LID SPN TRAP

1416021_a_at

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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
0.933 0.346 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.683
Model: OLS Adj. R-squared: 0.633
Method: Least Squares F-statistic: 13.66
Date: Thu, 21 Nov 2024 Prob (F-statistic): 5.53e-05
Time: 04:58:41 Log-Likelihood: -99.887
No. Observations: 23 AIC: 207.8
Df Residuals: 19 BIC: 212.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 242.9772 523.692 0.464 0.648 -853.123 1339.077
C(dose)[T.1] -602.1917 622.968 -0.967 0.346 -1906.079 701.695
expression -16.9501 47.021 -0.360 0.722 -115.366 81.466
expression:C(dose)[T.1] 58.8286 55.920 1.052 0.306 -58.213 175.870
Omnibus: 1.334 Durbin-Watson: 1.873
Prob(Omnibus): 0.513 Jarque-Bera (JB): 1.173
Skew: 0.399 Prob(JB): 0.556
Kurtosis: 2.234 Cond. No. 2.33e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.665
Model: OLS Adj. R-squared: 0.631
Method: Least Squares F-statistic: 19.82
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.80e-05
Time: 04:58:41 Log-Likelihood: -100.54
No. Observations: 23 AIC: 207.1
Df Residuals: 20 BIC: 210.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -220.2514 284.247 -0.775 0.447 -813.180 372.677
C(dose)[T.1] 53.1185 8.575 6.194 0.000 35.231 71.006
expression 24.6446 25.518 0.966 0.346 -28.585 77.874
Omnibus: 1.868 Durbin-Watson: 1.980
Prob(Omnibus): 0.393 Jarque-Bera (JB): 1.256
Skew: 0.311 Prob(JB): 0.534
Kurtosis: 2.039 Cond. No. 746.

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: 04:58:41 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: 0.021
Model: OLS Adj. R-squared: -0.025
Method: Least Squares F-statistic: 0.4591
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.505
Time: 04:58:41 Log-Likelihood: -112.86
No. Observations: 23 AIC: 229.7
Df Residuals: 21 BIC: 232.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -241.3286 473.863 -0.509 0.616 -1226.781 744.124
expression 28.8167 42.529 0.678 0.505 -59.626 117.260
Omnibus: 2.328 Durbin-Watson: 2.573
Prob(Omnibus): 0.312 Jarque-Bera (JB): 1.293
Skew: 0.245 Prob(JB): 0.524
Kurtosis: 1.947 Cond. No. 746.

CP101

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

F-statistic p-value df difference
0.290 0.600 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.472
Model: OLS Adj. R-squared: 0.328
Method: Least Squares F-statistic: 3.278
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0625
Time: 04:58:41 Log-Likelihood: -70.510
No. Observations: 15 AIC: 149.0
Df Residuals: 11 BIC: 151.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 387.3636 483.844 0.801 0.440 -677.570 1452.297
C(dose)[T.1] -519.4123 1254.052 -0.414 0.687 -3279.562 2240.737
expression -28.6462 43.309 -0.661 0.522 -123.970 66.677
expression:C(dose)[T.1] 50.0014 108.380 0.461 0.654 -188.542 288.544
Omnibus: 2.057 Durbin-Watson: 0.962
Prob(Omnibus): 0.358 Jarque-Bera (JB): 1.567
Skew: -0.729 Prob(JB): 0.457
Kurtosis: 2.382 Cond. No. 2.13e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.462
Model: OLS Adj. R-squared: 0.372
Method: Least Squares F-statistic: 5.148
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0243
Time: 04:58:41 Log-Likelihood: -70.654
No. Observations: 15 AIC: 147.3
Df Residuals: 12 BIC: 149.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 298.1886 428.764 0.695 0.500 -636.007 1232.384
C(dose)[T.1] 59.0343 23.996 2.460 0.030 6.752 111.316
expression -20.6617 38.377 -0.538 0.600 -104.278 62.955
Omnibus: 2.844 Durbin-Watson: 0.978
Prob(Omnibus): 0.241 Jarque-Bera (JB): 1.733
Skew: -0.829 Prob(JB): 0.420
Kurtosis: 2.844 Cond. No. 638.

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: 04:58:41 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: 0.190
Model: OLS Adj. R-squared: 0.128
Method: Least Squares F-statistic: 3.055
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.104
Time: 04:58:41 Log-Likelihood: -73.717
No. Observations: 15 AIC: 151.4
Df Residuals: 13 BIC: 152.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -491.5773 334.942 -1.468 0.166 -1215.175 232.021
expression 51.2364 29.312 1.748 0.104 -12.089 114.562
Omnibus: 0.844 Durbin-Watson: 1.048
Prob(Omnibus): 0.656 Jarque-Bera (JB): 0.784
Skew: -0.360 Prob(JB): 0.676
Kurtosis: 2.142 Cond. No. 422.