LID SPN TRAP

1456934_at

Calb1 calbindin 1

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
2.699 0.116 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.691
Model: OLS Adj. R-squared: 0.642
Method: Least Squares F-statistic: 14.16
Date: Thu, 21 Nov 2024 Prob (F-statistic): 4.39e-05
Time: 04:07:31 Log-Likelihood: -99.602
No. Observations: 23 AIC: 207.2
Df Residuals: 19 BIC: 211.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 452.6129 373.318 1.212 0.240 -328.750 1233.976
C(dose)[T.1] 9.7747 476.654 0.021 0.984 -987.874 1007.423
expression -40.4386 37.888 -1.067 0.299 -119.738 38.861
expression:C(dose)[T.1] 4.4530 48.357 0.092 0.928 -96.760 105.666
Omnibus: 0.561 Durbin-Watson: 1.836
Prob(Omnibus): 0.755 Jarque-Bera (JB): 0.604
Skew: 0.016 Prob(JB): 0.739
Kurtosis: 2.206 Cond. No. 1.54e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.691
Model: OLS Adj. R-squared: 0.660
Method: Least Squares F-statistic: 22.34
Date: Thu, 21 Nov 2024 Prob (F-statistic): 7.99e-06
Time: 04:07:31 Log-Likelihood: -99.607
No. Observations: 23 AIC: 205.2
Df Residuals: 20 BIC: 208.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 425.6820 226.199 1.882 0.074 -46.162 897.526
C(dose)[T.1] 53.6606 8.234 6.517 0.000 36.484 70.837
expression -37.7051 22.952 -1.643 0.116 -85.583 10.173
Omnibus: 0.564 Durbin-Watson: 1.850
Prob(Omnibus): 0.754 Jarque-Bera (JB): 0.607
Skew: 0.042 Prob(JB): 0.738
Kurtosis: 2.208 Cond. No. 548.

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: 04:07:31 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: 0.034
Model: OLS Adj. R-squared: -0.012
Method: Least Squares F-statistic: 0.7437
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.398
Time: 04:07:31 Log-Likelihood: -112.70
No. Observations: 23 AIC: 229.4
Df Residuals: 21 BIC: 231.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 416.0908 390.119 1.067 0.298 -395.206 1227.387
expression -34.1282 39.575 -0.862 0.398 -116.428 48.172
Omnibus: 2.743 Durbin-Watson: 2.589
Prob(Omnibus): 0.254 Jarque-Bera (JB): 1.441
Skew: 0.283 Prob(JB): 0.486
Kurtosis: 1.912 Cond. No. 548.

CP101

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

F-statistic p-value df difference
5.569 0.036 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: 0.578
Method: Least Squares F-statistic: 7.395
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00551
Time: 04:07:31 Log-Likelihood: -67.018
No. Observations: 15 AIC: 142.0
Df Residuals: 11 BIC: 144.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 421.9496 275.010 1.534 0.153 -183.344 1027.243
C(dose)[T.1] 629.8032 480.683 1.310 0.217 -428.173 1687.779
expression -36.3556 28.186 -1.290 0.224 -98.392 25.681
expression:C(dose)[T.1] -60.7448 49.693 -1.222 0.247 -170.119 48.629
Omnibus: 0.580 Durbin-Watson: 1.545
Prob(Omnibus): 0.748 Jarque-Bera (JB): 0.442
Skew: -0.368 Prob(JB): 0.802
Kurtosis: 2.592 Cond. No. 912.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.624
Model: OLS Adj. R-squared: 0.561
Method: Least Squares F-statistic: 9.936
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00285
Time: 04:07:31 Log-Likelihood: -67.974
No. Observations: 15 AIC: 141.9
Df Residuals: 12 BIC: 144.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 612.5149 231.174 2.650 0.021 108.829 1116.200
C(dose)[T.1] 42.4347 13.320 3.186 0.008 13.413 71.456
expression -55.8977 23.687 -2.360 0.036 -107.506 -4.289
Omnibus: 1.348 Durbin-Watson: 1.606
Prob(Omnibus): 0.510 Jarque-Bera (JB): 0.774
Skew: -0.544 Prob(JB): 0.679
Kurtosis: 2.770 Cond. No. 349.

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: 04:07:31 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: 0.305
Model: OLS Adj. R-squared: 0.252
Method: Least Squares F-statistic: 5.707
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0328
Time: 04:07:31 Log-Likelihood: -72.571
No. Observations: 15 AIC: 149.1
Df Residuals: 13 BIC: 150.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 792.3929 292.613 2.708 0.018 160.241 1424.545
expression -72.1305 30.194 -2.389 0.033 -137.361 -6.900
Omnibus: 0.537 Durbin-Watson: 1.793
Prob(Omnibus): 0.765 Jarque-Bera (JB): 0.554
Skew: 0.003 Prob(JB): 0.758
Kurtosis: 2.058 Cond. No. 338.