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 |
| 0.212 | 0.650 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.655 |
| Model: | OLS | Adj. R-squared: | 0.600 |
| Method: | Least Squares | F-statistic: | 12.02 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.000122 |
| Time: | 21:22:29 | Log-Likelihood: | -100.87 |
| No. Observations: | 23 | AIC: | 209.7 |
| Df Residuals: | 19 | BIC: | 214.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 28.4648 | 249.226 | 0.114 | 0.910 | -493.170 550.100 |
| C(dose)[T.1] | -84.3757 | 385.052 | -0.219 | 0.829 | -890.299 721.548 |
| expression | 2.6434 | 25.584 | 0.103 | 0.919 | -50.903 56.190 |
| expression:C(dose)[T.1] | 13.1267 | 38.158 | 0.344 | 0.735 | -66.739 92.993 |
| Omnibus: | 0.047 | Durbin-Watson: | 1.882 |
| Prob(Omnibus): | 0.977 | Jarque-Bera (JB): | 0.259 |
| Skew: | 0.046 | Prob(JB): | 0.878 |
| Kurtosis: | 2.488 | Cond. No. | 1.11e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.618 |
| Method: | Least Squares | F-statistic: | 18.80 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 2.55e-05 |
| Time: | 21:22:29 | Log-Likelihood: | -100.94 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 20 | BIC: | 211.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -28.9996 | 180.836 | -0.160 | 0.874 | -406.216 348.217 |
| C(dose)[T.1] | 47.9861 | 14.533 | 3.302 | 0.004 | 17.672 78.300 |
| expression | 8.5441 | 18.558 | 0.460 | 0.650 | -30.168 47.256 |
| Omnibus: | 0.092 | Durbin-Watson: | 1.857 |
| Prob(Omnibus): | 0.955 | Jarque-Bera (JB): | 0.311 |
| Skew: | 0.060 | Prob(JB): | 0.856 |
| Kurtosis: | 2.443 | Cond. No. | 422. |
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: | Mon, 27 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 21:22:29 | 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: | 0.463 |
| Model: | OLS | Adj. R-squared: | 0.438 |
| Method: | Least Squares | F-statistic: | 18.14 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.000350 |
| Time: | 21:22:29 | Log-Likelihood: | -105.95 |
| No. Observations: | 23 | AIC: | 215.9 |
| Df Residuals: | 21 | BIC: | 218.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -498.0264 | 135.763 | -3.668 | 0.001 | -780.361 -215.692 |
| expression | 57.5546 | 13.514 | 4.259 | 0.000 | 29.450 85.659 |
| Omnibus: | 0.631 | Durbin-Watson: | 2.075 |
| Prob(Omnibus): | 0.730 | Jarque-Bera (JB): | 0.705 |
| Skew: | 0.269 | Prob(JB): | 0.703 |
| Kurtosis: | 2.333 | Cond. No. | 261. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 25.172 | 0.000 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.847 |
| Model: | OLS | Adj. R-squared: | 0.805 |
| Method: | Least Squares | F-statistic: | 20.25 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 8.73e-05 |
| Time: | 21:22:29 | Log-Likelihood: | -61.235 |
| No. Observations: | 15 | AIC: | 130.5 |
| Df Residuals: | 11 | BIC: | 133.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -299.0766 | 143.229 | -2.088 | 0.061 | -614.322 16.168 |
| C(dose)[T.1] | -203.5951 | 194.912 | -1.045 | 0.319 | -632.594 225.404 |
| expression | 42.2066 | 16.478 | 2.561 | 0.026 | 5.939 78.475 |
| expression:C(dose)[T.1] | 29.9829 | 22.550 | 1.330 | 0.211 | -19.650 79.616 |
| Omnibus: | 0.147 | Durbin-Watson: | 1.260 |
| Prob(Omnibus): | 0.929 | Jarque-Bera (JB): | 0.057 |
| Skew: | -0.063 | Prob(JB): | 0.972 |
| Kurtosis: | 2.725 | Cond. No. | 532. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.822 |
| Model: | OLS | Adj. R-squared: | 0.792 |
| Method: | Least Squares | F-statistic: | 27.72 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 3.18e-05 |
| Time: | 21:22:29 | Log-Likelihood: | -62.353 |
| No. Observations: | 15 | AIC: | 130.7 |
| Df Residuals: | 12 | BIC: | 132.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -438.0985 | 100.971 | -4.339 | 0.001 | -658.095 -218.102 |
| C(dose)[T.1] | 55.2993 | 9.025 | 6.127 | 0.000 | 35.635 74.964 |
| expression | 58.2164 | 11.603 | 5.017 | 0.000 | 32.935 83.498 |
| Omnibus: | 0.232 | Durbin-Watson: | 1.744 |
| Prob(Omnibus): | 0.891 | Jarque-Bera (JB): | 0.232 |
| Skew: | 0.223 | Prob(JB): | 0.891 |
| Kurtosis: | 2.584 | Cond. No. | 198. |
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: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 21:22:29 | 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: | 0.265 |
| Model: | OLS | Adj. R-squared: | 0.209 |
| Method: | Least Squares | F-statistic: | 4.695 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0494 |
| Time: | 21:22:29 | Log-Likelihood: | -72.988 |
| No. Observations: | 15 | AIC: | 150.0 |
| Df Residuals: | 13 | BIC: | 151.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -325.9333 | 193.845 | -1.681 | 0.117 | -744.711 92.844 |
| expression | 48.6342 | 22.445 | 2.167 | 0.049 | 0.144 97.124 |
| Omnibus: | 4.123 | Durbin-Watson: | 2.361 |
| Prob(Omnibus): | 0.127 | Jarque-Bera (JB): | 1.304 |
| Skew: | 0.104 | Prob(JB): | 0.521 |
| Kurtosis: | 1.571 | Cond. No. | 195. |