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 |
| 2.702 | 0.116 | 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: | 14.27 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 4.16e-05 |
| Time: | 21:50:27 | Log-Likelihood: | -99.537 |
| 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 | -159.9834 | 289.770 | -0.552 | 0.587 | -766.478 446.512 |
| C(dose)[T.1] | -69.6772 | 370.052 | -0.188 | 0.853 | -844.205 704.851 |
| expression | 21.4548 | 29.019 | 0.739 | 0.469 | -39.283 82.193 |
| expression:C(dose)[T.1] | 12.4271 | 37.102 | 0.335 | 0.741 | -65.228 90.082 |
| Omnibus: | 0.723 | Durbin-Watson: | 2.223 |
| Prob(Omnibus): | 0.697 | Jarque-Bera (JB): | 0.688 |
| Skew: | -0.098 | Prob(JB): | 0.709 |
| Kurtosis: | 2.176 | Cond. No. | 1.21e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.691 |
| Model: | OLS | Adj. R-squared: | 0.660 |
| Method: | Least Squares | F-statistic: | 22.34 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 7.98e-06 |
| Time: | 21:50:27 | Log-Likelihood: | -99.605 |
| No. Observations: | 23 | AIC: | 205.2 |
| Df Residuals: | 20 | BIC: | 208.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -235.8814 | 176.555 | -1.336 | 0.197 | -604.168 132.406 |
| C(dose)[T.1] | 54.2376 | 8.250 | 6.575 | 0.000 | 37.029 71.446 |
| expression | 29.0572 | 17.676 | 1.644 | 0.116 | -7.814 65.928 |
| Omnibus: | 1.006 | Durbin-Watson: | 2.200 |
| Prob(Omnibus): | 0.605 | Jarque-Bera (JB): | 0.806 |
| Skew: | -0.122 | Prob(JB): | 0.668 |
| Kurtosis: | 2.116 | Cond. No. | 433. |
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: | 21:50:27 | 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: | 0.023 |
| Model: | OLS | Adj. R-squared: | -0.024 |
| Method: | Least Squares | F-statistic: | 0.4863 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.493 |
| Time: | 21:50:27 | Log-Likelihood: | -112.84 |
| No. Observations: | 23 | AIC: | 229.7 |
| Df Residuals: | 21 | BIC: | 232.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -133.0194 | 305.144 | -0.436 | 0.667 | -767.600 501.562 |
| expression | 21.3408 | 30.602 | 0.697 | 0.493 | -42.300 84.982 |
| Omnibus: | 3.864 | Durbin-Watson: | 2.618 |
| Prob(Omnibus): | 0.145 | Jarque-Bera (JB): | 1.578 |
| Skew: | 0.223 | Prob(JB): | 0.454 |
| Kurtosis: | 1.797 | Cond. No. | 431. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.024 | 0.880 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.470 |
| Model: | OLS | Adj. R-squared: | 0.326 |
| Method: | Least Squares | F-statistic: | 3.258 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0634 |
| Time: | 21:50:27 | Log-Likelihood: | -70.532 |
| No. Observations: | 15 | AIC: | 149.1 |
| Df Residuals: | 11 | BIC: | 151.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -48.4401 | 233.987 | -0.207 | 0.840 | -563.443 466.562 |
| C(dose)[T.1] | 320.0208 | 414.731 | 0.772 | 0.457 | -592.797 1232.838 |
| expression | 12.5068 | 25.225 | 0.496 | 0.630 | -43.012 68.026 |
| expression:C(dose)[T.1] | -29.0411 | 44.383 | -0.654 | 0.526 | -126.728 68.646 |
| Omnibus: | 1.203 | Durbin-Watson: | 0.964 |
| Prob(Omnibus): | 0.548 | Jarque-Bera (JB): | 0.984 |
| Skew: | -0.547 | Prob(JB): | 0.611 |
| Kurtosis: | 2.387 | Cond. No. | 601. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.450 |
| Model: | OLS | Adj. R-squared: | 0.358 |
| Method: | Least Squares | F-statistic: | 4.906 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0277 |
| Time: | 21:50:27 | Log-Likelihood: | -70.818 |
| 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 | 38.4637 | 187.994 | 0.205 | 0.841 | -371.141 448.068 |
| C(dose)[T.1] | 48.8608 | 15.874 | 3.078 | 0.010 | 14.275 83.447 |
| expression | 3.1265 | 20.254 | 0.154 | 0.880 | -41.004 47.256 |
| Omnibus: | 2.869 | Durbin-Watson: | 0.816 |
| Prob(Omnibus): | 0.238 | Jarque-Bera (JB): | 1.966 |
| Skew: | -0.868 | Prob(JB): | 0.374 |
| Kurtosis: | 2.637 | Cond. No. | 227. |
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: | 21:50:27 | 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: | 0.015 |
| Model: | OLS | Adj. R-squared: | -0.060 |
| Method: | Least Squares | F-statistic: | 0.2047 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.658 |
| Time: | 21:50:27 | Log-Likelihood: | -75.183 |
| No. Observations: | 15 | AIC: | 154.4 |
| Df Residuals: | 13 | BIC: | 155.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -15.0754 | 240.586 | -0.063 | 0.951 | -534.830 504.679 |
| expression | 11.6655 | 25.787 | 0.452 | 0.658 | -44.043 67.374 |
| Omnibus: | 1.558 | Durbin-Watson: | 1.633 |
| Prob(Omnibus): | 0.459 | Jarque-Bera (JB): | 0.857 |
| Skew: | 0.069 | Prob(JB): | 0.651 |
| Kurtosis: | 1.837 | Cond. No. | 225. |