LID SPN TRAP

1449319_at

Rspo1 R-spondin homolog (Xenopus laevis)

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
4.171 0.055 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.710
Model: OLS Adj. R-squared: 0.664
Method: Least Squares F-statistic: 15.49
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.44e-05
Time: 22:51:22 Log-Likelihood: -98.880
No. Observations: 23 AIC: 205.8
Df Residuals: 19 BIC: 210.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -108.0583 121.456 -0.890 0.385 -362.269 146.152
C(dose)[T.1] 57.6013 161.938 0.356 0.726 -281.338 396.541
expression 22.9545 17.163 1.337 0.197 -12.967 58.876
expression:C(dose)[T.1] -1.9227 22.298 -0.086 0.932 -48.593 44.748
Omnibus: 0.307 Durbin-Watson: 2.121
Prob(Omnibus): 0.858 Jarque-Bera (JB): 0.476
Skew: 0.059 Prob(JB): 0.788
Kurtosis: 2.305 Cond. No. 398.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.710
Model: OLS Adj. R-squared: 0.681
Method: Least Squares F-statistic: 24.44
Date: Thu, 03 Apr 2025 Prob (F-statistic): 4.26e-06
Time: 22:51:22 Log-Likelihood: -98.884
No. Observations: 23 AIC: 203.8
Df Residuals: 20 BIC: 207.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -100.0061 75.710 -1.321 0.201 -257.935 57.922
C(dose)[T.1] 43.6619 9.278 4.706 0.000 24.308 63.015
expression 21.8154 10.682 2.042 0.055 -0.466 44.097
Omnibus: 0.286 Durbin-Watson: 2.086
Prob(Omnibus): 0.867 Jarque-Bera (JB): 0.464
Skew: 0.068 Prob(JB): 0.793
Kurtosis: 2.318 Cond. No. 142.

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, 03 Apr 2025 Prob (F-statistic): 3.51e-06
Time: 22:51:22 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: 0.388
Model: OLS Adj. R-squared: 0.359
Method: Least Squares F-statistic: 13.32
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00150
Time: 22:51:22 Log-Likelihood: -107.46
No. Observations: 23 AIC: 218.9
Df Residuals: 21 BIC: 221.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -266.0073 94.904 -2.803 0.011 -463.371 -68.644
expression 47.4821 13.011 3.649 0.001 20.424 74.540
Omnibus: 1.107 Durbin-Watson: 2.629
Prob(Omnibus): 0.575 Jarque-Bera (JB): 1.049
Skew: 0.420 Prob(JB): 0.592
Kurtosis: 2.377 Cond. No. 125.

CP101

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

F-statistic p-value df difference
2.878 0.116 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.559
Model: OLS Adj. R-squared: 0.438
Method: Least Squares F-statistic: 4.641
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0248
Time: 22:51:22 Log-Likelihood: -69.166
No. Observations: 15 AIC: 146.3
Df Residuals: 11 BIC: 149.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -157.4231 173.621 -0.907 0.384 -539.560 224.714
C(dose)[T.1] -34.2259 346.932 -0.099 0.923 -797.817 729.365
expression 33.5885 25.886 1.298 0.221 -23.386 90.563
expression:C(dose)[T.1] 15.4060 54.280 0.284 0.782 -104.064 134.876
Omnibus: 1.036 Durbin-Watson: 1.116
Prob(Omnibus): 0.596 Jarque-Bera (JB): 0.504
Skew: -0.442 Prob(JB): 0.777
Kurtosis: 2.838 Cond. No. 373.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.555
Model: OLS Adj. R-squared: 0.481
Method: Least Squares F-statistic: 7.496
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00772
Time: 22:51:23 Log-Likelihood: -69.221
No. Observations: 15 AIC: 144.4
Df Residuals: 12 BIC: 146.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -180.8781 146.726 -1.233 0.241 -500.566 138.810
C(dose)[T.1] 64.1185 16.649 3.851 0.002 27.844 100.393
expression 37.0922 21.864 1.697 0.116 -10.545 84.729
Omnibus: 1.526 Durbin-Watson: 1.155
Prob(Omnibus): 0.466 Jarque-Bera (JB): 0.851
Skew: -0.576 Prob(JB): 0.653
Kurtosis: 2.810 Cond. No. 139.

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, 03 Apr 2025 Prob (F-statistic): 0.00629
Time: 22:51:23 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: 0.006
Model: OLS Adj. R-squared: -0.071
Method: Least Squares F-statistic: 0.07686
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.786
Time: 22:51:23 Log-Likelihood: -75.256
No. Observations: 15 AIC: 154.5
Df Residuals: 13 BIC: 155.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 141.5748 173.108 0.818 0.428 -232.402 515.552
expression -7.3935 26.669 -0.277 0.786 -65.009 50.222
Omnibus: 0.527 Durbin-Watson: 1.498
Prob(Omnibus): 0.768 Jarque-Bera (JB): 0.551
Skew: -0.034 Prob(JB): 0.759
Kurtosis: 2.063 Cond. No. 114.