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From the image you uploaded, the table shows the median annual earnings and number of male and female employees for different years, but it does not seem to directly indicate when male employees will surpass females in numbers. However, it provides data on the number of male and female employees from 1948 to 2013.

Let's analyze this trend:

- In 1948, the number of male employees is higher than female employees (47.370 million males vs. 22.725 million females).
- Over time, the number of both male and female employees increases, but female employees steadily close the gap with males.
- By 2013, males still have a slightly larger number (108.706 million males vs. 109.956 million females).

If the trend continues based on this data, the number of females may surpass males in future years, but from this data alone (until 2013), males still slightly outnumber females.

To provide a more precise forecast, we would need to analyze growth trends of both genders' employment rates using extrapolation techniques. Would you like me to perform such a calculation or analyze this further? 

### Follow-up Questions:
1. Would you like to forecast the employment rates of males and females beyond 2013 using growth trends?
2. Are you interested in a year-by-year comparison of growth rates between male and female employees?
3. Do you want to calculate the specific year when female employees are projected to surpass males based on this data?
4. Should we also consider the impact of earning ratios on workforce dynamics over time?
5. Would you like to explore more detailed demographic factors influencing these trends?

**Tip:** To predict when trends will change, it's important to analyze year-over-year growth patterns and calculate an expected intersection point using statistical models like linear regression.

Analyze the employment growth trends of male and female workers from 1948 to 2013. Understand how to predict when female employees may surpass males using linear regression and growth rate analysis. This problem covers growth trends and statistical models, suitable for students in Grades 10-12.

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