Regression Analysis — SeekingChartis Corpus

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95.3%

R²

95.2%

Adj R²

4,858

Observations

Features

4633.8

F-Statistic

419,775

RMSE (avg error)

R² = fraction of variance explained (1.0 = perfect, 0 = no signal). RMSE = root mean squared error (average prediction miss in target units). F-statistic tests whether the model explains more than random chance.

Intercept Interpretation

Value: $1.6M

SE: 6,023

Target Mean: $1.6M

Target Range: 9,665 — $62.8M

When all features are at their mean values, the predicted revenue per bed is 1,582,063.54

Coefficients (Standardized)

Target: Revenue Per Bed. Standardized coefficients (-1.0 to +1.0): a one-SD increase in the feature produces this fraction of the strongest effect. *** p<0.001, ** p<0.01, * p<0.05.

Variable	Strength	t	p-value	Sig	95% CI
Expense Per Bed	+1.000	236.0	0.0000	***	[1697359.063, 1725787.123]
Total Patient Days	-0.164	4.1	0.0000	***	[-414986.203, -146350.357]
Net Patient Revenue	+0.138	25.9	0.0000	***	[218448.829, 254191.016]
Revenue Per Day	+0.128	32.5	0.0000	***	[206449.569, 232950.501]
Operating Margin	+0.113	27.5	0.0000	***	[179921.203, 207579.426]
Operating Expenses	+0.107	22.9	0.0000	***	[166933.283, 198169.698]
Net Income	+0.102	23.4	0.0000	***	[160140.159, 189479.731]
Medicare Intensity	-0.054	1.5	0.1354	ns	[-213732.094, 28884.983]
Occupancy Rate	+0.044	7.6	0.0000	***	[56481.109, 95486.497]
Payer Diversity	+0.042	8.5	0.0000	***	[55867.223, 89326.577]
Size Quartile	-0.039	5.7	0.0000	***	[-89834.570, -44083.080]
Contractual Allowances	-0.037	7.1	0.0000	***	[-80296.054, -45448.206]
Beds	+0.030	0.7	0.4839	ns	[-92407.684, 195087.026]
Medicaid Day Pct	-0.027	6.6	0.0000	***	[-60398.578, -32596.589]
Medicare Days	+0.022	0.6	0.5539	ns	[-86665.335, 161648.700]
Bed Days Available	+0.022	0.8	0.4531	ns	[-60116.436, 134694.160]
Commercial Pct	+0.020	6.1	0.0000	***	[23288.344, 45499.063]
Medicaid Days	+0.017	2.9	0.0036	**	[9421.072, 48356.797]
Net To Gross Ratio	-0.010	2.1	0.0374	*	[-32579.271, -977.348]
Gross Patient Revenue	+0.004	1.1	0.2726	ns	[-5110.064, 18105.553]
Medicare Day Pct	-0.003	1.0	0.3187	ns	[-16155.517, 5260.719]

Univariate Correlations with Target

Pearson r for each feature vs Revenue Per Bed. Shows raw linear relationship before controlling for other variables.

Feature	r	Strength
Expense Per Bed	0.953	Strong
Revenue Per Day	0.458	Moderate
Net Patient Revenue	0.266	Weak
Operating Expenses	0.265	Weak
Gross Patient Revenue	0.190	Weak
Contractual Allowances	0.161	Weak
Net Income	-0.106	Weak
Occupancy Rate	0.099	Weak
Operating Margin	0.090	Weak
Medicare Days	0.088	Weak
Total Patient Days	0.085	Weak
Payer Diversity	0.073	Weak
Bed Days Available	0.056	Weak
Beds	0.050	Weak
Medicare Intensity	0.046	Weak
Size Quartile	-0.045	Weak
Medicaid Days	0.045	Weak
Commercial Pct	0.041	Weak
Medicaid Day Pct	-0.031	Weak
Medicare Day Pct	-0.022	Weak
Net To Gross Ratio	-0.008	Weak

Hospital Outliers (Residual Analysis)

Hospitals with the largest standardized residuals. >2σ = model underpredicts/overpredicts — investigate for deal opportunities or data quality issues.

Hospital	State	Actual	Predicted	Residual
MIDWESTERN REGIONAL MEDICAL CENTER	IL	$19.0M	$7.7M	+26.91σ
FRED HUTCHINSON CANCER CENTER	WA	$58.6M	$69.1M	-25.04σ
NATIONAL JEWISH HEALTH	CO	$11.6M	$21.0M	-22.44σ
MERCY WALWORTH HOSPITAL	WI	$24.7M	$18.3M	+15.03σ
SPECTRUM HEALTH REED CITY	MI	$3.1M	$9.3M	-14.76σ
WENATCHEE VALLEY HOSPITAL	WA	$25.2M	$19.7M	+13.13σ
LAGUNA HONDA HOSPITAL	CA	$36.6M	$40.2M	-8.53σ
TAHOE FOREST HOSPITAL	CA	$10.6M	$7.5M	+7.21σ
CALLAHAN EYE FOUNDATION HOSP	AL	$15.4M	$12.4M	+7.15σ
OAK LEAF SURGICAL HOSPITAL LLC	WI	$8.4M	$5.5M	+6.95σ
ST. JOSEPHS COMM. HOSPT.	WI	$6.2M	$3.4M	+6.82σ
CORYELL MEMORIAL HOSPITAL	TX	$12.2M	$9.7M	+5.93σ
DANA-FARBER CANCER INSTITUTE	MA	$62.8M	$60.4M	+5.75σ
MOUNTAIN VIEW HOSPITAL	ID	$8.9M	$6.6M	+5.43σ
RANCHO LOS AMIGOS NATL.REHAB.CTR.	CA	$6.2M	$4.0M	+5.11σ

Variance Inflation Factors

VIF > 10 = severe multicollinearity (coefficient estimates unreliable). VIF > 5 = moderate. Consider removing high-VIF features.

Feature	VIF	Status
Net Patient Revenue	999.0	High
Operating Expenses	999.0	High
Medicare Day Pct	999.0	High
Medicaid Day Pct	999.0	High
Net Income	999.0	High
Gross Patient Revenue	999.0	High
Contractual Allowances	999.0	High
Commercial Pct	999.0	High
Beds	148.3	High
Total Patient Days	129.5	High
Medicare Days	110.6	High
Medicare Intensity	105.6	High

Model Fit by State

How well the national model predicts within each state. Low R² states have unique market dynamics not captured by national features — consider state-specific models.

State	n	R²	Mean Residual
MA	83	99.7%	74,742
MO	133	99.7%	-55,176
CT	31	98.0%	29,445
DC	11	97.5%	-36,137
CA	317	97.1%	21,678
IA	91	96.7%	21,971
DE	8	96.5%	79,038
PA	174	96.2%	-2,324
WA	95	96.1%	60,844
ME	36	95.9%	105,284
ND	40	95.9%	-31,114
AL	87	95.4%	-24,109
AZ	85	95.1%	-50,647
NE	77	95.1%	88,720
HI	18	94.9%	44,063

Top Pairwise Correlations

Variable 1	Variable 2	r
Gross Patient Revenue	Contractual Allowances	0.992
Beds	Bed Days Available	0.984
Net Patient Revenue	Operating Expenses	0.982
Total Patient Days	Bed Days Available	0.976
Beds	Total Patient Days	0.962
Medicare Days	Medicare Intensity	0.955
Expense Per Bed	Revenue Per Bed	0.953
Net Patient Revenue	Gross Patient Revenue	0.912
Total Patient Days	Medicare Days	0.901
Operating Expenses	Gross Patient Revenue	0.891
Beds	Medicare Intensity	0.881
Net Patient Revenue	Total Patient Days	0.879
Medicare Days	Bed Days Available	0.874
Operating Expenses	Total Patient Days	0.869
Total Patient Days	Gross Patient Revenue	0.863

Market Heatmap Analysis Hub Regression JSON API Operating Margin Revenue/Bed Occupancy Net-to-Gross