LID SPN TRAP

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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
0.262 0.615 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.658
Model: OLS Adj. R-squared: 0.604
Method: Least Squares F-statistic: 12.20
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000111
Time: 04:59:45 Log-Likelihood: -100.75
No. Observations: 23 AIC: 209.5
Df Residuals: 19 BIC: 214.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -7.3109 85.770 -0.085 0.933 -186.830 172.208
C(dose)[T.1] 114.7307 118.749 0.966 0.346 -133.815 363.276
expression 9.1606 12.739 0.719 0.481 -17.502 35.824
expression:C(dose)[T.1] -9.1416 17.758 -0.515 0.613 -46.309 28.026
Omnibus: 0.715 Durbin-Watson: 1.913
Prob(Omnibus): 0.699 Jarque-Bera (JB): 0.675
Skew: -0.056 Prob(JB): 0.714
Kurtosis: 2.168 Cond. No. 239.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.654
Model: OLS Adj. R-squared: 0.619
Method: Least Squares F-statistic: 18.87
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.49e-05
Time: 04:59:45 Log-Likelihood: -100.91
No. Observations: 23 AIC: 207.8
Df Residuals: 20 BIC: 211.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 24.2835 58.805 0.413 0.684 -98.382 146.949
C(dose)[T.1] 53.7712 8.754 6.142 0.000 35.510 72.032
expression 4.4560 8.710 0.512 0.615 -13.714 22.626
Omnibus: 0.407 Durbin-Watson: 1.848
Prob(Omnibus): 0.816 Jarque-Bera (JB): 0.539
Skew: -0.110 Prob(JB): 0.764
Kurtosis: 2.283 Cond. No. 92.6

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: 04:59:45 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: 0.000
Model: OLS Adj. R-squared: -0.047
Method: Least Squares F-statistic: 0.002574
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.960
Time: 04:59:45 Log-Likelihood: -113.10
No. Observations: 23 AIC: 230.2
Df Residuals: 21 BIC: 232.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 84.5808 96.131 0.880 0.389 -115.334 284.496
expression -0.7293 14.374 -0.051 0.960 -30.621 29.163
Omnibus: 3.237 Durbin-Watson: 2.491
Prob(Omnibus): 0.198 Jarque-Bera (JB): 1.560
Skew: 0.291 Prob(JB): 0.458
Kurtosis: 1.865 Cond. No. 91.1

CP101

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

F-statistic p-value df difference
0.401 0.538 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.546
Model: OLS Adj. R-squared: 0.422
Method: Least Squares F-statistic: 4.409
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0288
Time: 04:59:45 Log-Likelihood: -69.379
No. Observations: 15 AIC: 146.8
Df Residuals: 11 BIC: 149.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 57.3768 139.937 0.410 0.690 -250.623 365.377
C(dose)[T.1] -435.1316 351.765 -1.237 0.242 -1209.361 339.098
expression 1.4338 19.901 0.072 0.944 -42.367 45.235
expression:C(dose)[T.1] 71.5999 51.641 1.386 0.193 -42.062 185.262
Omnibus: 1.574 Durbin-Watson: 1.153
Prob(Omnibus): 0.455 Jarque-Bera (JB): 1.089
Skew: -0.628 Prob(JB): 0.580
Kurtosis: 2.594 Cond. No. 389.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.467
Model: OLS Adj. R-squared: 0.378
Method: Least Squares F-statistic: 5.249
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0230
Time: 04:59:45 Log-Likelihood: -70.586
No. Observations: 15 AIC: 147.2
Df Residuals: 12 BIC: 149.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -17.1645 134.071 -0.128 0.900 -309.279 274.950
C(dose)[T.1] 52.1075 16.151 3.226 0.007 16.917 87.298
expression 12.0667 19.056 0.633 0.538 -29.453 53.587
Omnibus: 3.556 Durbin-Watson: 0.832
Prob(Omnibus): 0.169 Jarque-Bera (JB): 2.340
Skew: -0.961 Prob(JB): 0.310
Kurtosis: 2.776 Cond. No. 123.

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: 04:59:45 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: 0.004
Model: OLS Adj. R-squared: -0.073
Method: Least Squares F-statistic: 0.05131
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.824
Time: 04:59:45 Log-Likelihood: -75.271
No. Observations: 15 AIC: 154.5
Df Residuals: 13 BIC: 156.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 131.0536 165.366 0.793 0.442 -226.198 488.305
expression -5.4328 23.984 -0.227 0.824 -57.248 46.382
Omnibus: 1.071 Durbin-Watson: 1.578
Prob(Omnibus): 0.585 Jarque-Bera (JB): 0.734
Skew: 0.073 Prob(JB): 0.693
Kurtosis: 1.926 Cond. No. 115.