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 |
| 0.987 | 0.332 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.693 |
| Model: | OLS | Adj. R-squared: | 0.644 |
| Method: | Least Squares | F-statistic: | 14.28 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 4.15e-05 |
| Time: | 11:41:20 | Log-Likelihood: | -99.533 |
| No. Observations: | 23 | AIC: | 207.1 |
| Df Residuals: | 19 | BIC: | 211.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 60.1689 | 47.773 | 1.259 | 0.223 | -39.821 160.159 |
| C(dose)[T.1] | -35.5073 | 69.831 | -0.508 | 0.617 | -181.666 110.651 |
| expression | -1.1545 | 9.184 | -0.126 | 0.901 | -20.377 18.068 |
| expression:C(dose)[T.1] | 17.6951 | 13.645 | 1.297 | 0.210 | -10.864 46.254 |
| Omnibus: | 1.273 | Durbin-Watson: | 2.150 |
| Prob(Omnibus): | 0.529 | Jarque-Bera (JB): | 1.173 |
| Skew: | 0.454 | Prob(JB): | 0.556 |
| Kurtosis: | 2.367 | Cond. No. | 112. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.666 |
| Model: | OLS | Adj. R-squared: | 0.632 |
| Method: | Least Squares | F-statistic: | 19.90 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 1.75e-05 |
| Time: | 11:41:20 | Log-Likelihood: | -100.51 |
| No. Observations: | 23 | AIC: | 207.0 |
| Df Residuals: | 20 | BIC: | 210.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 18.7815 | 36.150 | 0.520 | 0.609 | -56.626 94.189 |
| C(dose)[T.1] | 54.3808 | 8.625 | 6.305 | 0.000 | 36.389 72.373 |
| expression | 6.8616 | 6.907 | 0.993 | 0.332 | -7.546 21.270 |
| Omnibus: | 1.580 | Durbin-Watson: | 1.970 |
| Prob(Omnibus): | 0.454 | Jarque-Bera (JB): | 1.134 |
| Skew: | 0.286 | Prob(JB): | 0.567 |
| Kurtosis: | 2.075 | Cond. No. | 45.2 |
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: | Tue, 03 Dec 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 11:41:20 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.01813 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.894 |
| Time: | 11:41:20 | Log-Likelihood: | -113.09 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 71.7905 | 59.304 | 1.211 | 0.240 | -51.540 195.121 |
| expression | 1.5573 | 11.564 | 0.135 | 0.894 | -22.491 25.606 |
| Omnibus: | 3.269 | Durbin-Watson: | 2.520 |
| Prob(Omnibus): | 0.195 | Jarque-Bera (JB): | 1.547 |
| Skew: | 0.279 | Prob(JB): | 0.461 |
| Kurtosis: | 1.859 | Cond. No. | 43.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.115 | 0.312 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.689 |
| Model: | OLS | Adj. R-squared: | 0.604 |
| Method: | Least Squares | F-statistic: | 8.124 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.00393 |
| Time: | 11:41:20 | Log-Likelihood: | -66.540 |
| No. Observations: | 15 | AIC: | 141.1 |
| Df Residuals: | 11 | BIC: | 143.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 131.3978 | 77.358 | 1.699 | 0.117 | -38.867 301.662 |
| C(dose)[T.1] | -224.8740 | 108.217 | -2.078 | 0.062 | -463.058 13.310 |
| expression | -11.1066 | 13.340 | -0.833 | 0.423 | -40.467 18.254 |
| expression:C(dose)[T.1] | 51.3448 | 19.631 | 2.615 | 0.024 | 8.137 94.553 |
| Omnibus: | 4.469 | Durbin-Watson: | 1.596 |
| Prob(Omnibus): | 0.107 | Jarque-Bera (JB): | 2.840 |
| Skew: | -1.066 | Prob(JB): | 0.242 |
| Kurtosis: | 2.959 | Cond. No. | 131. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.496 |
| Model: | OLS | Adj. R-squared: | 0.412 |
| Method: | Least Squares | F-statistic: | 5.896 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0165 |
| Time: | 11:41:20 | Log-Likelihood: | -70.167 |
| No. Observations: | 15 | AIC: | 146.3 |
| Df Residuals: | 12 | BIC: | 148.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -5.1494 | 69.604 | -0.074 | 0.942 | -156.803 146.505 |
| C(dose)[T.1] | 55.9773 | 16.368 | 3.420 | 0.005 | 20.315 91.640 |
| expression | 12.6013 | 11.933 | 1.056 | 0.312 | -13.399 38.602 |
| Omnibus: | 1.664 | Durbin-Watson: | 0.909 |
| Prob(Omnibus): | 0.435 | Jarque-Bera (JB): | 1.054 |
| Skew: | -0.632 | Prob(JB): | 0.590 |
| Kurtosis: | 2.699 | Cond. No. | 53.2 |
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: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 11:41:20 | 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: | 0.004 |
| Model: | OLS | Adj. R-squared: | -0.073 |
| Method: | Least Squares | F-statistic: | 0.05293 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.822 |
| Time: | 11:41:20 | Log-Likelihood: | -75.270 |
| 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 | 112.3256 | 81.732 | 1.374 | 0.193 | -64.245 288.897 |
| expression | -3.4095 | 14.819 | -0.230 | 0.822 | -35.425 28.606 |
| Omnibus: | 0.762 | Durbin-Watson: | 1.581 |
| Prob(Omnibus): | 0.683 | Jarque-Bera (JB): | 0.645 |
| Skew: | 0.098 | Prob(JB): | 0.724 |
| Kurtosis: | 2.004 | Cond. No. | 45.9 |