LID SPN TRAP

1416022_at

Fabp5 fatty acid binding protein 5, epidermal

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
1.194 0.287 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.728
Model: OLS Adj. R-squared: 0.685
Method: Least Squares F-statistic: 16.92
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.35e-05
Time: 05:17:21 Log-Likelihood: -98.148
No. Observations: 23 AIC: 204.3
Df Residuals: 19 BIC: 208.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 78.3859 208.282 0.376 0.711 -357.553 514.325
C(dose)[T.1] -723.1859 383.313 -1.887 0.075 -1525.469 79.098
expression -2.4573 21.162 -0.116 0.909 -46.750 41.835
expression:C(dose)[T.1] 78.7935 38.904 2.025 0.057 -2.633 160.220
Omnibus: 2.338 Durbin-Watson: 1.766
Prob(Omnibus): 0.311 Jarque-Bera (JB): 1.990
Skew: 0.668 Prob(JB): 0.370
Kurtosis: 2.458 Cond. No. 1.15e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.669
Model: OLS Adj. R-squared: 0.636
Method: Least Squares F-statistic: 20.20
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.59e-05
Time: 05:17:21 Log-Likelihood: -100.40
No. Observations: 23 AIC: 206.8
Df Residuals: 20 BIC: 210.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -150.9974 187.865 -0.804 0.431 -542.877 240.882
C(dose)[T.1] 52.9862 8.525 6.215 0.000 35.203 70.769
expression 20.8566 19.085 1.093 0.287 -18.953 60.667
Omnibus: 1.754 Durbin-Watson: 1.655
Prob(Omnibus): 0.416 Jarque-Bera (JB): 1.040
Skew: 0.129 Prob(JB): 0.594
Kurtosis: 1.990 Cond. No. 440.

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: 05:17:21 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: 0.029
Model: OLS Adj. R-squared: -0.017
Method: Least Squares F-statistic: 0.6316
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.436
Time: 05:17:21 Log-Likelihood: -112.76
No. Observations: 23 AIC: 229.5
Df Residuals: 21 BIC: 231.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -169.6516 313.861 -0.541 0.595 -822.361 483.057
expression 25.3246 31.866 0.795 0.436 -40.944 91.593
Omnibus: 2.024 Durbin-Watson: 2.457
Prob(Omnibus): 0.364 Jarque-Bera (JB): 1.117
Skew: 0.140 Prob(JB): 0.572
Kurtosis: 1.957 Cond. No. 439.

CP101

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

F-statistic p-value df difference
0.237 0.635 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.465
Model: OLS Adj. R-squared: 0.319
Method: Least Squares F-statistic: 3.183
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0670
Time: 05:17:21 Log-Likelihood: -70.613
No. Observations: 15 AIC: 149.2
Df Residuals: 11 BIC: 152.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 0.6116 620.420 0.001 0.999 -1364.923 1366.146
C(dose)[T.1] -262.1002 915.815 -0.286 0.780 -2277.795 1753.595
expression 6.7044 62.242 0.108 0.916 -130.288 143.697
expression:C(dose)[T.1] 29.1719 89.209 0.327 0.750 -167.176 225.520
Omnibus: 2.349 Durbin-Watson: 0.858
Prob(Omnibus): 0.309 Jarque-Bera (JB): 1.554
Skew: -0.771 Prob(JB): 0.460
Kurtosis: 2.675 Cond. No. 1.55e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.459
Model: OLS Adj. R-squared: 0.369
Method: Least Squares F-statistic: 5.100
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0249
Time: 05:17:21 Log-Likelihood: -70.686
No. Observations: 15 AIC: 147.4
Df Residuals: 12 BIC: 149.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -140.9132 427.676 -0.329 0.747 -1072.739 790.912
C(dose)[T.1] 37.2128 29.114 1.278 0.225 -26.222 100.647
expression 20.9050 42.898 0.487 0.635 -72.561 114.371
Omnibus: 2.464 Durbin-Watson: 0.820
Prob(Omnibus): 0.292 Jarque-Bera (JB): 1.688
Skew: -0.799 Prob(JB): 0.430
Kurtosis: 2.618 Cond. No. 572.

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: 05:17:21 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: 0.386
Model: OLS Adj. R-squared: 0.339
Method: Least Squares F-statistic: 8.169
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0134
Time: 05:17:21 Log-Likelihood: -71.643
No. Observations: 15 AIC: 147.3
Df Residuals: 13 BIC: 148.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -596.7718 241.707 -2.469 0.028 -1118.947 -74.597
expression 67.2166 23.518 2.858 0.013 16.409 118.025
Omnibus: 0.451 Durbin-Watson: 1.057
Prob(Omnibus): 0.798 Jarque-Bera (JB): 0.549
Skew: -0.254 Prob(JB): 0.760
Kurtosis: 2.212 Cond. No. 315.