LID SPN TRAP

1460700_at

Stat3 signal transducer and activator of transcription 3

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
1.749 0.201 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.681
Model: OLS Adj. R-squared: 0.631
Method: Least Squares F-statistic: 13.52
Date: Thu, 21 Nov 2024 Prob (F-statistic): 5.90e-05
Time: 03:39:32 Log-Likelihood: -99.968
No. Observations: 23 AIC: 207.9
Df Residuals: 19 BIC: 212.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -95.6487 129.079 -0.741 0.468 -365.813 174.516
C(dose)[T.1] 126.7026 165.492 0.766 0.453 -219.677 473.082
expression 18.6637 16.059 1.162 0.260 -14.948 52.275
expression:C(dose)[T.1] -9.4702 20.305 -0.466 0.646 -51.969 33.028
Omnibus: 0.770 Durbin-Watson: 1.920
Prob(Omnibus): 0.681 Jarque-Bera (JB): 0.732
Skew: 0.375 Prob(JB): 0.693
Kurtosis: 2.551 Cond. No. 440.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.677
Model: OLS Adj. R-squared: 0.645
Method: Least Squares F-statistic: 20.99
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.23e-05
Time: 03:39:32 Log-Likelihood: -100.10
No. Observations: 23 AIC: 206.2
Df Residuals: 20 BIC: 209.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -48.0853 77.566 -0.620 0.542 -209.885 113.715
C(dose)[T.1] 49.6319 8.864 5.599 0.000 31.142 68.122
expression 12.7400 9.633 1.323 0.201 -7.354 32.834
Omnibus: 1.006 Durbin-Watson: 1.991
Prob(Omnibus): 0.605 Jarque-Bera (JB): 0.876
Skew: 0.433 Prob(JB): 0.645
Kurtosis: 2.593 Cond. No. 154.

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: 03:39:32 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: 0.171
Model: OLS Adj. R-squared: 0.132
Method: Least Squares F-statistic: 4.344
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0495
Time: 03:39:32 Log-Likelihood: -110.94
No. Observations: 23 AIC: 225.9
Df Residuals: 21 BIC: 228.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -163.5991 116.924 -1.399 0.176 -406.755 79.557
expression 29.7875 14.291 2.084 0.050 0.067 59.508
Omnibus: 2.014 Durbin-Watson: 2.765
Prob(Omnibus): 0.365 Jarque-Bera (JB): 1.681
Skew: 0.623 Prob(JB): 0.432
Kurtosis: 2.554 Cond. No. 148.

CP101

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

F-statistic p-value df difference
3.024 0.108 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.587
Model: OLS Adj. R-squared: 0.474
Method: Least Squares F-statistic: 5.205
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0176
Time: 03:39:32 Log-Likelihood: -68.673
No. Observations: 15 AIC: 145.3
Df Residuals: 11 BIC: 148.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -1.8405 263.480 -0.007 0.995 -581.757 578.076
C(dose)[T.1] -214.9919 316.845 -0.679 0.511 -912.362 482.379
expression 8.5654 32.555 0.263 0.797 -63.088 80.219
expression:C(dose)[T.1] 33.3321 39.335 0.847 0.415 -53.244 119.908
Omnibus: 0.948 Durbin-Watson: 1.050
Prob(Omnibus): 0.622 Jarque-Bera (JB): 0.857
Skew: -0.412 Prob(JB): 0.652
Kurtosis: 2.169 Cond. No. 525.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.560
Model: OLS Adj. R-squared: 0.486
Method: Least Squares F-statistic: 7.628
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00728
Time: 03:39:32 Log-Likelihood: -69.148
No. Observations: 15 AIC: 144.3
Df Residuals: 12 BIC: 146.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -186.4840 146.381 -1.274 0.227 -505.421 132.453
C(dose)[T.1] 53.2206 14.256 3.733 0.003 22.160 84.282
expression 31.3974 18.056 1.739 0.108 -7.943 70.738
Omnibus: 2.888 Durbin-Watson: 1.301
Prob(Omnibus): 0.236 Jarque-Bera (JB): 1.271
Skew: -0.303 Prob(JB): 0.530
Kurtosis: 1.710 Cond. No. 170.

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: 03:39:32 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: 0.048
Model: OLS Adj. R-squared: -0.025
Method: Least Squares F-statistic: 0.6606
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.431
Time: 03:39:32 Log-Likelihood: -74.928
No. Observations: 15 AIC: 153.9
Df Residuals: 13 BIC: 155.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -70.3543 202.040 -0.348 0.733 -506.836 366.128
expression 20.4548 25.166 0.813 0.431 -33.913 74.822
Omnibus: 0.787 Durbin-Watson: 1.863
Prob(Omnibus): 0.675 Jarque-Bera (JB): 0.645
Skew: 0.050 Prob(JB): 0.724
Kurtosis: 1.989 Cond. No. 166.