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 |
| 1.876 | 0.186 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.690 |
| Model: | OLS | Adj. R-squared: | 0.642 |
| Method: | Least Squares | F-statistic: | 14.13 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.45e-05 |
| Time: | 05:01:00 | Log-Likelihood: | -99.619 |
| No. Observations: | 23 | AIC: | 207.2 |
| Df Residuals: | 19 | BIC: | 211.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -136.8772 | 318.506 | -0.430 | 0.672 | -803.517 529.763 |
| C(dose)[T.1] | -372.8930 | 525.529 | -0.710 | 0.487 | -1472.838 727.052 |
| expression | 18.6356 | 31.057 | 0.600 | 0.556 | -46.367 83.639 |
| expression:C(dose)[T.1] | 43.6251 | 52.360 | 0.833 | 0.415 | -65.966 153.216 |
| Omnibus: | 1.254 | Durbin-Watson: | 1.602 |
| Prob(Omnibus): | 0.534 | Jarque-Bera (JB): | 1.152 |
| Skew: | 0.426 | Prob(JB): | 0.562 |
| Kurtosis: | 2.310 | Cond. No. | 1.54e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.679 |
| Model: | OLS | Adj. R-squared: | 0.647 |
| Method: | Least Squares | F-statistic: | 21.17 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.16e-05 |
| Time: | 05:01:00 | Log-Likelihood: | -100.03 |
| No. Observations: | 23 | AIC: | 206.1 |
| Df Residuals: | 20 | BIC: | 209.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -294.2527 | 254.484 | -1.156 | 0.261 | -825.096 236.591 |
| C(dose)[T.1] | 64.8501 | 11.873 | 5.462 | 0.000 | 40.083 89.617 |
| expression | 33.9836 | 24.812 | 1.370 | 0.186 | -17.773 85.741 |
| Omnibus: | 0.989 | Durbin-Watson: | 1.509 |
| Prob(Omnibus): | 0.610 | Jarque-Bera (JB): | 0.850 |
| Skew: | 0.209 | Prob(JB): | 0.654 |
| Kurtosis: | 2.156 | Cond. No. | 620. |
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: | 05:01:00 | 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: | 0.162 |
| Method: | Least Squares | F-statistic: | 5.269 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0321 |
| Time: | 05:01:00 | Log-Likelihood: | -110.53 |
| No. Observations: | 23 | AIC: | 225.1 |
| Df Residuals: | 21 | BIC: | 227.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 705.0070 | 272.492 | 2.587 | 0.017 | 138.329 1271.685 |
| expression | -61.9604 | 26.994 | -2.295 | 0.032 | -118.097 -5.824 |
| Omnibus: | 1.570 | Durbin-Watson: | 2.393 |
| Prob(Omnibus): | 0.456 | Jarque-Bera (JB): | 0.952 |
| Skew: | 0.007 | Prob(JB): | 0.621 |
| Kurtosis: | 2.003 | Cond. No. | 430. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 5.252 | 0.041 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.620 |
| Model: | OLS | Adj. R-squared: | 0.516 |
| Method: | Least Squares | F-statistic: | 5.972 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0114 |
| Time: | 05:01:00 | Log-Likelihood: | -68.051 |
| No. Observations: | 15 | AIC: | 144.1 |
| Df Residuals: | 11 | BIC: | 146.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -1424.3595 | 1103.155 | -1.291 | 0.223 | -3852.388 1003.669 |
| C(dose)[T.1] | 407.1229 | 1276.868 | 0.319 | 0.756 | -2403.244 3217.490 |
| expression | 137.1599 | 101.424 | 1.352 | 0.203 | -86.072 360.392 |
| expression:C(dose)[T.1] | -34.4461 | 116.956 | -0.295 | 0.774 | -291.865 222.973 |
| Omnibus: | 1.228 | Durbin-Watson: | 1.015 |
| Prob(Omnibus): | 0.541 | Jarque-Bera (JB): | 0.908 |
| Skew: | -0.322 | Prob(JB): | 0.635 |
| Kurtosis: | 1.982 | Cond. No. | 3.08e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.617 |
| Model: | OLS | Adj. R-squared: | 0.553 |
| Method: | Least Squares | F-statistic: | 9.649 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00318 |
| Time: | 05:01:00 | Log-Likelihood: | -68.110 |
| No. Observations: | 15 | AIC: | 142.2 |
| Df Residuals: | 12 | BIC: | 144.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -1142.6181 | 528.090 | -2.164 | 0.051 | -2293.227 7.990 |
| C(dose)[T.1] | 31.0878 | 15.322 | 2.029 | 0.065 | -2.295 64.471 |
| expression | 111.2557 | 48.546 | 2.292 | 0.041 | 5.482 217.029 |
| Omnibus: | 0.920 | Durbin-Watson: | 0.938 |
| Prob(Omnibus): | 0.631 | Jarque-Bera (JB): | 0.825 |
| Skew: | -0.371 | Prob(JB): | 0.662 |
| Kurtosis: | 2.124 | Cond. No. | 893. |
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: | 05:01:00 | 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: | 0.485 |
| Model: | OLS | Adj. R-squared: | 0.445 |
| Method: | Least Squares | F-statistic: | 12.24 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00392 |
| Time: | 05:01:00 | Log-Likelihood: | -70.323 |
| No. Observations: | 15 | AIC: | 144.6 |
| Df Residuals: | 13 | BIC: | 146.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -1682.9480 | 507.762 | -3.314 | 0.006 | -2779.902 -585.994 |
| expression | 162.0544 | 46.311 | 3.499 | 0.004 | 62.006 262.103 |
| Omnibus: | 0.501 | Durbin-Watson: | 1.638 |
| Prob(Omnibus): | 0.778 | Jarque-Bera (JB): | 0.577 |
| Skew: | 0.240 | Prob(JB): | 0.749 |
| Kurtosis: | 2.168 | Cond. No. | 770. |