Sample Statistics Paper on Jobs Increasing or Decreasing between 2019 and 2029

The U.S Bureau of Labor Statistics contains data for the fastest growing occupations and fastest declining occupations. It displays data in 2019 and is projected in 2029. Data recorded describes how some areas are growing and how fast other sites are declining to lead to job loss. The data projections I clustered the data I obtained to examine the fastest growing and fastest declining occupations to address the following questions;

  • Among all the industries I obtained, which was the most evident trend? Whether it was a decline or increase in the occupations.
  • What are the primary industries in the growing occupations?
  • What are the primary industries in declining occupations?

The objectives of our study include;

  • To determine the average number of jobs in the fastest growing occupations between 2019 and 2029.
  • To determine the average number of jobs in the fastest declining occupations between 2019 and 2029.
  • To obtain which group of occupations will have more jobs by 2029.
  • To know which occupations will have more demand in 2029.

 

 

Data

Population 1: Fastest declining occupations

Data (from excel)

n =30

Numbers (In thousands)

 

Descriptive statistics for fastest declining occupations (from excel)

Population 2: Fastest growing occupations.

Data 2 (From excel)

n =30, Numbers in thousands

Descriptive statistics for the fastest growing occupations:

 

Methodology

Problem Statement:

To determine whether there will be more jobs declining or increasing between 2019 and 2029.

Variable I studied

Number of jobs in 2019 and the projected number of jobs in 2029 in different occupations

 

The population

I obtained my data from the U.S Bureau of Labour Statistics.

Population 1: Fastest declining occupations 2019 and 2029.

Population 2: Fastest growing occupations 2019 and 2029.

How I chose my sample size

I collected my data from the two populations by clustering and randomization.

The sample size I used for each of the two groups I studied was equal to 30.

Sampling Methodology

I used clustered sampling and simple random sampling. I did cluster first because the data in the two populations was much with multiple variables. I picked the employment data for 2019 and 2029 and the types of occupations. I then randomly picked professions from each population with the number of jobs in the two years to obtain a sample of 30. I used simple random sampling because every occupation has an even probability of being chosen despite their size in this method.

Results

The mean for the fastest declining jobs is equal to 49,125, while the mean for the fastest growing jobs is equal to 346,900.

I conducted a paired two-sample test for the means.

Since the t-score is less than the p-value of 0.05, our means are statistically significant. We can confidently say that the average number of jobs in the fastest growing occupations is higher than the average number of jobs in the fastest declining occupations.

Hypotheses

We need to test whether the average number of jobs in the fastest declining occupations is higher than the average number of jobs in the fastest increasing jobs.

Hypothesis testing allows us to conclude the entire population of occupations from a representative sample, in our case, 30 from each population.

 

Null Hypothesis

H0: The average number of jobs in the fastest declining occupation is more than the average number of jobs in the fastest growing occupations.

Alternative Hypothesis

H1: The average number of jobs in the fastest declining occupations is not more than the average number of jobs in the fastest growing occupations.

For our sample of 30 in each population, assuming a 95% confidence interval, which means that there is a 95% chance that the average number of jobs in fastest growing occupations will exceed the average number of jobs in fastest declining occupations,

In our research, we take a sample of 60 occupations and obtain the average number of jobs between 2019 and 2029,

For a 95% confidence interval, the average number of jobs in the fastest growing jobs should exceed the average number of fastest declining jobs by at least 95% of the total number of occupations.

=95% of 60= 57

Hence, if the average number of fastest growing occupations exceeds the average number of jobs in the fastest declining occupations by 57, we reject the null hypothesis.

Thus, we can confidently say that the average number of jobs in the fastest growing occupations will be more than the average number of jobs in the fastest declining fields between 2019 and 2029.

 

Conclusion

From my study, the average number of jobs in the fastest growing occupations exceeds the average number of jobs in the fastest declining occupations. The mean in the fastest declining occupations was way less than of the fastest growing occupations. Hence, we reject our null hypotheses because our study clearly shows that by 2029, the jobs in the fastest growing occupations will be more compared to those in the fastest declining occupations.

This implies that several of the fastest growing occupations like healthcare occupations and computer occupations will demand more.