LID SPN TRAP

1459843_s_at

Smad1 MAD homolog 1 (Drosophila)

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
0.593 0.450 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.660
Model: OLS Adj. R-squared: 0.607
Method: Least Squares F-statistic: 12.31
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000106
Time: 03:42:30 Log-Likelihood: -100.69
No. Observations: 23 AIC: 209.4
Df Residuals: 19 BIC: 213.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 183.8535 170.577 1.078 0.295 -173.167 540.874
C(dose)[T.1] -20.6316 303.086 -0.068 0.946 -654.998 613.735
expression -13.2126 17.373 -0.761 0.456 -49.575 23.149
expression:C(dose)[T.1] 7.6480 30.469 0.251 0.805 -56.125 71.421
Omnibus: 1.263 Durbin-Watson: 1.991
Prob(Omnibus): 0.532 Jarque-Bera (JB): 0.870
Skew: -0.059 Prob(JB): 0.647
Kurtosis: 2.055 Cond. No. 828.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.659
Model: OLS Adj. R-squared: 0.625
Method: Least Squares F-statistic: 19.34
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.12e-05
Time: 03:42:30 Log-Likelihood: -100.73
No. Observations: 23 AIC: 207.5
Df Residuals: 20 BIC: 210.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 159.4565 136.853 1.165 0.258 -126.013 444.926
C(dose)[T.1] 55.4097 9.052 6.121 0.000 36.527 74.293
expression -10.7262 13.934 -0.770 0.450 -39.792 18.339
Omnibus: 1.121 Durbin-Watson: 1.966
Prob(Omnibus): 0.571 Jarque-Bera (JB): 0.819
Skew: -0.029 Prob(JB): 0.664
Kurtosis: 2.077 Cond. No. 318.

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: 03:42:30 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: 0.021
Model: OLS Adj. R-squared: -0.026
Method: Least Squares F-statistic: 0.4426
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.513
Time: 03:42:30 Log-Likelihood: -112.86
No. Observations: 23 AIC: 229.7
Df Residuals: 21 BIC: 232.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -65.2939 218.086 -0.299 0.768 -518.829 388.242
expression 14.6408 22.007 0.665 0.513 -31.125 60.406
Omnibus: 3.962 Durbin-Watson: 2.425
Prob(Omnibus): 0.138 Jarque-Bera (JB): 1.675
Skew: 0.278 Prob(JB): 0.433
Kurtosis: 1.801 Cond. No. 306.

CP101

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

F-statistic p-value df difference
0.013 0.913 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.603
Model: OLS Adj. R-squared: 0.495
Method: Least Squares F-statistic: 5.581
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0142
Time: 03:42:30 Log-Likelihood: -68.362
No. Observations: 15 AIC: 144.7
Df Residuals: 11 BIC: 147.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 457.8175 290.369 1.577 0.143 -181.281 1096.916
C(dose)[T.1] -950.3814 483.633 -1.965 0.075 -2014.850 114.087
expression -43.4612 32.306 -1.345 0.206 -114.567 27.645
expression:C(dose)[T.1] 111.0201 53.687 2.068 0.063 -7.144 229.185
Omnibus: 1.564 Durbin-Watson: 0.583
Prob(Omnibus): 0.457 Jarque-Bera (JB): 0.210
Skew: -0.076 Prob(JB): 0.900
Kurtosis: 3.559 Cond. No. 794.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.358
Method: Least Squares F-statistic: 4.896
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0279
Time: 03:42:30 Log-Likelihood: -70.825
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 96.7126 261.755 0.369 0.718 -473.601 667.027
C(dose)[T.1] 49.3096 15.764 3.128 0.009 14.963 83.656
expression -3.2601 29.113 -0.112 0.913 -66.691 60.171
Omnibus: 2.642 Durbin-Watson: 0.795
Prob(Omnibus): 0.267 Jarque-Bera (JB): 1.817
Skew: -0.831 Prob(JB): 0.403
Kurtosis: 2.618 Cond. No. 305.

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: 03:42:30 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: 0.000
Model: OLS Adj. R-squared: -0.077
Method: Least Squares F-statistic: 0.004689
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.946
Time: 03:42:30 Log-Likelihood: -75.297
No. Observations: 15 AIC: 154.6
Df Residuals: 13 BIC: 156.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 70.4858 338.667 0.208 0.838 -661.161 802.132
expression 2.5754 37.609 0.068 0.946 -78.673 83.824
Omnibus: 0.428 Durbin-Watson: 1.633
Prob(Omnibus): 0.807 Jarque-Bera (JB): 0.510
Skew: 0.010 Prob(JB): 0.775
Kurtosis: 2.097 Cond. No. 304.