Why Use This Data Source In Your Models?
Average weekly hours of production and non-supervisory manufacturing employees represents the average work week of employees in these fields in the United States. This is indicative of the demand for these jobs.
Manufacturing Employee Avg Hours, Nonsupervisory
U.S. Bureau of Labor Statistics
Hours, Seasonally Adjusted
The data shows auto correlation and a non-normal distribution. The data should be differenced. While the Arcsin transformation, provides the best normality, the Boxcox variable will also perform well.
Data is unable to be distributed by time or geography. The roll up method used is Weighted Average.
Data shows auto correlation indicating a need for differencing
The ACF indicates 1 order differencing is appropriate.
Following first order differencing, no further differencing is required based on the differenced ACF at lag one of -0.21
The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.17 p-value = 0.03 indicates that the data is not stationary.
The Shapiro-Wilk test returned W = 0.97 with a p-value =0.02 indicating the data does not follow a normal distribution.
A skewness score of -0.09 indicates the data are fairly symmetrical.
Hartigan's dip test score of 0.09 with a p-value of 0.00 inidcates the data is multimodal
Auto Correlation Function
Auto Correlation Function After Differencing
Partial Auto Correlation Function
Seasonal and Trend Decompostion
U.S. Bureau of Labor Statistics, Average Weekly Hours of Production and Nonsupervisory Employees, Manufacturing [AWHMAN], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/AWHMAN, December 15, 2019.
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