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 |
| 0.107 | 0.747 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.722 |
| Model: | OLS | Adj. R-squared: | 0.678 |
| Method: | Least Squares | F-statistic: | 16.43 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.65e-05 |
| Time: | 05:24:57 | Log-Likelihood: | -98.393 |
| No. Observations: | 23 | AIC: | 204.8 |
| Df Residuals: | 19 | BIC: | 209.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 73.6638 | 39.153 | 1.881 | 0.075 | -8.285 155.612 |
| C(dose)[T.1] | -180.9683 | 106.593 | -1.698 | 0.106 | -404.070 42.134 |
| expression | -3.6130 | 7.198 | -0.502 | 0.621 | -18.678 11.452 |
| expression:C(dose)[T.1] | 41.7933 | 19.004 | 2.199 | 0.040 | 2.017 81.569 |
| Omnibus: | 0.393 | Durbin-Watson: | 2.085 |
| Prob(Omnibus): | 0.822 | Jarque-Bera (JB): | 0.529 |
| Skew: | 0.229 | Prob(JB): | 0.768 |
| Kurtosis: | 2.416 | Cond. No. | 175. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 18.65 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.69e-05 |
| Time: | 05:24:57 | Log-Likelihood: | -101.00 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 41.3794 | 39.626 | 1.044 | 0.309 | -41.278 124.037 |
| C(dose)[T.1] | 52.7596 | 8.922 | 5.913 | 0.000 | 34.148 71.371 |
| expression | 2.3824 | 7.272 | 0.328 | 0.747 | -12.788 17.553 |
| Omnibus: | 0.204 | Durbin-Watson: | 1.945 |
| Prob(Omnibus): | 0.903 | Jarque-Bera (JB): | 0.395 |
| Skew: | 0.140 | Prob(JB): | 0.821 |
| Kurtosis: | 2.422 | Cond. No. | 52.0 |
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, 21 Nov 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 05:24:57 | 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: | 0.041 |
| Model: | OLS | Adj. R-squared: | -0.005 |
| Method: | Least Squares | F-statistic: | 0.8897 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.356 |
| Time: | 05:24:57 | Log-Likelihood: | -112.63 |
| No. Observations: | 23 | AIC: | 229.3 |
| Df Residuals: | 21 | BIC: | 231.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 19.8739 | 63.838 | 0.311 | 0.759 | -112.884 152.632 |
| expression | 10.8791 | 11.534 | 0.943 | 0.356 | -13.107 34.865 |
| Omnibus: | 3.468 | Durbin-Watson: | 2.493 |
| Prob(Omnibus): | 0.177 | Jarque-Bera (JB): | 1.440 |
| Skew: | 0.161 | Prob(JB): | 0.487 |
| Kurtosis: | 1.817 | Cond. No. | 51.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.022 | 0.884 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.477 |
| Model: | OLS | Adj. R-squared: | 0.334 |
| Method: | Least Squares | F-statistic: | 3.342 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0596 |
| Time: | 05:24:57 | Log-Likelihood: | -70.441 |
| No. Observations: | 15 | AIC: | 148.9 |
| Df Residuals: | 11 | BIC: | 151.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -2.1177 | 129.054 | -0.016 | 0.987 | -286.164 281.929 |
| C(dose)[T.1] | 247.4384 | 269.094 | 0.920 | 0.378 | -344.833 839.709 |
| expression | 11.4380 | 21.138 | 0.541 | 0.599 | -35.086 57.962 |
| expression:C(dose)[T.1] | -28.6954 | 38.042 | -0.754 | 0.467 | -112.426 55.035 |
| Omnibus: | 2.961 | Durbin-Watson: | 0.762 |
| Prob(Omnibus): | 0.228 | Jarque-Bera (JB): | 1.651 |
| Skew: | -0.813 | Prob(JB): | 0.438 |
| Kurtosis: | 2.993 | Cond. No. | 295. |
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: | 4.905 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0277 |
| Time: | 05:24:57 | Log-Likelihood: | -70.819 |
| 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 | 51.7482 | 105.547 | 0.490 | 0.633 | -178.220 281.716 |
| C(dose)[T.1] | 45.6450 | 28.495 | 1.602 | 0.135 | -16.441 107.731 |
| expression | 2.5789 | 17.256 | 0.149 | 0.884 | -35.018 40.176 |
| Omnibus: | 2.612 | Durbin-Watson: | 0.753 |
| Prob(Omnibus): | 0.271 | Jarque-Bera (JB): | 1.865 |
| Skew: | -0.832 | Prob(JB): | 0.393 |
| Kurtosis: | 2.536 | Cond. No. | 97.0 |
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, 21 Nov 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 05:24:57 | 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: | 0.332 |
| Model: | OLS | Adj. R-squared: | 0.281 |
| Method: | Least Squares | F-statistic: | 6.465 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0245 |
| Time: | 05:24:57 | Log-Likelihood: | -72.272 |
| No. Observations: | 15 | AIC: | 148.5 |
| Df Residuals: | 13 | BIC: | 150.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -80.9963 | 69.191 | -1.171 | 0.263 | -230.475 68.482 |
| expression | 25.6301 | 10.080 | 2.543 | 0.025 | 3.854 47.406 |
| Omnibus: | 3.544 | Durbin-Watson: | 0.639 |
| Prob(Omnibus): | 0.170 | Jarque-Bera (JB): | 1.389 |
| Skew: | -0.312 | Prob(JB): | 0.499 |
| Kurtosis: | 1.647 | Cond. No. | 58.4 |