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
0.160 0.693 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.655
Model: OLS Adj. R-squared: 0.601
Method: Least Squares F-statistic: 12.03
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000121
Time: 03:34:36 Log-Likelihood: -100.86
No. Observations: 23 AIC: 209.7
Df Residuals: 19 BIC: 214.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 53.0012 99.918 0.530 0.602 -156.129 262.131
C(dose)[T.1] 110.1199 138.820 0.793 0.437 -180.434 400.674
expression 0.1727 14.265 0.012 0.990 -29.684 30.030
expression:C(dose)[T.1] -8.7439 20.577 -0.425 0.676 -51.812 34.324
Omnibus: 0.221 Durbin-Watson: 1.980
Prob(Omnibus): 0.896 Jarque-Bera (JB): 0.313
Skew: 0.199 Prob(JB): 0.855
Kurtosis: 2.590 Cond. No. 277.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.617
Method: Least Squares F-statistic: 18.72
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.62e-05
Time: 03:34:36 Log-Likelihood: -100.97
No. Observations: 23 AIC: 207.9
Df Residuals: 20 BIC: 211.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 82.3794 70.644 1.166 0.257 -64.982 229.741
C(dose)[T.1] 51.2938 10.117 5.070 0.000 30.189 72.398
expression -4.0296 10.068 -0.400 0.693 -25.031 16.972
Omnibus: 0.139 Durbin-Watson: 1.877
Prob(Omnibus): 0.933 Jarque-Bera (JB): 0.306
Skew: 0.147 Prob(JB): 0.858
Kurtosis: 2.517 Cond. No. 112.

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: 0.204
Model: OLS Adj. R-squared: 0.167
Method: Least Squares F-statistic: 5.396
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0303
Time: 03:34:36 Log-Likelihood: -110.47
No. Observations: 23 AIC: 224.9
Df Residuals: 21 BIC: 227.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 280.7277 86.776 3.235 0.004 100.267 461.188
expression -29.7857 12.823 -2.323 0.030 -56.453 -3.119
Omnibus: 1.355 Durbin-Watson: 2.247
Prob(Omnibus): 0.508 Jarque-Bera (JB): 0.731
Skew: 0.437 Prob(JB): 0.694
Kurtosis: 3.002 Cond. No. 93.2

CP101

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

F-statistic p-value df difference
1.029 0.330 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.498
Model: OLS Adj. R-squared: 0.362
Method: Least Squares F-statistic: 3.644
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0480
Time: 03:34:36 Log-Likelihood: -70.125
No. Observations: 15 AIC: 148.2
Df Residuals: 11 BIC: 151.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -152.2352 264.969 -0.575 0.577 -735.429 430.959
C(dose)[T.1] 163.2167 313.275 0.521 0.613 -526.298 852.731
expression 29.8191 35.936 0.830 0.424 -49.275 108.913
expression:C(dose)[T.1] -15.5758 42.391 -0.367 0.720 -108.879 77.727
Omnibus: 3.186 Durbin-Watson: 0.455
Prob(Omnibus): 0.203 Jarque-Bera (JB): 2.211
Skew: -0.922 Prob(JB): 0.331
Kurtosis: 2.628 Cond. No. 443.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.492
Model: OLS Adj. R-squared: 0.408
Method: Least Squares F-statistic: 5.818
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0171
Time: 03:34:36 Log-Likelihood: -70.216
No. Observations: 15 AIC: 146.4
Df Residuals: 12 BIC: 148.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -69.7818 135.717 -0.514 0.616 -365.485 225.921
C(dose)[T.1] 48.2557 15.134 3.189 0.008 15.282 81.229
expression 18.6261 18.363 1.014 0.330 -21.382 58.635
Omnibus: 3.475 Durbin-Watson: 0.536
Prob(Omnibus): 0.176 Jarque-Bera (JB): 2.369
Skew: -0.961 Prob(JB): 0.306
Kurtosis: 2.691 Cond. No. 136.

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: 0.062
Model: OLS Adj. R-squared: -0.010
Method: Least Squares F-statistic: 0.8615
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.370
Time: 03:34:36 Log-Likelihood: -74.819
No. Observations: 15 AIC: 153.6
Df Residuals: 13 BIC: 155.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -70.5723 177.222 -0.398 0.697 -453.438 312.294
expression 22.2140 23.933 0.928 0.370 -29.490 73.918
Omnibus: 1.805 Durbin-Watson: 1.532
Prob(Omnibus): 0.406 Jarque-Bera (JB): 0.915
Skew: 0.083 Prob(JB): 0.633
Kurtosis: 1.801 Cond. No. 136.