Nonfarm Employees, By State
Why Use This Data Source In Your Models?
Total nonfarm employees measures the number of persons 16 years of age or older working in nonfarm positions. This indicates population size and occupation opportunity.
Nonfarm Employees, By State
Instantly Download this Data Using Our Automated Feature Engineering Tool.
Automated Data Profiling
Suggested Treatment:
Grain Transformation:
Source:
U.S. Bureau of Labor Statistics
Release:
Employment Situation
Units:
Thousands of Persons, Seasonally Adjusted
Frequency:
Monthly
Available Through:
03/31/2025
Suggested Treatment:
The data shows auto correlation and a non-normal distribution. The data should be differenced. While the Order Norm transformation, provides the best normality, the Yeo Johnson variable will also perform well.
Grain Transformation:
Data is able to be distributed by geography but not by time. The roll up method used is Sum.
Auto Correlation Analysis:
Data shows auto correlation indicating a need for differencing
The ACF indicates 1 order differencing is appropriate.
Further differencing is reccommended
Trend Analysis:
The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.34 p-value = 0.01 indicates that the data is not stationary.
Distribution Analysis:
The Shapiro-Wilk test returned W = 0.94 with a p-value =0.00 indicating the data does not follow a normal distribution.
A skewness score of -0.67 indicates the data are moderately skewed.
Hartigan's dip test score of 0.04 with a p-value of 0.26 inidcates the data is unimodal
Statistics (Pearson P/ df, lower => more normal)
Auto Correlation Function
Auto Correlation Function After Differencing
Partial Auto Correlation Function
Seasonal Impact
Seasonal and Trend Decompostion
Data Notes:
The following states do not report for this feature: District of Columbia, Puerto Rico.
Citation:
Varies
Designed For Data Scientists and Analysts
400+ Data Sources
Use our platform to aggregate, normalize, and profile open source and premium control data. Spend less time finding and wrangling data, and more time building efficient and feature-rich machine learning data pipelines.
Data Science Treatments
Instantly apply industry-standard data science treatments and transformations, including (but not limited to) Differencing, Lead/Lag, Box Cox. Easily manipulate data across different time and geographic grains.
Auto Discovery
Our Patent Pending iterative testing engine allows you to upload your target variable, and the platform will test for possible statistical relationships across all available data sources. Saving you time and removing analyst bias.
Data Ingestion
Easily integrate your Ready Signal data to the data science platform of your choice. Connect directly to Ready Signal through our API or using one of our pre-built data connectors or download directly in Excel or CSV format.
