# Civilian Labor Force by State

Why Use This Data Source In Your Models?

Civilian labor force by US state measures the number of persons 16 years of age or older, employed or unemployed, excluding military personnel, federal government employees, retirees, handicapped or discouraged workers, and agricultural workers. This indicates state populations and can influence employment or unemployment rates.

Civilian Labor Force by State

## Automated Data Profiling

Ready Signal automatically profiles each data set and offers up suggested industry standard data science treatments to utilize with these data in your models.

### 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 time but not by geography. 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 = 0.09 p-value = 0.10 indicates that the data is stationary.

### Distribution Analysis:

The Shapiro-Wilk test returned W = 0.77 with a p-value =0.00 indicating the data does not follow a normal distribution.

A skewness score of -2.43 indicates the data are substantially skewed.

Hartigan's dip test score of 0.02 with a p-value of 0.96 inidcates the data is unimodal

### Statistics (Pearson P/ df, lower => more normal)

No transform
11.19
Box-cox
11.22
Log_b(x-a)
11.17
sqrt(x+a)
11.13
exp(x)
NA
arcsinh(x)
11.17
Yeo-Johnson
9.90
OrderNorm
0.03

Auto Correlation Function

Auto Correlation Function After Differencing

Partial Auto Correlation Function

Seasonal Impact

Seasonal and Trend Decompostion

### 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.