LID SPN TRAP

1434343_at

Zfp954 zinc finger protein 954

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
0.212 0.650 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.657
Model: OLS Adj. R-squared: 0.602
Method: Least Squares F-statistic: 12.11
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000117
Time: 04:45:05 Log-Likelihood: -100.81
No. Observations: 23 AIC: 209.6
Df Residuals: 19 BIC: 214.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 46.4570 118.345 0.393 0.699 -201.241 294.155
C(dose)[T.1] -35.2085 191.391 -0.184 0.856 -435.794 365.377
expression 1.1240 17.138 0.066 0.948 -34.747 36.995
expression:C(dose)[T.1] 12.5890 27.417 0.459 0.651 -44.795 69.973
Omnibus: 0.526 Durbin-Watson: 2.039
Prob(Omnibus): 0.769 Jarque-Bera (JB): 0.602
Skew: 0.111 Prob(JB): 0.740
Kurtosis: 2.239 Cond. No. 378.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.653
Model: OLS Adj. R-squared: 0.618
Method: Least Squares F-statistic: 18.80
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.55e-05
Time: 04:45:05 Log-Likelihood: -100.94
No. Observations: 23 AIC: 207.9
Df Residuals: 20 BIC: 211.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 12.5351 90.611 0.138 0.891 -176.477 201.547
C(dose)[T.1] 52.5738 8.879 5.921 0.000 34.052 71.096
expression 6.0431 13.111 0.461 0.650 -21.305 33.391
Omnibus: 0.281 Durbin-Watson: 1.959
Prob(Omnibus): 0.869 Jarque-Bera (JB): 0.461
Skew: 0.117 Prob(JB): 0.794
Kurtosis: 2.347 Cond. No. 148.

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: 04:45:05 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: 0.044
Model: OLS Adj. R-squared: -0.001
Method: Least Squares F-statistic: 0.9681
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.336
Time: 04:45:05 Log-Likelihood: -112.59
No. Observations: 23 AIC: 229.2
Df Residuals: 21 BIC: 231.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -63.0329 145.253 -0.434 0.669 -365.104 239.038
expression 20.5207 20.856 0.984 0.336 -22.851 63.893
Omnibus: 2.746 Durbin-Watson: 2.479
Prob(Omnibus): 0.253 Jarque-Bera (JB): 1.394
Skew: 0.249 Prob(JB): 0.498
Kurtosis: 1.901 Cond. No. 146.

CP101

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

F-statistic p-value df difference
0.145 0.710 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.554
Model: OLS Adj. R-squared: 0.432
Method: Least Squares F-statistic: 4.550
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0263
Time: 04:45:05 Log-Likelihood: -69.249
No. Observations: 15 AIC: 146.5
Df Residuals: 11 BIC: 149.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -224.8575 249.219 -0.902 0.386 -773.385 323.670
C(dose)[T.1] 489.8643 283.575 1.727 0.112 -134.280 1114.009
expression 43.6314 37.168 1.174 0.265 -38.174 125.437
expression:C(dose)[T.1] -65.9322 42.339 -1.557 0.148 -159.120 27.255
Omnibus: 0.993 Durbin-Watson: 0.976
Prob(Omnibus): 0.609 Jarque-Bera (JB): 0.880
Skew: -0.479 Prob(JB): 0.644
Kurtosis: 2.298 Cond. No. 393.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.455
Model: OLS Adj. R-squared: 0.365
Method: Least Squares F-statistic: 5.017
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0261
Time: 04:45:05 Log-Likelihood: -70.743
No. Observations: 15 AIC: 147.5
Df Residuals: 12 BIC: 149.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 115.5145 126.643 0.912 0.380 -160.417 391.446
C(dose)[T.1] 48.8709 15.668 3.119 0.009 14.732 83.010
expression -7.1781 18.828 -0.381 0.710 -48.200 33.844
Omnibus: 2.752 Durbin-Watson: 0.883
Prob(Omnibus): 0.253 Jarque-Bera (JB): 1.899
Skew: -0.850 Prob(JB): 0.387
Kurtosis: 2.616 Cond. No. 111.

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: 04:45:05 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: 0.014
Model: OLS Adj. R-squared: -0.062
Method: Least Squares F-statistic: 0.1823
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.676
Time: 04:45:05 Log-Likelihood: -75.196
No. Observations: 15 AIC: 154.4
Df Residuals: 13 BIC: 155.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 162.9377 162.544 1.002 0.334 -188.218 514.094
expression -10.3780 24.305 -0.427 0.676 -62.886 42.130
Omnibus: 0.100 Durbin-Watson: 1.760
Prob(Omnibus): 0.951 Jarque-Bera (JB): 0.319
Skew: -0.082 Prob(JB): 0.852
Kurtosis: 2.304 Cond. No. 110.