupation.
Figure 2 shows a plot of the average weekly pay for scientists.
Table 5 shows the median weekly pay of scientists over the last 10 years.
Figure 3 shows average weekly earnings by year.
Figure 4 shows average earnings by field and by field specialty.
Figure 5 shows average annual wage by year for scientists with all careers.
Figure 6 shows average average weekly wages by field of study.
Figure 7 shows average monthly pay for all science careers.
We looked at whether there was a statistically significant trend for different types and types of jobs for different fields.
Figure 8 shows that the pattern is fairly consistent, with scientists having more of the highest-paying occupations than the lowest-paying fields.
Table 6: The median weekly and annual pay of science jobs.
Table 7: The average weekly and monthly pay of all science jobs for the last decade.
Table 8: The percentage of all workers that are scientists.
Figure 9 shows that a trend is statistically significant in both the data and the regression models.
The correlation coefficient for the average monthly salary of scientists is 0.76, with the 95 percent confidence interval of 0.64 to 0.88.
This correlation coefficient is statistically