LID SPN TRAP

1459917_at

Ggnbp2 gametogenetin binding protein 2

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
0.007 0.936 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.655
Model: OLS Adj. R-squared: 0.600
Method: Least Squares F-statistic: 12.02
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000122
Time: 05:16:21 Log-Likelihood: -100.87
No. Observations: 23 AIC: 209.7
Df Residuals: 19 BIC: 214.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 43.1051 30.455 1.415 0.173 -20.638 106.848
C(dose)[T.1] 85.5913 58.280 1.469 0.158 -36.390 207.572
expression 2.8340 7.612 0.372 0.714 -13.098 18.766
expression:C(dose)[T.1] -7.9005 14.044 -0.563 0.580 -37.295 21.494
Omnibus: 0.364 Durbin-Watson: 1.978
Prob(Omnibus): 0.834 Jarque-Bera (JB): 0.516
Skew: -0.121 Prob(JB): 0.773
Kurtosis: 2.308 Cond. No. 67.9

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.50
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.82e-05
Time: 05:16:21 Log-Likelihood: -101.06
No. Observations: 23 AIC: 208.1
Df Residuals: 20 BIC: 211.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 52.1988 25.366 2.058 0.053 -0.714 105.111
C(dose)[T.1] 53.2054 8.916 5.968 0.000 34.607 71.803
expression 0.5129 6.287 0.082 0.936 -12.601 13.627
Omnibus: 0.391 Durbin-Watson: 1.913
Prob(Omnibus): 0.822 Jarque-Bera (JB): 0.523
Skew: 0.052 Prob(JB): 0.770
Kurtosis: 2.268 Cond. No. 25.3

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: 05:16:21 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: 0.024
Model: OLS Adj. R-squared: -0.022
Method: Least Squares F-statistic: 0.5273
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.476
Time: 05:16:21 Log-Likelihood: -112.82
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 50.1955 41.275 1.216 0.237 -35.641 136.032
expression 7.3061 10.061 0.726 0.476 -13.618 28.230
Omnibus: 1.457 Durbin-Watson: 2.655
Prob(Omnibus): 0.483 Jarque-Bera (JB): 1.098
Skew: 0.291 Prob(JB): 0.578
Kurtosis: 2.101 Cond. No. 25.1

CP101

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

F-statistic p-value df difference
0.210 0.655 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.479
Model: OLS Adj. R-squared: 0.337
Method: Least Squares F-statistic: 3.374
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0582
Time: 05:16:21 Log-Likelihood: -70.407
No. Observations: 15 AIC: 148.8
Df Residuals: 11 BIC: 151.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 28.8093 50.455 0.571 0.579 -82.241 139.860
C(dose)[T.1] 96.2219 74.635 1.289 0.224 -68.048 260.492
expression 11.2744 14.330 0.787 0.448 -20.266 42.815
expression:C(dose)[T.1] -13.5163 20.298 -0.666 0.519 -58.193 31.160
Omnibus: 2.666 Durbin-Watson: 0.917
Prob(Omnibus): 0.264 Jarque-Bera (JB): 1.825
Skew: -0.834 Prob(JB): 0.401
Kurtosis: 2.628 Cond. No. 49.1

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.458
Model: OLS Adj. R-squared: 0.368
Method: Least Squares F-statistic: 5.075
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0253
Time: 05:16:21 Log-Likelihood: -70.703
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 51.8854 35.810 1.449 0.173 -26.138 129.909
C(dose)[T.1] 47.7254 15.931 2.996 0.011 13.014 82.437
expression 4.5376 9.911 0.458 0.655 -17.056 26.132
Omnibus: 2.292 Durbin-Watson: 0.882
Prob(Omnibus): 0.318 Jarque-Bera (JB): 1.522
Skew: -0.762 Prob(JB): 0.467
Kurtosis: 2.665 Cond. No. 18.3

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: 05:16:21 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: 0.053
Model: OLS Adj. R-squared: -0.020
Method: Least Squares F-statistic: 0.7287
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.409
Time: 05:16:21 Log-Likelihood: -74.891
No. Observations: 15 AIC: 153.8
Df Residuals: 13 BIC: 155.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 55.7938 45.456 1.227 0.241 -42.408 153.995
expression 10.5253 12.330 0.854 0.409 -16.112 37.163
Omnibus: 0.669 Durbin-Watson: 1.486
Prob(Omnibus): 0.716 Jarque-Bera (JB): 0.667
Skew: 0.270 Prob(JB): 0.716
Kurtosis: 2.120 Cond. No. 18.1