LID SPN TRAP

1460136_at

AW047481 expressed sequence AW047481

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
6.145 0.022 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.752
Model: OLS Adj. R-squared: 0.713
Method: Least Squares F-statistic: 19.21
Date: Thu, 21 Nov 2024 Prob (F-statistic): 5.61e-06
Time: 05:01:06 Log-Likelihood: -97.068
No. Observations: 23 AIC: 202.1
Df Residuals: 19 BIC: 206.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 95.2341 33.068 2.880 0.010 26.022 164.446
C(dose)[T.1] 120.2890 54.500 2.207 0.040 6.219 234.359
expression -7.9051 6.292 -1.256 0.224 -21.074 5.263
expression:C(dose)[T.1] -13.1268 10.472 -1.254 0.225 -35.044 8.790
Omnibus: 0.016 Durbin-Watson: 1.551
Prob(Omnibus): 0.992 Jarque-Bera (JB): 0.122
Skew: -0.039 Prob(JB): 0.941
Kurtosis: 2.652 Cond. No. 95.2

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.732
Model: OLS Adj. R-squared: 0.705
Method: Least Squares F-statistic: 27.25
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.94e-06
Time: 05:01:06 Log-Likelihood: -97.982
No. Observations: 23 AIC: 202.0
Df Residuals: 20 BIC: 205.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 119.8265 26.998 4.438 0.000 63.510 176.143
C(dose)[T.1] 52.6318 7.676 6.857 0.000 36.621 68.643
expression -12.6437 5.101 -2.479 0.022 -23.284 -2.004
Omnibus: 0.135 Durbin-Watson: 1.714
Prob(Omnibus): 0.935 Jarque-Bera (JB): 0.284
Skew: -0.151 Prob(JB): 0.868
Kurtosis: 2.548 Cond. No. 38.3

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: 05:01:06 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: 0.100
Model: OLS Adj. R-squared: 0.058
Method: Least Squares F-statistic: 2.344
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.141
Time: 05:01:06 Log-Likelihood: -111.89
No. Observations: 23 AIC: 227.8
Df Residuals: 21 BIC: 230.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 151.6923 47.510 3.193 0.004 52.890 250.494
expression -13.9403 9.106 -1.531 0.141 -32.877 4.996
Omnibus: 3.775 Durbin-Watson: 2.269
Prob(Omnibus): 0.151 Jarque-Bera (JB): 1.449
Skew: 0.102 Prob(JB): 0.485
Kurtosis: 1.787 Cond. No. 37.5

CP101

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

F-statistic p-value df difference
0.002 0.964 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.507
Model: OLS Adj. R-squared: 0.373
Method: Least Squares F-statistic: 3.771
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0440
Time: 05:01:06 Log-Likelihood: -69.995
No. Observations: 15 AIC: 148.0
Df Residuals: 11 BIC: 150.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -35.1395 107.107 -0.328 0.749 -270.880 200.601
C(dose)[T.1] 189.5222 124.150 1.527 0.155 -83.731 462.775
expression 18.7552 19.475 0.963 0.356 -24.109 61.619
expression:C(dose)[T.1] -25.6178 22.489 -1.139 0.279 -75.115 23.880
Omnibus: 2.012 Durbin-Watson: 1.170
Prob(Omnibus): 0.366 Jarque-Bera (JB): 1.366
Skew: -0.715 Prob(JB): 0.505
Kurtosis: 2.621 Cond. No. 136.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 4.887
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0280
Time: 05:01:06 Log-Likelihood: -70.832
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 69.9229 55.128 1.268 0.229 -50.192 190.037
C(dose)[T.1] 49.2115 15.742 3.126 0.009 14.913 83.510
expression -0.4561 9.859 -0.046 0.964 -21.937 21.025
Omnibus: 2.744 Durbin-Watson: 0.809
Prob(Omnibus): 0.254 Jarque-Bera (JB): 1.884
Skew: -0.848 Prob(JB): 0.390
Kurtosis: 2.627 Cond. No. 40.3

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: 05:01:06 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: 0.000
Model: OLS Adj. R-squared: -0.077
Method: Least Squares F-statistic: 0.0002052
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.989
Time: 05:01:06 Log-Likelihood: -75.300
No. Observations: 15 AIC: 154.6
Df Residuals: 13 BIC: 156.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 92.6641 70.721 1.310 0.213 -60.120 245.448
expression 0.1827 12.756 0.014 0.989 -27.376 27.741
Omnibus: 0.599 Durbin-Watson: 1.619
Prob(Omnibus): 0.741 Jarque-Bera (JB): 0.580
Skew: 0.050 Prob(JB): 0.748
Kurtosis: 2.042 Cond. No. 39.8