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
0.475 0.499 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.659
Model: OLS Adj. R-squared: 0.605
Method: Least Squares F-statistic: 12.22
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000110
Time: 03:34:36 Log-Likelihood: -100.74
No. Observations: 23 AIC: 209.5
Df Residuals: 19 BIC: 214.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 63.9715 65.246 0.980 0.339 -72.590 200.533
C(dose)[T.1] 74.2259 78.267 0.948 0.355 -89.589 238.041
expression -1.8826 12.526 -0.150 0.882 -28.099 24.334
expression:C(dose)[T.1] -4.4132 15.297 -0.289 0.776 -36.430 27.604
Omnibus: 2.020 Durbin-Watson: 1.903
Prob(Omnibus): 0.364 Jarque-Bera (JB): 1.170
Skew: 0.203 Prob(JB): 0.557
Kurtosis: 1.973 Cond. No. 130.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.657
Model: OLS Adj. R-squared: 0.623
Method: Least Squares F-statistic: 19.17
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.24e-05
Time: 03:34:36 Log-Likelihood: -100.79
No. Observations: 23 AIC: 207.6
Df Residuals: 20 BIC: 211.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 79.3165 36.912 2.149 0.044 2.319 156.314
C(dose)[T.1] 51.8010 8.949 5.788 0.000 33.133 70.469
expression -4.8417 7.023 -0.689 0.499 -19.492 9.809
Omnibus: 1.574 Durbin-Watson: 1.907
Prob(Omnibus): 0.455 Jarque-Bera (JB): 1.044
Skew: 0.202 Prob(JB): 0.593
Kurtosis: 2.037 Cond. No. 45.2

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: 03:34:37 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: 0.083
Model: OLS Adj. R-squared: 0.039
Method: Least Squares F-statistic: 1.899
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.183
Time: 03:34:37 Log-Likelihood: -112.11
No. Observations: 23 AIC: 228.2
Df Residuals: 21 BIC: 230.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 155.0483 55.094 2.814 0.010 40.475 269.622
expression -14.9641 10.858 -1.378 0.183 -37.544 7.616
Omnibus: 2.586 Durbin-Watson: 2.430
Prob(Omnibus): 0.274 Jarque-Bera (JB): 1.412
Skew: 0.288 Prob(JB): 0.494
Kurtosis: 1.931 Cond. No. 42.0

CP101

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

F-statistic p-value df difference
0.060 0.811 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.463
Model: OLS Adj. R-squared: 0.317
Method: Least Squares F-statistic: 3.166
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0678
Time: 03:34:37 Log-Likelihood: -70.632
No. Observations: 15 AIC: 149.3
Df Residuals: 11 BIC: 152.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 165.0551 182.792 0.903 0.386 -237.268 567.378
C(dose)[T.1] -65.6852 239.176 -0.275 0.789 -592.108 460.738
expression -17.6019 32.888 -0.535 0.603 -89.988 54.784
expression:C(dose)[T.1] 20.4243 41.411 0.493 0.632 -70.721 111.570
Omnibus: 2.205 Durbin-Watson: 0.639
Prob(Omnibus): 0.332 Jarque-Bera (JB): 1.577
Skew: -0.759 Prob(JB): 0.455
Kurtosis: 2.532 Cond. No. 252.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.451
Model: OLS Adj. R-squared: 0.360
Method: Least Squares F-statistic: 4.939
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0272
Time: 03:34:37 Log-Likelihood: -70.796
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 93.6070 107.906 0.867 0.403 -141.499 328.713
C(dose)[T.1] 51.8737 19.155 2.708 0.019 10.138 93.609
expression -4.7199 19.345 -0.244 0.811 -46.869 37.429
Omnibus: 2.406 Durbin-Watson: 0.787
Prob(Omnibus): 0.300 Jarque-Bera (JB): 1.704
Skew: -0.795 Prob(JB): 0.427
Kurtosis: 2.553 Cond. No. 84.0

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: 03:34:37 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: 0.116
Model: OLS Adj. R-squared: 0.048
Method: Least Squares F-statistic: 1.711
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.214
Time: 03:34:37 Log-Likelihood: -74.373
No. Observations: 15 AIC: 152.7
Df Residuals: 13 BIC: 154.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -54.2537 113.503 -0.478 0.641 -299.462 190.955
expression 25.2904 19.337 1.308 0.214 -16.485 67.066
Omnibus: 0.309 Durbin-Watson: 1.368
Prob(Omnibus): 0.857 Jarque-Bera (JB): 0.119
Skew: -0.184 Prob(JB): 0.942
Kurtosis: 2.766 Cond. No. 71.8