Project Title: 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.
INTRODUCTION TO STATISTICS 3
Data
Population 1: Fastest declining occupations
Data (from excel)
n =30
Numbers (In thousands)
Sample 1- Data fo fastest declining jobs20192029average
Word processors and typists52.733.543.1
Parking enforcement workers8.15.26.65
Nuclear power reactor operators5.33.44.35
Watch and clock repairers3.22.12.65
Cutters and trimmers, hand9.86.98.35
Telephone operators53.64.3
Travel agents8260.871.4
Data entry keyers172.4130151.2
Electronic equipment installers and repairers, motor vehicles10.489.2
Switchboard operators, including answering service69.954.162
Manufactured building and mobile home installers2.92.22.55
Timing device assemblers and adjusters1.311.15
Legal secretaries and administrative assistants171.8133.8152.8
Postmasters and mail superintendents13.410.511.95
Forging machine setters, operators, and tenders, metal and plastic16.41314.7
Prepress technicians and workers30.22427.1
Executive secretaries and executive administrative assistants593.4472.4532.9
Floral designers51.841.446.6
Door-to-door sales workers, news and street vendors, and related workers72.958.365.6
Grinding and polishing workers, hand2923.426.2
Photographic process workers and processing machine operators12.39.911.1
Refractory materials repairers, except brickmasons0.80.70.75
Desktop publishers10.48.49.4
Drilling and boring machine tool setters, operators, and tenders, metal and plastic11.29.110.15
Nuclear technicians6.75.46.05
Pressers, textile, garment, and related materials38.331.134.7
Coil winders, tapers, and finishers1310.511.75
Milling and planing machine setters, operators, and tenders, metal and plastic19.215.617.4
Postal service mail sorters, processors, and processing machine operators98.580.989.7
Aircraft structure, surfaces, rigging, and systems assemblers43.936.340.1
total average1475.85
INTRODUCTION TO STATISTICS 4
Descriptive statistics for fastest declining occupations (from excel)
Column1
Mean49.195
Standard Error18.2115
Median13.325
Mode#N/A
Standard Deviation99.7484935
Sample Variance9949.76196
Kurtosis20.239352
Skewness4.24937868
Range532.15
Minimum0.75
Maximum532.9
Sum1475.85
Count30
Largest(1)532.9
Smallest(1)0.75
Confidence Level(95.0%)37.2466996
INTRODUCTION TO STATISTICS 5
Population 2: Fastest growing occupations.
Data 2 (From excel)
n =30, Numbers in thousands
Fastest Growing ocuupations20192029Average
Wind turbine service technicians711.39.15
Nurse practitioners211.3322266.65
Solar photovoltaic installers1218.115.05
Occupational therapy assistants47.163.555.3
Statisticians42.757.550.1
Home health and personal care aides3,439.704,599.204019.45
Physical therapist assistants98.7130.9114.8
Medical and health services managers422.3555.5488.9
Physician assistants125.5164.8145.15
Information security analysts131171.9151.45
Data scientists and mathematical science occupations, all other33.243.438.3
Derrick operators, oil and gas1215.713.85
Rotary drill operators, oil and gas20.926.623.75
Roustabouts, oil and gas58.573.165.8
Speech-language pathologists162.6203.1182.85
Operations research analysts105.1131.3118.2
Substance abuse, behavioral disorder, and mental health counselors319.4398.4358.9
Forest fire inspectors and prevention specialists2.32.82.55
Cooks, restaurant1,417.301,744.601580.95
Animal caretakers300.7369.5335.1
Service unit operators, oil and gas51.763.657.65
Marriage and family therapists66.280.973.55
Computer numerically controlled tool programmers25.731.328.5
Film and video editors38.346.542.4
Software developers and software quality assurance analysts and testers1,469.201,785.201627.2
Genetic counselors2.63.22.9
Physical therapist aides50.661.355.95
Massage therapists166.7201.1183.9
Health specialties teachers, postsecondary254306.1280.05
Helpers–extraction workers16.920.318.6
TotalAverages10407
INTRODUCTION TO STATISTICS 6
Descriptive statistics for the fastest growing occupations:
Column1
Mean346.8983
Standard Error145.7771
Median69.675
Mode#N/A
Standard Deviation798.454
Sample Variance637528.8
Kurtosis16.18873
Skewness3.844561
Range4016.9
Minimum2.55
Maximum4019.45
Sum10406.95
Count30
Largest(1)4019.45
Smallest(1)2.55
Confidence Level(95.0%)298.1476
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
INTRODUCTION TO STATISTICS 7
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.
INTRODUCTION TO STATISTICS 8
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.
t-Test: Paired Two Sample for Means
Variable 1Variable 2
Mean49.195346.8983
Variance9949.762637528.8
Observations3030
Pearson Correlation-0.01892
Hypothesized Mean Difference297.695
df29
t Stat-4.0434
P(T<=t) one-tail0.000178
t Critical one-tail1.699127
P(T<=t) two-tail0.000356
t Critical two-tail2.04523
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.
INTRODUCTION TO STATISTICS 9
Null Hypothesis
H 0: 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
H 1 : 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.
INTRODUCTION TO STATISTICS 10
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.
INTRODUCTION TO STATISTICS