LID SPN TRAP

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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
0.429 0.520 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.664
Model: OLS Adj. R-squared: 0.611
Method: Least Squares F-statistic: 12.52
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.50e-05
Time: 03:58:19 Log-Likelihood: -100.56
No. Observations: 23 AIC: 209.1
Df Residuals: 19 BIC: 213.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 46.6482 170.771 0.273 0.788 -310.779 404.075
C(dose)[T.1] -117.5363 258.119 -0.455 0.654 -657.786 422.714
expression 0.9008 20.335 0.044 0.965 -41.661 43.463
expression:C(dose)[T.1] 20.2185 30.623 0.660 0.517 -43.876 84.313
Omnibus: 0.431 Durbin-Watson: 1.974
Prob(Omnibus): 0.806 Jarque-Bera (JB): 0.553
Skew: 0.113 Prob(JB): 0.758
Kurtosis: 2.275 Cond. No. 633.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.656
Model: OLS Adj. R-squared: 0.622
Method: Least Squares F-statistic: 19.11
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.29e-05
Time: 03:58:19 Log-Likelihood: -100.82
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 -28.1750 125.933 -0.224 0.825 -290.867 234.517
C(dose)[T.1] 52.7841 8.718 6.054 0.000 34.598 70.970
expression 9.8163 14.988 0.655 0.520 -21.449 41.082
Omnibus: 0.161 Durbin-Watson: 2.038
Prob(Omnibus): 0.923 Jarque-Bera (JB): 0.324
Skew: 0.159 Prob(JB): 0.850
Kurtosis: 2.513 Cond. No. 249.

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: 03:58:19 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: 0.027
Model: OLS Adj. R-squared: -0.020
Method: Least Squares F-statistic: 0.5765
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.456
Time: 03:58:19 Log-Likelihood: -112.79
No. Observations: 23 AIC: 229.6
Df Residuals: 21 BIC: 231.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -76.9274 206.426 -0.373 0.713 -506.214 352.359
expression 18.6052 24.503 0.759 0.456 -32.352 69.562
Omnibus: 3.907 Durbin-Watson: 2.428
Prob(Omnibus): 0.142 Jarque-Bera (JB): 1.655
Skew: 0.272 Prob(JB): 0.437
Kurtosis: 1.804 Cond. No. 248.

CP101

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

F-statistic p-value df difference
2.234 0.161 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.539
Model: OLS Adj. R-squared: 0.414
Method: Least Squares F-statistic: 4.291
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0310
Time: 03:58:19 Log-Likelihood: -69.489
No. Observations: 15 AIC: 147.0
Df Residuals: 11 BIC: 149.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 1166.6711 1505.488 0.775 0.455 -2146.886 4480.229
C(dose)[T.1] -421.5712 1584.153 -0.266 0.795 -3908.268 3065.126
expression -126.9669 173.885 -0.730 0.481 -509.686 255.752
expression:C(dose)[T.1] 55.8815 182.604 0.306 0.765 -346.026 457.790
Omnibus: 0.722 Durbin-Watson: 1.317
Prob(Omnibus): 0.697 Jarque-Bera (JB): 0.717
Skew: -0.375 Prob(JB): 0.699
Kurtosis: 2.236 Cond. No. 2.98e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.535
Model: OLS Adj. R-squared: 0.458
Method: Least Squares F-statistic: 6.911
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0101
Time: 03:58:19 Log-Likelihood: -69.553
No. Observations: 15 AIC: 145.1
Df Residuals: 12 BIC: 147.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 727.9607 442.047 1.647 0.126 -235.176 1691.098
C(dose)[T.1] 63.1903 17.219 3.670 0.003 25.672 100.708
expression -76.2941 51.044 -1.495 0.161 -187.509 34.920
Omnibus: 0.603 Durbin-Watson: 1.288
Prob(Omnibus): 0.740 Jarque-Bera (JB): 0.632
Skew: -0.360 Prob(JB): 0.729
Kurtosis: 2.298 Cond. No. 545.

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: 03:58:19 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: 0.014
Model: OLS Adj. R-squared: -0.062
Method: Least Squares F-statistic: 0.1816
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.677
Time: 03:58:19 Log-Likelihood: -75.196
No. Observations: 15 AIC: 154.4
Df Residuals: 13 BIC: 155.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -130.0509 525.076 -0.248 0.808 -1264.408 1004.307
expression 25.5516 59.960 0.426 0.677 -103.983 155.087
Omnibus: 0.512 Durbin-Watson: 1.403
Prob(Omnibus): 0.774 Jarque-Bera (JB): 0.546
Skew: 0.043 Prob(JB): 0.761
Kurtosis: 2.069 Cond. No. 462.