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 |
| 0.480 | 0.496 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.675 |
| Model: | OLS | Adj. R-squared: | 0.624 |
| Method: | Least Squares | F-statistic: | 13.16 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 6.97e-05 |
| Time: | 23:00:41 | Log-Likelihood: | -100.17 |
| No. Observations: | 23 | AIC: | 208.3 |
| Df Residuals: | 19 | BIC: | 212.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 50.3951 | 33.162 | 1.520 | 0.145 | -19.013 119.804 |
| C(dose)[T.1] | -11.8407 | 65.451 | -0.181 | 0.858 | -148.832 125.150 |
| expression | 0.7627 | 6.524 | 0.117 | 0.908 | -12.892 14.417 |
| expression:C(dose)[T.1] | 13.8771 | 13.571 | 1.023 | 0.319 | -14.528 42.282 |
| Omnibus: | 1.995 | Durbin-Watson: | 1.979 |
| Prob(Omnibus): | 0.369 | Jarque-Bera (JB): | 1.167 |
| Skew: | 0.208 | Prob(JB): | 0.558 |
| Kurtosis: | 1.977 | Cond. No. | 90.0 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.657 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 19.18 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.24e-05 |
| Time: | 23:00:42 | Log-Likelihood: | -100.79 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 20 | BIC: | 211.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 34.3624 | 29.254 | 1.175 | 0.254 | -26.661 95.386 |
| C(dose)[T.1] | 54.4771 | 8.821 | 6.176 | 0.000 | 36.077 72.878 |
| expression | 3.9694 | 5.727 | 0.693 | 0.496 | -7.977 15.916 |
| Omnibus: | 1.243 | Durbin-Watson: | 1.950 |
| Prob(Omnibus): | 0.537 | Jarque-Bera (JB): | 0.876 |
| Skew: | 0.099 | Prob(JB): | 0.645 |
| Kurtosis: | 2.065 | Cond. No. | 34.9 |
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, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 23:00:42 | 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: | 0.004 |
| Model: | OLS | Adj. R-squared: | -0.044 |
| Method: | Least Squares | F-statistic: | 0.07864 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.782 |
| Time: | 23:00:42 | Log-Likelihood: | -113.06 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 92.4831 | 46.089 | 2.007 | 0.058 | -3.364 188.330 |
| expression | -2.6254 | 9.362 | -0.280 | 0.782 | -22.095 16.844 |
| Omnibus: | 3.131 | Durbin-Watson: | 2.463 |
| Prob(Omnibus): | 0.209 | Jarque-Bera (JB): | 1.546 |
| Skew: | 0.296 | Prob(JB): | 0.462 |
| Kurtosis: | 1.877 | Cond. No. | 32.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.282 | 0.605 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.463 |
| Model: | OLS | Adj. R-squared: | 0.316 |
| Method: | Least Squares | F-statistic: | 3.155 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0684 |
| Time: | 23:00:42 | Log-Likelihood: | -70.643 |
| No. Observations: | 15 | AIC: | 149.3 |
| Df Residuals: | 11 | BIC: | 152.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 23.2497 | 109.394 | 0.213 | 0.836 | -217.525 264.024 |
| C(dose)[T.1] | 66.3467 | 135.511 | 0.490 | 0.634 | -231.912 364.605 |
| expression | 7.9330 | 19.528 | 0.406 | 0.692 | -35.047 50.913 |
| expression:C(dose)[T.1] | -3.4994 | 23.455 | -0.149 | 0.884 | -55.123 48.124 |
| Omnibus: | 2.549 | Durbin-Watson: | 0.853 |
| Prob(Omnibus): | 0.280 | Jarque-Bera (JB): | 1.776 |
| Skew: | -0.817 | Prob(JB): | 0.411 |
| Kurtosis: | 2.588 | Cond. No. | 148. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.461 |
| Model: | OLS | Adj. R-squared: | 0.372 |
| Method: | Least Squares | F-statistic: | 5.141 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0244 |
| Time: | 23:00:42 | Log-Likelihood: | -70.659 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 12 | BIC: | 149.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 36.7581 | 58.840 | 0.625 | 0.544 | -91.444 164.960 |
| C(dose)[T.1] | 46.2926 | 16.490 | 2.807 | 0.016 | 10.364 82.221 |
| expression | 5.5073 | 10.367 | 0.531 | 0.605 | -17.080 28.095 |
| Omnibus: | 2.548 | Durbin-Watson: | 0.836 |
| Prob(Omnibus): | 0.280 | Jarque-Bera (JB): | 1.749 |
| Skew: | -0.814 | Prob(JB): | 0.417 |
| Kurtosis: | 2.618 | Cond. No. | 46.3 |
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, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 23:00:42 | 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: | 0.108 |
| Model: | OLS | Adj. R-squared: | 0.039 |
| Method: | Least Squares | F-statistic: | 1.570 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.232 |
| Time: | 23:00:42 | Log-Likelihood: | -74.445 |
| No. Observations: | 15 | AIC: | 152.9 |
| Df Residuals: | 13 | BIC: | 154.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 5.0105 | 71.408 | 0.070 | 0.945 | -149.258 159.279 |
| expression | 15.1543 | 12.095 | 1.253 | 0.232 | -10.976 41.285 |
| Omnibus: | 0.307 | Durbin-Watson: | 1.694 |
| Prob(Omnibus): | 0.858 | Jarque-Bera (JB): | 0.460 |
| Skew: | 0.194 | Prob(JB): | 0.795 |
| Kurtosis: | 2.235 | Cond. No. | 45.2 |