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
0.028 0.870 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.656
Model: OLS Adj. R-squared: 0.601
Method: Least Squares F-statistic: 12.06
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000120
Time: 03:34:36 Log-Likelihood: -100.84
No. Observations: 23 AIC: 209.7
Df Residuals: 19 BIC: 214.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -25.2656 232.933 -0.108 0.915 -512.800 462.269
C(dose)[T.1] 233.7699 309.126 0.756 0.459 -413.237 880.777
expression 8.7797 25.724 0.341 0.737 -45.061 62.620
expression:C(dose)[T.1] -19.4314 33.481 -0.580 0.568 -89.508 50.645
Omnibus: 0.223 Durbin-Watson: 1.847
Prob(Omnibus): 0.894 Jarque-Bera (JB): 0.420
Skew: 0.099 Prob(JB): 0.810
Kurtosis: 2.368 Cond. No. 879.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.53
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.79e-05
Time: 03:34:36 Log-Likelihood: -101.05
No. Observations: 23 AIC: 208.1
Df Residuals: 20 BIC: 211.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 78.5631 146.677 0.536 0.598 -227.401 384.527
C(dose)[T.1] 54.4839 11.154 4.885 0.000 31.216 77.751
expression -2.6906 16.190 -0.166 0.870 -36.462 31.081
Omnibus: 0.456 Durbin-Watson: 1.858
Prob(Omnibus): 0.796 Jarque-Bera (JB): 0.558
Skew: 0.060 Prob(JB): 0.757
Kurtosis: 2.246 Cond. No. 315.

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: 03:34:36 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: 0.231
Model: OLS Adj. R-squared: 0.195
Method: Least Squares F-statistic: 6.325
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0201
Time: 03:34:36 Log-Likelihood: -110.08
No. Observations: 23 AIC: 224.2
Df Residuals: 21 BIC: 226.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -348.1890 170.267 -2.045 0.054 -702.279 5.901
expression 46.2311 18.383 2.515 0.020 8.002 84.461
Omnibus: 1.826 Durbin-Watson: 2.404
Prob(Omnibus): 0.401 Jarque-Bera (JB): 1.511
Skew: 0.485 Prob(JB): 0.470
Kurtosis: 2.202 Cond. No. 252.

CP101

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

F-statistic p-value df difference
0.399 0.540 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.487
Model: OLS Adj. R-squared: 0.347
Method: Least Squares F-statistic: 3.482
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0539
Time: 03:34:36 Log-Likelihood: -70.292
No. Observations: 15 AIC: 148.6
Df Residuals: 11 BIC: 151.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 259.0122 215.663 1.201 0.255 -215.658 733.682
C(dose)[T.1] -194.1866 366.350 -0.530 0.607 -1000.518 612.145
expression -22.5364 25.332 -0.890 0.393 -78.292 33.219
expression:C(dose)[T.1] 28.6330 43.070 0.665 0.520 -66.163 123.429
Omnibus: 2.569 Durbin-Watson: 0.692
Prob(Omnibus): 0.277 Jarque-Bera (JB): 1.811
Skew: -0.823 Prob(JB): 0.404
Kurtosis: 2.566 Cond. No. 497.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.466
Model: OLS Adj. R-squared: 0.378
Method: Least Squares F-statistic: 5.246
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0231
Time: 03:34:36 Log-Likelihood: -70.588
No. Observations: 15 AIC: 147.2
Df Residuals: 12 BIC: 149.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 174.8070 170.441 1.026 0.325 -196.553 546.167
C(dose)[T.1] 49.1360 15.485 3.173 0.008 15.397 82.875
expression -12.6311 20.005 -0.631 0.540 -56.219 30.956
Omnibus: 3.538 Durbin-Watson: 0.720
Prob(Omnibus): 0.171 Jarque-Bera (JB): 2.217
Skew: -0.939 Prob(JB): 0.330
Kurtosis: 2.872 Cond. No. 191.

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: 03:34:36 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: 0.019
Model: OLS Adj. R-squared: -0.057
Method: Least Squares F-statistic: 0.2497
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.626
Time: 03:34:36 Log-Likelihood: -75.157
No. Observations: 15 AIC: 154.3
Df Residuals: 13 BIC: 155.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 204.3472 221.741 0.922 0.374 -274.695 683.390
expression -13.0235 26.065 -0.500 0.626 -69.333 43.286
Omnibus: 0.972 Durbin-Watson: 1.708
Prob(Omnibus): 0.615 Jarque-Bera (JB): 0.716
Skew: 0.116 Prob(JB): 0.699
Kurtosis: 1.955 Cond. No. 190.