Effects of Change

Employment Statistics

The variable, in this case, is the percentage of individuals in the civilian labor force from data collected from West Palm Beach Florida. In this case, the data under comparison is for the years 2013 and 2018.

2013 Employed = 65.4% representing 55,458 people out of 102,433.

2019 Employed = 63.2% representing 57,815 of 109,767 people (Bureau, U.2022).

I found a difference in the variables' values because the percentage of persons employed rose from 65.4% in 2013 to 63.2% in 2019.

If I were to test the difference statistically, I would write the null hypothesis as follows:

Let P1= percentage of employed people in 2013

P2= percentage of employed people in 2019

Therefore, the Null hypothesis formula is P2-P1=0

To substantiate, in 2013, sample number n1= 102,433

                                    P1= 65.4%

In 2019, sample number n2= 109,767

                                    P2= 63.2%

Therefore; Test statistic (SE)

Thus, 0.654-0.632/√(0.64(1-0.64)(1/102433+1/109767))

Thus the statistic test(SE) is 72.392

Z statistic=(p1-p2)/SE

(0.654-0.632)/72.392= 0.0003039

Whereby for the test statistic p-value is read as 0(P=0).

As a result, the test result is said to be significant because the significant level is greater than the p-value, or the null hypothesis is rejected. We can conclude that the result is significantly different.

The difference is significant because it suggests that the unemployment rate increased or, in other words, the employment rate decreased.

The changes in value show that the economy is in the wrong direction, unemployed people spend less, and hence the society's economy is not good.

Reference

Bureau, U. (2022). Data Profiles. Census.gov. Retrieved 16 August 2022, from https://www.census.gov/acs/www/data/data-tables-and-tools/data-profiles/2017/.