AIM Score vs. Gene Expression
Full X range:
Auto X range:
Group Comparisons: Boxplots

CP73

Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)

F-statistic p-value df difference
0.079 0.781 1.0

Model:
AIM ~ expression + C(dose) + expression:C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.654
Model: OLS Adj. R-squared: 0.599
Method: Least Squares F-statistic: 11.95
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000127
Time: 04:41:58 Log-Likelihood: -100.91
No. Observations: 23 AIC: 209.8
Df Residuals: 19 BIC: 214.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 69.8424 82.214 0.850 0.406 -102.234 241.919
C(dose)[T.1] 15.3905 95.767 0.161 0.874 -185.052 215.833
expression -2.5407 13.323 -0.191 0.851 -30.426 25.344
expression:C(dose)[T.1] 6.6018 16.001 0.413 0.685 -26.888 40.092
Omnibus: 0.537 Durbin-Watson: 1.917
Prob(Omnibus): 0.765 Jarque-Bera (JB): 0.614
Skew: 0.136 Prob(JB): 0.736
Kurtosis: 2.247 Cond. No. 183.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.61
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.72e-05
Time: 04:41:58 Log-Likelihood: -101.02
No. Observations: 23 AIC: 208.0
Df Residuals: 20 BIC: 211.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 41.6783 44.861 0.929 0.364 -51.900 135.257
C(dose)[T.1] 54.6794 9.964 5.488 0.000 33.895 75.464
expression 2.0362 7.224 0.282 0.781 -13.032 17.105
Omnibus: 0.580 Durbin-Watson: 1.881
Prob(Omnibus): 0.748 Jarque-Bera (JB): 0.622
Skew: 0.087 Prob(JB): 0.733
Kurtosis: 2.213 Cond. No. 62.6

Model:
AIM ~ C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.632
Method: Least Squares F-statistic: 38.84
Date: Thu, 21 Nov 2024 Prob (F-statistic): 3.51e-06
Time: 04:41:58 Log-Likelihood: -101.06
No. Observations: 23 AIC: 206.1
Df Residuals: 21 BIC: 208.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 54.2083 5.919 9.159 0.000 41.900 66.517
C(dose)[T.1] 53.3371 8.558 6.232 0.000 35.539 71.135
Omnibus: 0.322 Durbin-Watson: 1.888
Prob(Omnibus): 0.851 Jarque-Bera (JB): 0.485
Skew: 0.060 Prob(JB): 0.785
Kurtosis: 2.299 Cond. No. 2.57

Model:
AIM ~ expression

OLS Regression Results
Dep. Variable: AIM R-squared: 0.124
Model: OLS Adj. R-squared: 0.082
Method: Least Squares F-statistic: 2.976
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0992
Time: 04:41:58 Log-Likelihood: -111.58
No. Observations: 23 AIC: 227.2
Df Residuals: 21 BIC: 229.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 178.4345 57.625 3.096 0.005 58.597 298.272
expression -16.9088 9.802 -1.725 0.099 -37.294 3.476
Omnibus: 4.159 Durbin-Watson: 2.301
Prob(Omnibus): 0.125 Jarque-Bera (JB): 1.827
Skew: 0.343 Prob(JB): 0.401
Kurtosis: 1.802 Cond. No. 51.6

CP101

Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)

F-statistic p-value df difference
12.375 0.004 1.0

Model:
AIM ~ expression + C(dose) + expression:C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.732
Model: OLS Adj. R-squared: 0.658
Method: Least Squares F-statistic: 9.991
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00179
Time: 04:41:58 Log-Likelihood: -65.438
No. Observations: 15 AIC: 138.9
Df Residuals: 11 BIC: 141.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -380.8294 271.605 -1.402 0.188 -978.629 216.970
C(dose)[T.1] -72.1914 332.357 -0.217 0.832 -803.704 659.321
expression 59.2912 35.908 1.651 0.127 -19.742 138.325
expression:C(dose)[T.1] 15.0703 43.745 0.345 0.737 -81.211 111.352
Omnibus: 1.636 Durbin-Watson: 1.565
Prob(Omnibus): 0.441 Jarque-Bera (JB): 1.167
Skew: -0.645 Prob(JB): 0.558
Kurtosis: 2.552 Cond. No. 647.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.729
Model: OLS Adj. R-squared: 0.683
Method: Least Squares F-statistic: 16.11
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000399
Time: 04:41:58 Log-Likelihood: -65.518
No. Observations: 15 AIC: 137.0
Df Residuals: 12 BIC: 139.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -457.6003 149.464 -3.062 0.010 -783.255 -131.945
C(dose)[T.1] 42.2371 11.219 3.765 0.003 17.792 66.682
expression 69.4457 19.741 3.518 0.004 26.434 112.457
Omnibus: 0.901 Durbin-Watson: 1.636
Prob(Omnibus): 0.637 Jarque-Bera (JB): 0.777
Skew: -0.472 Prob(JB): 0.678
Kurtosis: 2.406 Cond. No. 211.

Model:
AIM ~ C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.406
Method: Least Squares F-statistic: 10.58
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00629
Time: 04:41:58 Log-Likelihood: -70.833
No. Observations: 15 AIC: 145.7
Df Residuals: 13 BIC: 147.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 67.4286 11.044 6.106 0.000 43.570 91.287
C(dose)[T.1] 49.1964 15.122 3.253 0.006 16.527 81.866
Omnibus: 2.713 Durbin-Watson: 0.810
Prob(Omnibus): 0.258 Jarque-Bera (JB): 1.868
Skew: -0.843 Prob(JB): 0.393
Kurtosis: 2.619 Cond. No. 2.70

Model:
AIM ~ expression

OLS Regression Results
Dep. Variable: AIM R-squared: 0.408
Model: OLS Adj. R-squared: 0.363
Method: Least Squares F-statistic: 8.964
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0104
Time: 04:41:58 Log-Likelihood: -71.367
No. Observations: 15 AIC: 146.7
Df Residuals: 13 BIC: 148.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -534.8461 210.067 -2.546 0.024 -988.669 -81.023
expression 82.5500 27.571 2.994 0.010 22.985 142.115
Omnibus: 1.491 Durbin-Watson: 2.480
Prob(Omnibus): 0.475 Jarque-Bera (JB): 0.849
Skew: 0.099 Prob(JB): 0.654
Kurtosis: 1.852 Cond. No. 208.