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
0.336 0.568 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.666
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 12.65
Date: Tue, 28 Jan 2025 Prob (F-statistic): 8.93e-05
Time: 19:42:32 Log-Likelihood: -100.48
No. Observations: 23 AIC: 209.0
Df Residuals: 19 BIC: 213.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 76.9335 137.240 0.561 0.582 -210.313 364.180
C(dose)[T.1] 316.3718 322.799 0.980 0.339 -359.255 991.998
expression -2.9139 17.580 -0.166 0.870 -39.710 33.882
expression:C(dose)[T.1] -32.6648 40.395 -0.809 0.429 -117.212 51.882
Omnibus: 0.584 Durbin-Watson: 1.794
Prob(Omnibus): 0.747 Jarque-Bera (JB): 0.360
Skew: -0.293 Prob(JB): 0.835
Kurtosis: 2.821 Cond. No. 688.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.655
Model: OLS Adj. R-squared: 0.620
Method: Least Squares F-statistic: 18.97
Date: Tue, 28 Jan 2025 Prob (F-statistic): 2.40e-05
Time: 19:42:32 Log-Likelihood: -100.87
No. Observations: 23 AIC: 207.7
Df Residuals: 20 BIC: 211.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 125.1849 122.515 1.022 0.319 -130.377 380.747
C(dose)[T.1] 55.4569 9.434 5.879 0.000 35.779 75.135
expression -9.1009 15.690 -0.580 0.568 -41.831 23.629
Omnibus: 0.093 Durbin-Watson: 1.987
Prob(Omnibus): 0.955 Jarque-Bera (JB): 0.318
Skew: -0.020 Prob(JB): 0.853
Kurtosis: 2.426 Cond. No. 227.

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: Tue, 28 Jan 2025 Prob (F-statistic): 3.51e-06
Time: 19:42:32 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: 0.058
Model: OLS Adj. R-squared: 0.014
Method: Least Squares F-statistic: 1.305
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.266
Time: 19:42:32 Log-Likelihood: -112.41
No. Observations: 23 AIC: 228.8
Df Residuals: 21 BIC: 231.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -130.9461 184.565 -0.709 0.486 -514.769 252.877
expression 26.6318 23.316 1.142 0.266 -21.856 75.119
Omnibus: 0.524 Durbin-Watson: 2.294
Prob(Omnibus): 0.769 Jarque-Bera (JB): 0.631
Skew: 0.243 Prob(JB): 0.730
Kurtosis: 2.351 Cond. No. 212.

CP101

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

F-statistic p-value df difference
0.136 0.719 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.456
Model: OLS Adj. R-squared: 0.307
Method: Least Squares F-statistic: 3.072
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.0728
Time: 19:42:32 Log-Likelihood: -70.736
No. Observations: 15 AIC: 149.5
Df Residuals: 11 BIC: 152.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -0.5888 223.888 -0.003 0.998 -493.362 492.185
C(dose)[T.1] 86.3874 262.613 0.329 0.748 -491.620 664.395
expression 9.4004 30.899 0.304 0.767 -58.607 77.408
expression:C(dose)[T.1] -5.0009 36.550 -0.137 0.894 -85.448 75.446
Omnibus: 2.350 Durbin-Watson: 0.909
Prob(Omnibus): 0.309 Jarque-Bera (JB): 1.737
Skew: -0.787 Prob(JB): 0.420
Kurtosis: 2.451 Cond. No. 342.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.455
Model: OLS Adj. R-squared: 0.364
Method: Least Squares F-statistic: 5.008
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.0262
Time: 19:42:32 Log-Likelihood: -70.749
No. Observations: 15 AIC: 147.5
Df Residuals: 12 BIC: 149.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 25.2705 115.009 0.220 0.830 -225.313 275.854
C(dose)[T.1] 50.5295 16.064 3.145 0.008 15.528 85.531
expression 5.8265 15.816 0.368 0.719 -28.634 40.287
Omnibus: 2.349 Durbin-Watson: 0.897
Prob(Omnibus): 0.309 Jarque-Bera (JB): 1.747
Skew: -0.786 Prob(JB): 0.418
Kurtosis: 2.434 Cond. No. 107.

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: Tue, 28 Jan 2025 Prob (F-statistic): 0.00629
Time: 19:42:32 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: 0.006
Model: OLS Adj. R-squared: -0.071
Method: Least Squares F-statistic: 0.07238
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.792
Time: 19:42:32 Log-Likelihood: -75.258
No. Observations: 15 AIC: 154.5
Df Residuals: 13 BIC: 155.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 131.9390 142.617 0.925 0.372 -176.166 440.044
expression -5.3802 19.998 -0.269 0.792 -48.583 37.822
Omnibus: 0.401 Durbin-Watson: 1.561
Prob(Omnibus): 0.818 Jarque-Bera (JB): 0.498
Skew: 0.021 Prob(JB): 0.779
Kurtosis: 2.108 Cond. No. 102.