LID SPN TRAP

1460330_at

Anxa3 annexin A3

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
0.094 0.763 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.661
Model: OLS Adj. R-squared: 0.607
Method: Least Squares F-statistic: 12.35
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000104
Time: 04:04:07 Log-Likelihood: -100.66
No. Observations: 23 AIC: 209.3
Df Residuals: 19 BIC: 213.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 32.3152 145.422 0.222 0.827 -272.057 336.687
C(dose)[T.1] 268.1192 280.541 0.956 0.351 -319.060 855.298
expression 3.2028 21.255 0.151 0.882 -41.285 47.691
expression:C(dose)[T.1] -30.2358 39.768 -0.760 0.456 -113.471 52.999
Omnibus: 1.125 Durbin-Watson: 1.844
Prob(Omnibus): 0.570 Jarque-Bera (JB): 0.863
Skew: 0.151 Prob(JB): 0.650
Kurtosis: 2.100 Cond. No. 540.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.651
Model: OLS Adj. R-squared: 0.616
Method: Least Squares F-statistic: 18.63
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.70e-05
Time: 04:04:07 Log-Likelihood: -101.01
No. Observations: 23 AIC: 208.0
Df Residuals: 20 BIC: 211.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 91.3596 121.647 0.751 0.461 -162.391 345.110
C(dose)[T.1] 54.9662 10.244 5.366 0.000 33.598 76.335
expression -5.4350 17.774 -0.306 0.763 -42.511 31.641
Omnibus: 0.873 Durbin-Watson: 1.909
Prob(Omnibus): 0.646 Jarque-Bera (JB): 0.735
Skew: 0.049 Prob(JB): 0.692
Kurtosis: 2.130 Cond. No. 199.

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: 04:04:07 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: 0.148
Model: OLS Adj. R-squared: 0.107
Method: Least Squares F-statistic: 3.643
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0701
Time: 04:04:07 Log-Likelihood: -111.27
No. Observations: 23 AIC: 226.5
Df Residuals: 21 BIC: 228.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -228.5174 161.631 -1.414 0.172 -564.647 107.612
expression 44.1665 23.140 1.909 0.070 -3.956 92.289
Omnibus: 0.483 Durbin-Watson: 2.213
Prob(Omnibus): 0.786 Jarque-Bera (JB): 0.591
Skew: 0.264 Prob(JB): 0.744
Kurtosis: 2.419 Cond. No. 173.

CP101

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

F-statistic p-value df difference
13.591 0.003 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: 0.684
Method: Least Squares F-statistic: 11.10
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00118
Time: 04:04:07 Log-Likelihood: -64.854
No. Observations: 15 AIC: 137.7
Df Residuals: 11 BIC: 140.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -190.2764 150.880 -1.261 0.233 -522.361 141.809
C(dose)[T.1] -89.8401 194.498 -0.462 0.653 -517.927 338.247
expression 29.4690 17.229 1.710 0.115 -8.451 67.389
expression:C(dose)[T.1] 14.7081 21.975 0.669 0.517 -33.659 63.075
Omnibus: 7.182 Durbin-Watson: 0.793
Prob(Omnibus): 0.028 Jarque-Bera (JB): 4.026
Skew: -1.184 Prob(JB): 0.134
Kurtosis: 3.913 Cond. No. 445.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.742
Model: OLS Adj. R-squared: 0.698
Method: Least Squares F-statistic: 17.21
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000298
Time: 04:04:07 Log-Likelihood: -65.153
No. Observations: 15 AIC: 136.3
Df Residuals: 12 BIC: 138.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -269.3366 91.687 -2.938 0.012 -469.105 -69.568
C(dose)[T.1] 40.1174 11.056 3.629 0.003 16.029 64.206
expression 38.5097 10.446 3.687 0.003 15.750 61.269
Omnibus: 2.736 Durbin-Watson: 0.868
Prob(Omnibus): 0.255 Jarque-Bera (JB): 1.430
Skew: -0.756 Prob(JB): 0.489
Kurtosis: 3.047 Cond. No. 154.

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: 04:04:07 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: 0.458
Model: OLS Adj. R-squared: 0.416
Method: Least Squares F-statistic: 10.98
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00560
Time: 04:04:07 Log-Likelihood: -70.708
No. Observations: 15 AIC: 145.4
Df Residuals: 13 BIC: 146.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -322.8369 125.910 -2.564 0.024 -594.849 -50.825
expression 46.9528 14.169 3.314 0.006 16.343 77.563
Omnibus: 4.252 Durbin-Watson: 1.946
Prob(Omnibus): 0.119 Jarque-Bera (JB): 1.379
Skew: -0.201 Prob(JB): 0.502
Kurtosis: 1.570 Cond. No. 151.