LID SPN TRAP

1430309_at

Nipbl Nipped-B homolog (Drosophila)

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
0.018 0.894 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.594
Method: Least Squares F-statistic: 11.74
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.000141
Time: 22:44:35 Log-Likelihood: -101.05
No. Observations: 23 AIC: 210.1
Df Residuals: 19 BIC: 214.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 67.2852 174.777 0.385 0.705 -298.528 433.098
C(dose)[T.1] 97.3147 424.338 0.229 0.821 -790.835 985.465
expression -1.5187 20.285 -0.075 0.941 -43.977 40.939
expression:C(dose)[T.1] -4.7544 47.101 -0.101 0.921 -103.337 93.828
Omnibus: 0.429 Durbin-Watson: 1.936
Prob(Omnibus): 0.807 Jarque-Bera (JB): 0.547
Skew: 0.082 Prob(JB): 0.761
Kurtosis: 2.263 Cond. No. 987.

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: 18.52
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.81e-05
Time: 22:44:35 Log-Likelihood: -101.05
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 74.8786 153.806 0.487 0.632 -245.956 395.713
C(dose)[T.1] 54.5006 12.315 4.425 0.000 28.811 80.190
expression -2.4006 17.849 -0.134 0.894 -39.633 34.832
Omnibus: 0.423 Durbin-Watson: 1.922
Prob(Omnibus): 0.809 Jarque-Bera (JB): 0.541
Skew: 0.057 Prob(JB): 0.763
Kurtosis: 2.257 Cond. No. 316.

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: 22:44:35 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: 0.306
Model: OLS Adj. R-squared: 0.273
Method: Least Squares F-statistic: 9.261
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00618
Time: 22:44:35 Log-Likelihood: -108.90
No. Observations: 23 AIC: 221.8
Df Residuals: 21 BIC: 224.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -389.6363 154.347 -2.524 0.020 -710.618 -68.654
expression 53.0809 17.442 3.043 0.006 16.807 89.354
Omnibus: 3.933 Durbin-Watson: 1.721
Prob(Omnibus): 0.140 Jarque-Bera (JB): 1.493
Skew: 0.128 Prob(JB): 0.474
Kurtosis: 1.778 Cond. No. 230.

CP101

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

F-statistic p-value df difference
2.339 0.152 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.561
Model: OLS Adj. R-squared: 0.441
Method: Least Squares F-statistic: 4.686
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0241
Time: 22:44:35 Log-Likelihood: -69.126
No. Observations: 15 AIC: 146.3
Df Residuals: 11 BIC: 149.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 223.8464 200.868 1.114 0.289 -218.260 665.953
C(dose)[T.1] 315.5479 348.623 0.905 0.385 -451.767 1082.863
expression -19.2924 24.740 -0.780 0.452 -73.744 35.159
expression:C(dose)[T.1] -31.6020 42.276 -0.748 0.470 -124.650 61.446
Omnibus: 1.171 Durbin-Watson: 0.777
Prob(Omnibus): 0.557 Jarque-Bera (JB): 0.622
Skew: -0.489 Prob(JB): 0.733
Kurtosis: 2.807 Cond. No. 495.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.539
Model: OLS Adj. R-squared: 0.462
Method: Least Squares F-statistic: 7.007
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00964
Time: 22:44:36 Log-Likelihood: -69.497
No. Observations: 15 AIC: 145.0
Df Residuals: 12 BIC: 147.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 311.5910 159.978 1.948 0.075 -36.970 660.152
C(dose)[T.1] 55.1912 14.922 3.699 0.003 22.678 87.704
expression -30.1148 19.689 -1.530 0.152 -73.013 12.783
Omnibus: 1.607 Durbin-Watson: 0.752
Prob(Omnibus): 0.448 Jarque-Bera (JB): 1.191
Skew: -0.642 Prob(JB): 0.551
Kurtosis: 2.493 Cond. No. 186.

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: 22:44:36 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: 0.013
Model: OLS Adj. R-squared: -0.063
Method: Least Squares F-statistic: 0.1694
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.687
Time: 22:44:36 Log-Likelihood: -75.203
No. Observations: 15 AIC: 154.4
Df Residuals: 13 BIC: 155.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 183.9289 219.546 0.838 0.417 -290.372 658.230
expression -10.9890 26.700 -0.412 0.687 -68.672 46.694
Omnibus: 1.559 Durbin-Watson: 1.734
Prob(Omnibus): 0.459 Jarque-Bera (JB): 0.866
Skew: 0.104 Prob(JB): 0.648
Kurtosis: 1.841 Cond. No. 181.