LID SPN TRAP

1442032_at

BC030500 cDNA sequence BC030500

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
1.717 0.205 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.705
Model: OLS Adj. R-squared: 0.658
Method: Least Squares F-statistic: 15.11
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.88e-05
Time: 05:23:27 Log-Likelihood: -99.081
No. Observations: 23 AIC: 206.2
Df Residuals: 19 BIC: 210.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 356.8599 160.366 2.225 0.038 21.211 692.509
C(dose)[T.1] -263.7111 231.181 -1.141 0.268 -747.578 220.156
expression -31.7821 16.830 -1.888 0.074 -67.007 3.443
expression:C(dose)[T.1] 33.3759 24.953 1.338 0.197 -18.851 85.603
Omnibus: 0.108 Durbin-Watson: 1.966
Prob(Omnibus): 0.947 Jarque-Bera (JB): 0.307
Skew: -0.108 Prob(JB): 0.858
Kurtosis: 2.477 Cond. No. 670.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.677
Model: OLS Adj. R-squared: 0.644
Method: Least Squares F-statistic: 20.94
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.24e-05
Time: 05:23:27 Log-Likelihood: -100.12
No. Observations: 23 AIC: 206.2
Df Residuals: 20 BIC: 209.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 212.2804 120.777 1.758 0.094 -39.655 464.216
C(dose)[T.1] 45.2037 10.458 4.323 0.000 23.390 67.018
expression -16.5995 12.668 -1.310 0.205 -43.025 9.826
Omnibus: 0.525 Durbin-Watson: 1.940
Prob(Omnibus): 0.769 Jarque-Bera (JB): 0.608
Skew: 0.136 Prob(JB): 0.738
Kurtosis: 2.251 Cond. No. 271.

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: 05:23:27 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: 0.375
Model: OLS Adj. R-squared: 0.345
Method: Least Squares F-statistic: 12.59
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00190
Time: 05:23:27 Log-Likelihood: -107.70
No. Observations: 23 AIC: 219.4
Df Residuals: 21 BIC: 221.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 535.8056 128.654 4.165 0.000 268.254 803.357
expression -49.1032 13.838 -3.549 0.002 -77.880 -20.326
Omnibus: 1.580 Durbin-Watson: 1.808
Prob(Omnibus): 0.454 Jarque-Bera (JB): 1.398
Skew: 0.532 Prob(JB): 0.497
Kurtosis: 2.428 Cond. No. 212.

CP101

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

F-statistic p-value df difference
1.822 0.202 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.523
Model: OLS Adj. R-squared: 0.393
Method: Least Squares F-statistic: 4.023
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0371
Time: 05:23:27 Log-Likelihood: -69.746
No. Observations: 15 AIC: 147.5
Df Residuals: 11 BIC: 150.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 478.4350 482.753 0.991 0.343 -584.098 1540.968
C(dose)[T.1] 195.9911 739.752 0.265 0.796 -1432.191 1824.173
expression -40.0466 47.025 -0.852 0.413 -143.547 63.454
expression:C(dose)[T.1] -14.3254 72.080 -0.199 0.846 -172.972 144.321
Omnibus: 0.461 Durbin-Watson: 0.809
Prob(Omnibus): 0.794 Jarque-Bera (JB): 0.546
Skew: -0.302 Prob(JB): 0.761
Kurtosis: 2.286 Cond. No. 1.29e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.521
Model: OLS Adj. R-squared: 0.442
Method: Least Squares F-statistic: 6.538
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0120
Time: 05:23:27 Log-Likelihood: -69.773
No. Observations: 15 AIC: 145.5
Df Residuals: 12 BIC: 147.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 541.0124 350.989 1.541 0.149 -223.727 1305.752
C(dose)[T.1] 49.0008 14.666 3.341 0.006 17.046 80.956
expression -46.1438 34.183 -1.350 0.202 -120.622 28.334
Omnibus: 0.478 Durbin-Watson: 0.833
Prob(Omnibus): 0.788 Jarque-Bera (JB): 0.554
Skew: -0.312 Prob(JB): 0.758
Kurtosis: 2.294 Cond. No. 498.

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: 05:23:27 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: 0.076
Model: OLS Adj. R-squared: 0.005
Method: Least Squares F-statistic: 1.073
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.319
Time: 05:23:27 Log-Likelihood: -74.705
No. Observations: 15 AIC: 153.4
Df Residuals: 13 BIC: 154.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 578.7246 468.266 1.236 0.238 -432.903 1590.352
expression -47.2722 45.626 -1.036 0.319 -145.841 51.296
Omnibus: 3.216 Durbin-Watson: 1.679
Prob(Omnibus): 0.200 Jarque-Bera (JB): 1.337
Skew: 0.313 Prob(JB): 0.513
Kurtosis: 1.678 Cond. No. 497.