LID SPN TRAP

1460196_at

Cbr1 carbonyl reductase 1

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
0.579 0.456 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.661
Model: OLS Adj. R-squared: 0.607
Method: Least Squares F-statistic: 12.35
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000104
Time: 04:31:39 Log-Likelihood: -100.66
No. Observations: 23 AIC: 209.3
Df Residuals: 19 BIC: 213.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 166.7179 266.784 0.625 0.539 -391.667 725.102
C(dose)[T.1] 223.2283 483.312 0.462 0.649 -788.356 1234.812
expression -11.2405 26.647 -0.422 0.678 -67.012 44.531
expression:C(dose)[T.1] -16.0487 47.184 -0.340 0.737 -114.805 82.708
Omnibus: 0.084 Durbin-Watson: 1.921
Prob(Omnibus): 0.959 Jarque-Bera (JB): 0.204
Skew: -0.123 Prob(JB): 0.903
Kurtosis: 2.609 Cond. No. 1.35e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.659
Model: OLS Adj. R-squared: 0.625
Method: Least Squares F-statistic: 19.32
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.13e-05
Time: 04:31:39 Log-Likelihood: -100.73
No. Observations: 23 AIC: 207.5
Df Residuals: 20 BIC: 210.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 217.9501 215.272 1.012 0.323 -231.100 667.000
C(dose)[T.1] 58.8848 11.309 5.207 0.000 35.294 82.476
expression -16.3589 21.499 -0.761 0.456 -61.205 28.487
Omnibus: 0.089 Durbin-Watson: 1.980
Prob(Omnibus): 0.956 Jarque-Bera (JB): 0.297
Skew: -0.086 Prob(JB): 0.862
Kurtosis: 2.471 Cond. No. 513.

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: 04:31:39 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: 0.197
Model: OLS Adj. R-squared: 0.158
Method: Least Squares F-statistic: 5.139
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0341
Time: 04:31:39 Log-Likelihood: -110.59
No. Observations: 23 AIC: 225.2
Df Residuals: 21 BIC: 227.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -487.8970 250.467 -1.948 0.065 -1008.771 32.977
expression 55.8044 24.616 2.267 0.034 4.612 106.997
Omnibus: 3.874 Durbin-Watson: 2.364
Prob(Omnibus): 0.144 Jarque-Bera (JB): 1.719
Skew: 0.314 Prob(JB): 0.423
Kurtosis: 1.817 Cond. No. 398.

CP101

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

F-statistic p-value df difference
1.680 0.219 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.516
Model: OLS Adj. R-squared: 0.385
Method: Least Squares F-statistic: 3.917
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0398
Time: 04:31:39 Log-Likelihood: -69.850
No. Observations: 15 AIC: 147.7
Df Residuals: 11 BIC: 150.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -381.4310 546.749 -0.698 0.500 -1584.818 821.956
C(dose)[T.1] 48.3831 729.735 0.066 0.948 -1557.753 1654.519
expression 48.7858 59.413 0.821 0.429 -81.981 179.552
expression:C(dose)[T.1] 0.0881 79.296 0.001 0.999 -174.441 174.617
Omnibus: 4.206 Durbin-Watson: 0.850
Prob(Omnibus): 0.122 Jarque-Bera (JB): 2.057
Skew: -0.877 Prob(JB): 0.357
Kurtosis: 3.467 Cond. No. 1.21e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.516
Model: OLS Adj. R-squared: 0.436
Method: Least Squares F-statistic: 6.409
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0128
Time: 04:31:39 Log-Likelihood: -69.850
No. Observations: 15 AIC: 145.7
Df Residuals: 12 BIC: 147.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -381.8860 346.781 -1.101 0.292 -1137.457 373.685
C(dose)[T.1] 49.1937 14.741 3.337 0.006 17.075 81.312
expression 48.8352 37.673 1.296 0.219 -33.247 130.917
Omnibus: 4.204 Durbin-Watson: 0.850
Prob(Omnibus): 0.122 Jarque-Bera (JB): 2.056
Skew: -0.876 Prob(JB): 0.358
Kurtosis: 3.466 Cond. No. 440.

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: 04:31:39 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: 0.068
Model: OLS Adj. R-squared: -0.004
Method: Least Squares F-statistic: 0.9449
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.349
Time: 04:31:39 Log-Likelihood: -74.774
No. Observations: 15 AIC: 153.5
Df Residuals: 13 BIC: 155.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -355.8150 462.510 -0.769 0.455 -1355.008 643.378
expression 48.8532 50.258 0.972 0.349 -59.723 157.429
Omnibus: 1.926 Durbin-Watson: 1.495
Prob(Omnibus): 0.382 Jarque-Bera (JB): 0.945
Skew: 0.098 Prob(JB): 0.623
Kurtosis: 1.786 Cond. No. 439.