LID SPN TRAP

1450710_at

Jarid2 jumonji, AT rich interactive domain 2

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
0.273 0.607 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.751
Model: OLS Adj. R-squared: 0.712
Method: Least Squares F-statistic: 19.12
Date: Thu, 21 Nov 2024 Prob (F-statistic): 5.80e-06
Time: 04:01:23 Log-Likelihood: -97.109
No. Observations: 23 AIC: 202.2
Df Residuals: 19 BIC: 206.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -329.2172 313.449 -1.050 0.307 -985.273 326.839
C(dose)[T.1] 1460.4772 514.434 2.839 0.010 383.754 2537.200
expression 33.6421 27.498 1.223 0.236 -23.913 91.197
expression:C(dose)[T.1] -120.6641 44.253 -2.727 0.013 -213.286 -28.042
Omnibus: 0.886 Durbin-Watson: 1.469
Prob(Omnibus): 0.642 Jarque-Bera (JB): 0.753
Skew: 0.404 Prob(JB): 0.686
Kurtosis: 2.635 Cond. No. 1.96e+03

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: 18.88
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.47e-05
Time: 04:01:23 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 201.8050 282.368 0.715 0.483 -387.205 790.815
C(dose)[T.1] 58.0859 12.585 4.616 0.000 31.835 84.337
expression -12.9503 24.770 -0.523 0.607 -64.619 38.718
Omnibus: 1.399 Durbin-Watson: 1.971
Prob(Omnibus): 0.497 Jarque-Bera (JB): 0.943
Skew: 0.135 Prob(JB): 0.624
Kurtosis: 2.046 Cond. No. 758.

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: 04:01:23 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: 0.285
Model: OLS Adj. R-squared: 0.251
Method: Least Squares F-statistic: 8.371
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00870
Time: 04:01:23 Log-Likelihood: -109.25
No. Observations: 23 AIC: 222.5
Df Residuals: 21 BIC: 224.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -725.3303 278.322 -2.606 0.016 -1304.132 -146.529
expression 69.5653 24.044 2.893 0.009 19.562 119.568
Omnibus: 0.932 Durbin-Watson: 1.664
Prob(Omnibus): 0.627 Jarque-Bera (JB): 0.922
Skew: 0.371 Prob(JB): 0.631
Kurtosis: 2.359 Cond. No. 532.

CP101

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

F-statistic p-value df difference
0.049 0.829 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.614
Model: OLS Adj. R-squared: 0.509
Method: Least Squares F-statistic: 5.835
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0123
Time: 04:01:23 Log-Likelihood: -68.158
No. Observations: 15 AIC: 144.3
Df Residuals: 11 BIC: 147.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 347.7251 248.572 1.399 0.189 -199.377 894.828
C(dose)[T.1] -812.3843 400.141 -2.030 0.067 -1693.089 68.320
expression -24.4589 21.673 -1.129 0.283 -72.160 23.243
expression:C(dose)[T.1] 75.9002 35.200 2.156 0.054 -1.575 153.375
Omnibus: 3.372 Durbin-Watson: 1.343
Prob(Omnibus): 0.185 Jarque-Bera (JB): 2.307
Skew: -0.947 Prob(JB): 0.316
Kurtosis: 2.674 Cond. No. 843.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.451
Model: OLS Adj. R-squared: 0.360
Method: Least Squares F-statistic: 4.929
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0274
Time: 04:01:23 Log-Likelihood: -70.802
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 17.9910 223.790 0.080 0.937 -469.606 505.588
C(dose)[T.1] 49.8865 16.014 3.115 0.009 14.994 84.779
expression 4.3140 19.502 0.221 0.829 -38.178 46.806
Omnibus: 2.588 Durbin-Watson: 0.849
Prob(Omnibus): 0.274 Jarque-Bera (JB): 1.790
Skew: -0.823 Prob(JB): 0.409
Kurtosis: 2.605 Cond. No. 328.

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: 04:01:23 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: 0.007
Model: OLS Adj. R-squared: -0.069
Method: Least Squares F-statistic: 0.09259
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.766
Time: 04:01:23 Log-Likelihood: -75.247
No. Observations: 15 AIC: 154.5
Df Residuals: 13 BIC: 155.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 179.2110 281.321 0.637 0.535 -428.545 786.967
expression -7.5206 24.716 -0.304 0.766 -60.917 45.876
Omnibus: 1.034 Durbin-Watson: 1.605
Prob(Omnibus): 0.596 Jarque-Bera (JB): 0.740
Skew: 0.134 Prob(JB): 0.691
Kurtosis: 1.945 Cond. No. 319.