LID SPN TRAP

1455483_at

Zfp148 zinc finger protein 148

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
0.107 0.747 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.669
Model: OLS Adj. R-squared: 0.616
Method: Least Squares F-statistic: 12.78
Date: Thu, 21 Nov 2024 Prob (F-statistic): 8.39e-05
Time: 05:04:32 Log-Likelihood: -100.40
No. Observations: 23 AIC: 208.8
Df Residuals: 19 BIC: 213.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -47.6107 123.322 -0.386 0.704 -305.728 210.506
C(dose)[T.1] 273.0282 216.815 1.259 0.223 -180.771 726.828
expression 13.7197 16.597 0.827 0.419 -21.019 48.458
expression:C(dose)[T.1] -30.4040 30.196 -1.007 0.327 -93.605 32.797
Omnibus: 0.228 Durbin-Watson: 1.895
Prob(Omnibus): 0.892 Jarque-Bera (JB): 0.421
Skew: 0.125 Prob(JB): 0.810
Kurtosis: 2.386 Cond. No. 438.

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: 18.65
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.69e-05
Time: 05:04:32 Log-Likelihood: -101.00
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 20.5581 103.112 0.199 0.844 -194.531 235.647
C(dose)[T.1] 54.9538 10.048 5.469 0.000 33.994 75.913
expression 4.5342 13.870 0.327 0.747 -24.398 33.467
Omnibus: 0.188 Durbin-Watson: 1.918
Prob(Omnibus): 0.910 Jarque-Bera (JB): 0.396
Skew: 0.063 Prob(JB): 0.820
Kurtosis: 2.370 Cond. No. 175.

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: 05:04:32 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: 0.129
Model: OLS Adj. R-squared: 0.087
Method: Least Squares F-statistic: 3.106
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0926
Time: 05:04:32 Log-Likelihood: -111.52
No. Observations: 23 AIC: 227.0
Df Residuals: 21 BIC: 229.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 317.5634 135.134 2.350 0.029 36.537 598.590
expression -32.8024 18.614 -1.762 0.093 -71.512 5.907
Omnibus: 0.847 Durbin-Watson: 2.351
Prob(Omnibus): 0.655 Jarque-Bera (JB): 0.721
Skew: 0.012 Prob(JB): 0.697
Kurtosis: 2.133 Cond. No. 148.

CP101

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

F-statistic p-value df difference
4.028 0.068 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.588
Model: OLS Adj. R-squared: 0.475
Method: Least Squares F-statistic: 5.222
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0174
Time: 05:04:32 Log-Likelihood: -68.659
No. Observations: 15 AIC: 145.3
Df Residuals: 11 BIC: 148.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 398.1687 185.376 2.148 0.055 -9.842 806.179
C(dose)[T.1] 7.2216 446.250 0.016 0.987 -974.968 989.411
expression -43.6238 24.412 -1.787 0.101 -97.355 10.107
expression:C(dose)[T.1] 4.4051 60.280 0.073 0.943 -128.269 137.079
Omnibus: 4.606 Durbin-Watson: 1.321
Prob(Omnibus): 0.100 Jarque-Bera (JB): 2.399
Skew: -0.956 Prob(JB): 0.301
Kurtosis: 3.426 Cond. No. 561.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.587
Model: OLS Adj. R-squared: 0.519
Method: Least Squares F-statistic: 8.539
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00494
Time: 05:04:32 Log-Likelihood: -68.662
No. Observations: 15 AIC: 143.3
Df Residuals: 12 BIC: 145.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 392.6911 162.367 2.419 0.032 38.923 746.459
C(dose)[T.1] 39.8139 14.399 2.765 0.017 8.441 71.187
expression -42.9014 21.376 -2.007 0.068 -89.475 3.672
Omnibus: 4.554 Durbin-Watson: 1.308
Prob(Omnibus): 0.103 Jarque-Bera (JB): 2.358
Skew: -0.948 Prob(JB): 0.308
Kurtosis: 3.427 Cond. No. 182.

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: 05:04:32 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: 0.324
Model: OLS Adj. R-squared: 0.272
Method: Least Squares F-statistic: 6.241
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0267
Time: 05:04:32 Log-Likelihood: -72.359
No. Observations: 15 AIC: 148.7
Df Residuals: 13 BIC: 150.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 557.1730 185.721 3.000 0.010 155.947 958.399
expression -62.0906 24.854 -2.498 0.027 -115.784 -8.397
Omnibus: 2.293 Durbin-Watson: 2.245
Prob(Omnibus): 0.318 Jarque-Bera (JB): 1.212
Skew: 0.353 Prob(JB): 0.545
Kurtosis: 1.800 Cond. No. 169.