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 |
| 0.160 | 0.693 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.670 |
| Model: | OLS | Adj. R-squared: | 0.618 |
| Method: | Least Squares | F-statistic: | 12.89 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 7.96e-05 |
| Time: | 21:35:31 | Log-Likelihood: | -100.34 |
| No. Observations: | 23 | AIC: | 208.7 |
| Df Residuals: | 19 | BIC: | 213.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 114.7552 | 60.820 | 1.887 | 0.075 | -12.543 242.053 |
| C(dose)[T.1] | -37.7198 | 87.867 | -0.429 | 0.673 | -221.628 146.188 |
| expression | -10.8946 | 10.890 | -1.000 | 0.330 | -33.687 11.898 |
| expression:C(dose)[T.1] | 16.5266 | 15.946 | 1.036 | 0.313 | -16.849 49.902 |
| Omnibus: | 0.115 | Durbin-Watson: | 2.264 |
| Prob(Omnibus): | 0.944 | Jarque-Bera (JB): | 0.339 |
| Skew: | 0.018 | Prob(JB): | 0.844 |
| Kurtosis: | 2.406 | Cond. No. | 147. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.652 |
| Model: | OLS | Adj. R-squared: | 0.617 |
| Method: | Least Squares | F-statistic: | 18.72 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 2.62e-05 |
| Time: | 21:35:31 | Log-Likelihood: | -100.97 |
| 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 | 71.9190 | 44.701 | 1.609 | 0.123 | -21.326 165.164 |
| C(dose)[T.1] | 52.8903 | 8.806 | 6.006 | 0.000 | 34.521 71.260 |
| expression | -3.1868 | 7.970 | -0.400 | 0.693 | -19.811 13.438 |
| Omnibus: | 0.644 | Durbin-Watson: | 1.978 |
| Prob(Omnibus): | 0.725 | Jarque-Bera (JB): | 0.640 |
| Skew: | 0.003 | Prob(JB): | 0.726 |
| Kurtosis: | 2.183 | Cond. No. | 58.6 |
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: | 21:35:31 | 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: | 0.024 |
| Model: | OLS | Adj. R-squared: | -0.023 |
| Method: | Least Squares | F-statistic: | 0.5139 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.481 |
| Time: | 21:35:31 | Log-Likelihood: | -112.83 |
| No. Observations: | 23 | AIC: | 229.7 |
| Df Residuals: | 21 | BIC: | 231.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 130.5586 | 71.281 | 1.832 | 0.081 | -17.678 278.795 |
| expression | -9.2599 | 12.918 | -0.717 | 0.481 | -36.124 17.604 |
| Omnibus: | 2.935 | Durbin-Watson: | 2.590 |
| Prob(Omnibus): | 0.230 | Jarque-Bera (JB): | 1.625 |
| Skew: | 0.366 | Prob(JB): | 0.444 |
| Kurtosis: | 1.922 | Cond. No. | 57.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.158 | 0.698 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.560 |
| Model: | OLS | Adj. R-squared: | 0.440 |
| Method: | Least Squares | F-statistic: | 4.668 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0244 |
| Time: | 21:35:31 | Log-Likelihood: | -69.142 |
| No. Observations: | 15 | AIC: | 146.3 |
| Df Residuals: | 11 | BIC: | 149.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 112.0762 | 73.030 | 1.535 | 0.153 | -48.662 272.815 |
| C(dose)[T.1] | -152.0697 | 125.113 | -1.215 | 0.250 | -427.441 123.302 |
| expression | -8.0509 | 13.026 | -0.618 | 0.549 | -36.721 20.619 |
| expression:C(dose)[T.1] | 35.3988 | 21.938 | 1.614 | 0.135 | -12.886 83.683 |
| Omnibus: | 0.852 | Durbin-Watson: | 0.724 |
| Prob(Omnibus): | 0.653 | Jarque-Bera (JB): | 0.799 |
| Skew: | -0.425 | Prob(JB): | 0.671 |
| Kurtosis: | 2.254 | Cond. No. | 125. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.456 |
| Model: | OLS | Adj. R-squared: | 0.365 |
| Method: | Least Squares | F-statistic: | 5.028 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0259 |
| Time: | 21:35:31 | Log-Likelihood: | -70.735 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 42.8634 | 62.932 | 0.681 | 0.509 | -94.254 179.981 |
| C(dose)[T.1] | 48.3936 | 15.768 | 3.069 | 0.010 | 14.039 82.748 |
| expression | 4.4296 | 11.160 | 0.397 | 0.698 | -19.885 28.744 |
| Omnibus: | 2.229 | Durbin-Watson: | 0.796 |
| Prob(Omnibus): | 0.328 | Jarque-Bera (JB): | 1.669 |
| Skew: | -0.764 | Prob(JB): | 0.434 |
| Kurtosis: | 2.421 | Cond. No. | 47.5 |
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: | 21:35:31 | 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: | 0.029 |
| Model: | OLS | Adj. R-squared: | -0.046 |
| Method: | Least Squares | F-statistic: | 0.3857 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.545 |
| Time: | 21:35:31 | Log-Likelihood: | -75.081 |
| No. Observations: | 15 | AIC: | 154.2 |
| Df Residuals: | 13 | BIC: | 155.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 43.8852 | 80.780 | 0.543 | 0.596 | -130.630 218.400 |
| expression | 8.8229 | 14.206 | 0.621 | 0.545 | -21.868 39.514 |
| Omnibus: | 0.484 | Durbin-Watson: | 1.532 |
| Prob(Omnibus): | 0.785 | Jarque-Bera (JB): | 0.540 |
| Skew: | -0.092 | Prob(JB): | 0.763 |
| Kurtosis: | 2.089 | Cond. No. | 47.3 |