LID SPN TRAP

1422134_at

Fosb FBJ osteosarcoma oncogene B

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
0.572 0.458 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.663
Model: OLS Adj. R-squared: 0.609
Method: Least Squares F-statistic: 12.43
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.92e-05
Time: 04:53:20 Log-Likelihood: -100.61
No. Observations: 23 AIC: 209.2
Df Residuals: 19 BIC: 213.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 136.8219 280.484 0.488 0.631 -450.238 723.882
C(dose)[T.1] 274.1243 464.242 0.590 0.562 -697.545 1245.794
expression -7.4233 25.197 -0.295 0.771 -60.162 45.315
expression:C(dose)[T.1] -18.5779 40.493 -0.459 0.652 -103.330 66.175
Omnibus: 0.862 Durbin-Watson: 1.856
Prob(Omnibus): 0.650 Jarque-Bera (JB): 0.733
Skew: 0.062 Prob(JB): 0.693
Kurtosis: 2.134 Cond. No. 1.50e+03

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: 19.31
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.14e-05
Time: 04:53:20 Log-Likelihood: -100.74
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 216.8787 215.220 1.008 0.326 -232.063 665.820
C(dose)[T.1] 61.2273 13.552 4.518 0.000 32.958 89.497
expression -14.6169 19.331 -0.756 0.458 -54.941 25.708
Omnibus: 1.318 Durbin-Watson: 1.959
Prob(Omnibus): 0.517 Jarque-Bera (JB): 0.879
Skew: -0.011 Prob(JB): 0.644
Kurtosis: 2.042 Cond. No. 573.

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: 04:53:20 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: 0.311
Model: OLS Adj. R-squared: 0.278
Method: Least Squares F-statistic: 9.461
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00573
Time: 04:53:20 Log-Likelihood: -108.83
No. Observations: 23 AIC: 221.7
Df Residuals: 21 BIC: 223.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -519.5988 194.932 -2.666 0.014 -924.982 -114.216
expression 52.6312 17.111 3.076 0.006 17.048 88.215
Omnibus: 1.544 Durbin-Watson: 2.158
Prob(Omnibus): 0.462 Jarque-Bera (JB): 1.109
Skew: 0.527 Prob(JB): 0.574
Kurtosis: 2.782 Cond. No. 373.

CP101

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

F-statistic p-value df difference
0.030 0.864 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.458
Model: OLS Adj. R-squared: 0.310
Method: Least Squares F-statistic: 3.094
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0716
Time: 04:53:20 Log-Likelihood: -70.711
No. Observations: 15 AIC: 149.4
Df Residuals: 11 BIC: 152.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 174.0116 291.210 0.598 0.562 -466.938 814.961
C(dose)[T.1] -141.9017 491.047 -0.289 0.778 -1222.689 938.886
expression -11.6157 31.710 -0.366 0.721 -81.410 58.178
expression:C(dose)[T.1] 20.8298 53.499 0.389 0.704 -96.921 138.581
Omnibus: 2.193 Durbin-Watson: 0.823
Prob(Omnibus): 0.334 Jarque-Bera (JB): 1.594
Skew: -0.758 Prob(JB): 0.451
Kurtosis: 2.497 Cond. No. 699.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.450
Model: OLS Adj. R-squared: 0.359
Method: Least Squares F-statistic: 4.912
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0276
Time: 04:53:20 Log-Likelihood: -70.814
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 106.8631 226.201 0.472 0.645 -385.987 599.713
C(dose)[T.1] 49.1816 15.720 3.129 0.009 14.931 83.432
expression -4.2977 24.620 -0.175 0.864 -57.941 49.345
Omnibus: 3.109 Durbin-Watson: 0.763
Prob(Omnibus): 0.211 Jarque-Bera (JB): 2.068
Skew: -0.898 Prob(JB): 0.355
Kurtosis: 2.713 Cond. No. 268.

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: 04:53:20 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: 0.002
Model: OLS Adj. R-squared: -0.075
Method: Least Squares F-statistic: 0.02187
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.885
Time: 04:53:20 Log-Likelihood: -75.287
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 136.9047 292.579 0.468 0.648 -495.173 768.983
expression -4.7131 31.873 -0.148 0.885 -73.571 64.145
Omnibus: 0.679 Durbin-Watson: 1.631
Prob(Omnibus): 0.712 Jarque-Bera (JB): 0.607
Skew: 0.037 Prob(JB): 0.738
Kurtosis: 2.017 Cond. No. 268.