LID SPN TRAP

1423100_at

Fos FBJ osteosarcoma oncogene

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
0.049 0.828 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.712
Model: OLS Adj. R-squared: 0.667
Method: Least Squares F-statistic: 15.66
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.27e-05
Time: 04:08:21 Log-Likelihood: -98.789
No. Observations: 23 AIC: 205.6
Df Residuals: 19 BIC: 210.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 93.1346 45.032 2.068 0.053 -1.119 187.388
C(dose)[T.1] -134.9617 92.919 -1.452 0.163 -329.443 59.520
expression -5.3866 6.183 -0.871 0.394 -18.327 7.554
expression:C(dose)[T.1] 23.6321 11.673 2.024 0.057 -0.801 48.065
Omnibus: 1.390 Durbin-Watson: 2.159
Prob(Omnibus): 0.499 Jarque-Bera (JB): 1.016
Skew: -0.234 Prob(JB): 0.602
Kurtosis: 2.083 Cond. No. 216.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.56
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.77e-05
Time: 04:08:21 Log-Likelihood: -101.03
No. Observations: 23 AIC: 208.1
Df Residuals: 20 BIC: 211.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 45.2313 41.175 1.099 0.285 -40.658 131.121
C(dose)[T.1] 52.1441 10.297 5.064 0.000 30.666 73.622
expression 1.2422 5.636 0.220 0.828 -10.514 12.998
Omnibus: 0.399 Durbin-Watson: 1.865
Prob(Omnibus): 0.819 Jarque-Bera (JB): 0.530
Skew: 0.077 Prob(JB): 0.767
Kurtosis: 2.272 Cond. No. 74.7

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: 04:08:21 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: 0.201
Model: OLS Adj. R-squared: 0.163
Method: Least Squares F-statistic: 5.282
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0319
Time: 04:08:21 Log-Likelihood: -110.52
No. Observations: 23 AIC: 225.0
Df Residuals: 21 BIC: 227.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -45.1391 54.708 -0.825 0.419 -158.911 68.633
expression 16.2451 7.068 2.298 0.032 1.545 30.945
Omnibus: 3.967 Durbin-Watson: 2.277
Prob(Omnibus): 0.138 Jarque-Bera (JB): 1.580
Skew: -0.211 Prob(JB): 0.454
Kurtosis: 1.787 Cond. No. 66.7

CP101

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

F-statistic p-value df difference
0.005 0.946 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.481
Model: OLS Adj. R-squared: 0.339
Method: Least Squares F-statistic: 3.398
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0573
Time: 04:08:22 Log-Likelihood: -70.382
No. Observations: 15 AIC: 148.8
Df Residuals: 11 BIC: 151.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 199.9149 177.979 1.123 0.285 -191.813 591.643
C(dose)[T.1] -119.3619 205.309 -0.581 0.573 -571.245 332.521
expression -19.3835 25.983 -0.746 0.471 -76.573 37.806
expression:C(dose)[T.1] 24.7284 30.042 0.823 0.428 -41.394 90.850
Omnibus: 2.366 Durbin-Watson: 0.830
Prob(Omnibus): 0.306 Jarque-Bera (JB): 1.376
Skew: -0.738 Prob(JB): 0.503
Kurtosis: 2.858 Cond. No. 266.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 4.889
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0280
Time: 04:08:22 Log-Likelihood: -70.830
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 73.4790 88.684 0.829 0.424 -119.747 266.706
C(dose)[T.1] 49.1202 15.775 3.114 0.009 14.748 83.492
expression -0.8852 12.866 -0.069 0.946 -28.917 27.147
Omnibus: 2.630 Durbin-Watson: 0.799
Prob(Omnibus): 0.268 Jarque-Bera (JB): 1.839
Skew: -0.832 Prob(JB): 0.399
Kurtosis: 2.584 Cond. No. 78.9

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: 04:08:22 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: 0.004
Model: OLS Adj. R-squared: -0.073
Method: Least Squares F-statistic: 0.04974
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.827
Time: 04:08:22 Log-Likelihood: -75.271
No. Observations: 15 AIC: 154.5
Df Residuals: 13 BIC: 156.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 118.7703 113.015 1.051 0.312 -125.383 362.924
expression -3.6976 16.579 -0.223 0.827 -39.515 32.120
Omnibus: 0.892 Durbin-Watson: 1.569
Prob(Omnibus): 0.640 Jarque-Bera (JB): 0.688
Skew: 0.105 Prob(JB): 0.709
Kurtosis: 1.972 Cond. No. 77.6