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
1.241 0.278 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.690
Model: OLS Adj. R-squared: 0.641
Method: Least Squares F-statistic: 14.09
Date: Thu, 21 Nov 2024 Prob (F-statistic): 4.53e-05
Time: 04:30:23 Log-Likelihood: -99.641
No. Observations: 23 AIC: 207.3
Df Residuals: 19 BIC: 211.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -180.9498 149.867 -1.207 0.242 -494.626 132.726
C(dose)[T.1] 265.5043 194.076 1.368 0.187 -140.702 671.711
expression 27.7497 17.672 1.570 0.133 -9.237 64.737
expression:C(dose)[T.1] -25.1394 22.535 -1.116 0.279 -72.305 22.026
Omnibus: 0.403 Durbin-Watson: 1.503
Prob(Omnibus): 0.817 Jarque-Bera (JB): 0.479
Skew: -0.267 Prob(JB): 0.787
Kurtosis: 2.535 Cond. No. 550.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.670
Model: OLS Adj. R-squared: 0.637
Method: Least Squares F-statistic: 20.26
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.55e-05
Time: 04:30:23 Log-Likelihood: -100.37
No. Observations: 23 AIC: 206.7
Df Residuals: 20 BIC: 210.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -49.9388 93.675 -0.533 0.600 -245.341 145.464
C(dose)[T.1] 49.2379 9.271 5.311 0.000 29.898 68.578
expression 12.2898 11.032 1.114 0.278 -10.723 35.303
Omnibus: 0.348 Durbin-Watson: 1.619
Prob(Omnibus): 0.840 Jarque-Bera (JB): 0.335
Skew: -0.246 Prob(JB): 0.846
Kurtosis: 2.672 Cond. No. 193.

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: 04:30:23 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: 0.204
Model: OLS Adj. R-squared: 0.166
Method: Least Squares F-statistic: 5.368
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0307
Time: 04:30:23 Log-Likelihood: -110.49
No. Observations: 23 AIC: 225.0
Df Residuals: 21 BIC: 227.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -227.1615 132.613 -1.713 0.101 -502.946 48.623
expression 35.5440 15.342 2.317 0.031 3.639 67.449
Omnibus: 2.070 Durbin-Watson: 2.164
Prob(Omnibus): 0.355 Jarque-Bera (JB): 1.500
Skew: 0.424 Prob(JB): 0.472
Kurtosis: 2.080 Cond. No. 180.

CP101

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

F-statistic p-value df difference
0.070 0.796 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.469
Model: OLS Adj. R-squared: 0.324
Method: Least Squares F-statistic: 3.236
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0644
Time: 04:30:23 Log-Likelihood: -70.555
No. Observations: 15 AIC: 149.1
Df Residuals: 11 BIC: 151.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -51.1807 307.669 -0.166 0.871 -728.356 625.994
C(dose)[T.1] 259.4161 355.379 0.730 0.481 -522.769 1041.601
expression 16.0019 41.478 0.386 0.707 -75.290 107.294
expression:C(dose)[T.1] -28.1911 47.732 -0.591 0.567 -133.248 76.866
Omnibus: 3.674 Durbin-Watson: 1.039
Prob(Omnibus): 0.159 Jarque-Bera (JB): 1.964
Skew: -0.882 Prob(JB): 0.375
Kurtosis: 3.179 Cond. No. 501.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.452
Model: OLS Adj. R-squared: 0.361
Method: Least Squares F-statistic: 4.948
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0271
Time: 04:30:23 Log-Likelihood: -70.789
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 106.6100 148.395 0.718 0.486 -216.714 429.934
C(dose)[T.1] 49.7433 15.829 3.143 0.008 15.255 84.232
expression -5.2861 19.960 -0.265 0.796 -48.776 38.204
Omnibus: 2.635 Durbin-Watson: 0.847
Prob(Omnibus): 0.268 Jarque-Bera (JB): 1.675
Skew: -0.810 Prob(JB): 0.433
Kurtosis: 2.757 Cond. No. 145.

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: 04:30:23 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: 0.001
Model: OLS Adj. R-squared: -0.076
Method: Least Squares F-statistic: 0.01273
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.912
Time: 04:30:23 Log-Likelihood: -75.293
No. Observations: 15 AIC: 154.6
Df Residuals: 13 BIC: 156.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 72.0381 191.968 0.375 0.714 -342.683 486.759
expression 2.8964 25.671 0.113 0.912 -52.563 58.356
Omnibus: 0.620 Durbin-Watson: 1.629
Prob(Omnibus): 0.733 Jarque-Bera (JB): 0.588
Skew: 0.055 Prob(JB): 0.745
Kurtosis: 2.036 Cond. No. 144.