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
0.894 0.356 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.665
Model: OLS Adj. R-squared: 0.612
Method: Least Squares F-statistic: 12.58
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.22e-05
Time: 04:41:57 Log-Likelihood: -100.52
No. Observations: 23 AIC: 209.0
Df Residuals: 19 BIC: 213.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -108.3460 244.148 -0.444 0.662 -619.354 402.662
C(dose)[T.1] -74.7425 487.676 -0.153 0.880 -1095.461 945.976
expression 18.1374 27.233 0.666 0.513 -38.862 75.136
expression:C(dose)[T.1] 13.6783 53.637 0.255 0.801 -98.584 125.941
Omnibus: 0.197 Durbin-Watson: 2.060
Prob(Omnibus): 0.906 Jarque-Bera (JB): 0.194
Skew: 0.172 Prob(JB): 0.908
Kurtosis: 2.711 Cond. No. 1.20e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.664
Model: OLS Adj. R-squared: 0.630
Method: Least Squares F-statistic: 19.77
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.83e-05
Time: 04:41:57 Log-Likelihood: -100.56
No. Observations: 23 AIC: 207.1
Df Residuals: 20 BIC: 210.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -139.9487 205.384 -0.681 0.503 -568.373 288.475
C(dose)[T.1] 49.5997 9.446 5.251 0.000 29.895 69.305
expression 21.6635 22.907 0.946 0.356 -26.119 69.446
Omnibus: 0.161 Durbin-Watson: 2.043
Prob(Omnibus): 0.923 Jarque-Bera (JB): 0.245
Skew: 0.169 Prob(JB): 0.885
Kurtosis: 2.624 Cond. No. 440.

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: 04:41:57 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: 0.201
Model: OLS Adj. R-squared: 0.163
Method: Least Squares F-statistic: 5.284
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0319
Time: 04:41:57 Log-Likelihood: -110.52
No. Observations: 23 AIC: 225.0
Df Residuals: 21 BIC: 227.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -571.3319 283.305 -2.017 0.057 -1160.497 17.833
expression 71.9796 31.314 2.299 0.032 6.859 137.100
Omnibus: 0.699 Durbin-Watson: 2.585
Prob(Omnibus): 0.705 Jarque-Bera (JB): 0.710
Skew: 0.182 Prob(JB): 0.701
Kurtosis: 2.220 Cond. No. 402.

CP101

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

F-statistic p-value df difference
0.201 0.662 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.477
Model: OLS Adj. R-squared: 0.334
Method: Least Squares F-statistic: 3.339
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0597
Time: 04:41:57 Log-Likelihood: -70.444
No. Observations: 15 AIC: 148.9
Df Residuals: 11 BIC: 151.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 127.8273 303.261 0.422 0.682 -539.646 795.301
C(dose)[T.1] -188.2063 386.678 -0.487 0.636 -1039.279 662.866
expression -6.6734 33.482 -0.199 0.846 -80.367 67.021
expression:C(dose)[T.1] 27.4735 43.751 0.628 0.543 -68.822 123.769
Omnibus: 2.293 Durbin-Watson: 1.041
Prob(Omnibus): 0.318 Jarque-Bera (JB): 1.325
Skew: -0.725 Prob(JB): 0.516
Kurtosis: 2.857 Cond. No. 594.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.458
Model: OLS Adj. R-squared: 0.367
Method: Least Squares F-statistic: 5.067
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0254
Time: 04:41:57 Log-Likelihood: -70.708
No. Observations: 15 AIC: 147.4
Df Residuals: 12 BIC: 149.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -17.8009 190.415 -0.093 0.927 -432.679 397.077
C(dose)[T.1] 54.2898 19.305 2.812 0.016 12.228 96.351
expression 9.4170 21.001 0.448 0.662 -36.341 55.174
Omnibus: 2.162 Durbin-Watson: 1.002
Prob(Omnibus): 0.339 Jarque-Bera (JB): 1.411
Skew: -0.735 Prob(JB): 0.494
Kurtosis: 2.686 Cond. No. 218.

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: 04:41:57 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: 0.101
Model: OLS Adj. R-squared: 0.031
Method: Least Squares F-statistic: 1.453
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.249
Time: 04:41:57 Log-Likelihood: -74.505
No. Observations: 15 AIC: 153.0
Df Residuals: 13 BIC: 154.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 315.6434 184.382 1.712 0.111 -82.689 713.976
expression -25.3335 21.014 -1.206 0.249 -70.732 20.065
Omnibus: 1.165 Durbin-Watson: 1.165
Prob(Omnibus): 0.559 Jarque-Bera (JB): 0.764
Skew: -0.090 Prob(JB): 0.683
Kurtosis: 1.909 Cond. No. 170.