Pending Tests (State)

Source:
The COVID Tracking Project

Release:
COVID-19 Pending Tests (State)

Units:
Tests

Frequency:
Daily

Available Through:
03/07/2021

Why Use:

The COVID‑19 pandemic is an ongoing global pandemic of coronavirus disease 2019 (COVID‑19), caused by severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2). From the business focused data scientist there are three main measures to inform the economic impact- cases diagnosed, available hospital beds and recovered cases.

Suggested Treatment:

The data shows autocorrection, seasonality and a non-normal distribution. The data should be differenced and seasonally adjusted. While the Order Norm transformation, provides the best normality, the Boxcox variable will also perform well.

Grain Transformation:

Data is unable to be distributed by time or geography. The roll up method used is Sum.

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Pending Tests (State)

Auto Correction Function

Auto Correlation Function After Differencing

Partial Auto Correlation Function

Autocorrectation Analysis:

Data shows autocorrectation 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 = 0.15 p-value = 0.05 indicates that the data is not stationary.

Distribution Analysis:

The Shapiro-Wilk test returned W = 0.54 with a p-value =0.00 indicating the data does not follow a normal distribution.

A skewness score of 2.51 indicates the data are substantially skewed.

Hartigan's dip test score of 0.05 with a p-value of 0.07 inidcates the data is unimodal

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

No transform
5.62
Box-cox
1.95
Log_b(x-a)
1.99
sqrt(x+a)
3.87
exp(x)
NA
arcsinh(x)
1.99
Yeo-Johnson
1.95
OrderNorm
1.21

Data Notes:

As of March 7, 2021 The COVID Tracking Project are no longer collecting new data

Citation:

The COVID Tracking Project, Data API; https://covidtracking.com/data/api, Retrieved daily.

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