Cumulative Hospitalizations by State - HealthData
Why Use This Data Source In Your Models?
Seasonal Impact
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Automated Data Profiling
Suggested Treatment:
Grain Transformation:
Source:
U.S. Department of Health & Human Services
Release:
COVID-19 Hospitalizations
Units:
Hospitalizations
Frequency:
Daily
Available Through:
04/27/2024
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 Square Root variable will also perform well.
Grain Transformation:
Data is unable to be distributed by time or geography. The roll up method used is Weighted Average.
Auto Correlation Analysis:
Data shows auto correlation indicating a need for differencing
The ACF indicates 2 order differencing is appropriate.
Further differencing is reccommended
Trend Analysis:
The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.67 p-value = 0.01 indicates that the data is not stationary.
Distribution Analysis:
The Shapiro-Wilk test returned W = 0.91 with a p-value =0.00 indicating the data does not follow a normal distribution.
A skewness score of 0.06 indicates the data are fairly symmetrical.
Hartigan's dip test score of 0.06 with a p-value of 0.00 inidcates the data is multimodal
Statistics (Pearson P/ df, lower => more normal)
Auto Correlation Function
Auto Correlation Function After Differencing
Partial Auto Correlation Function
Seasonal and Trend Decompostion
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