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 |
| 0.008 | 0.930 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.659 |
| Model: | OLS | Adj. R-squared: | 0.605 |
| Method: | Least Squares | F-statistic: | 12.23 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000110 |
| Time: | 04:23:43 | Log-Likelihood: | -100.74 |
| No. Observations: | 23 | AIC: | 209.5 |
| Df Residuals: | 19 | BIC: | 214.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 34.2711 | 34.018 | 1.007 | 0.326 | -36.929 105.472 |
| C(dose)[T.1] | 87.2762 | 47.153 | 1.851 | 0.080 | -11.416 185.968 |
| expression | 6.3099 | 10.590 | 0.596 | 0.558 | -15.855 28.474 |
| expression:C(dose)[T.1] | -10.6388 | 14.496 | -0.734 | 0.472 | -40.979 19.701 |
| Omnibus: | 0.232 | Durbin-Watson: | 1.789 |
| Prob(Omnibus): | 0.890 | Jarque-Bera (JB): | 0.340 |
| Skew: | 0.203 | Prob(JB): | 0.844 |
| Kurtosis: | 2.565 | Cond. No. | 49.7 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.51 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.82e-05 |
| Time: | 04:23:43 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 52.2113 | 23.384 | 2.233 | 0.037 | 3.434 100.989 |
| C(dose)[T.1] | 53.2898 | 8.784 | 6.066 | 0.000 | 34.966 71.614 |
| expression | 0.6320 | 7.147 | 0.088 | 0.930 | -14.277 15.541 |
| Omnibus: | 0.256 | Durbin-Watson: | 1.869 |
| Prob(Omnibus): | 0.880 | Jarque-Bera (JB): | 0.444 |
| Skew: | 0.053 | Prob(JB): | 0.801 |
| Kurtosis: | 2.328 | Cond. No. | 19.1 |
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: | 04:23:43 | 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: | 0.07778 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.783 |
| Time: | 04:23:43 | 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 | 69.2606 | 38.179 | 1.814 | 0.084 | -10.137 148.658 |
| expression | 3.2724 | 11.733 | 0.279 | 0.783 | -21.128 27.673 |
| Omnibus: | 3.534 | Durbin-Watson: | 2.395 |
| Prob(Omnibus): | 0.171 | Jarque-Bera (JB): | 1.656 |
| Skew: | 0.314 | Prob(JB): | 0.437 |
| Kurtosis: | 1.845 | Cond. No. | 18.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.751 | 0.403 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.543 |
| Model: | OLS | Adj. R-squared: | 0.419 |
| Method: | Least Squares | F-statistic: | 4.361 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0297 |
| Time: | 04:23:43 | Log-Likelihood: | -69.423 |
| No. Observations: | 15 | AIC: | 146.8 |
| Df Residuals: | 11 | BIC: | 149.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 126.7353 | 45.514 | 2.785 | 0.018 | 26.561 226.910 |
| C(dose)[T.1] | -63.1438 | 90.133 | -0.701 | 0.498 | -261.526 135.238 |
| expression | -19.4561 | 14.494 | -1.342 | 0.207 | -51.358 12.446 |
| expression:C(dose)[T.1] | 39.0246 | 31.936 | 1.222 | 0.247 | -31.266 109.315 |
| Omnibus: | 1.007 | Durbin-Watson: | 0.949 |
| Prob(Omnibus): | 0.604 | Jarque-Bera (JB): | 0.873 |
| Skew: | -0.493 | Prob(JB): | 0.646 |
| Kurtosis: | 2.349 | Cond. No. | 46.3 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.481 |
| Model: | OLS | Adj. R-squared: | 0.395 |
| Method: | Least Squares | F-statistic: | 5.566 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0195 |
| Time: | 04:23:43 | Log-Likelihood: | -70.378 |
| No. Observations: | 15 | AIC: | 146.8 |
| Df Residuals: | 12 | BIC: | 148.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 102.2320 | 41.689 | 2.452 | 0.030 | 11.399 193.065 |
| C(dose)[T.1] | 45.3364 | 15.906 | 2.850 | 0.015 | 10.680 79.993 |
| expression | -11.4176 | 13.178 | -0.866 | 0.403 | -40.131 17.295 |
| Omnibus: | 3.100 | Durbin-Watson: | 0.837 |
| Prob(Omnibus): | 0.212 | Jarque-Bera (JB): | 1.568 |
| Skew: | -0.788 | Prob(JB): | 0.456 |
| Kurtosis: | 3.158 | Cond. No. | 18.2 |
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: | 04:23:44 | 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: | 0.130 |
| Model: | OLS | Adj. R-squared: | 0.063 |
| Method: | Least Squares | F-statistic: | 1.943 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.187 |
| Time: | 04:23:44 | Log-Likelihood: | -74.255 |
| No. Observations: | 15 | AIC: | 152.5 |
| Df Residuals: | 13 | BIC: | 153.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 156.5848 | 46.125 | 3.395 | 0.005 | 56.938 256.232 |
| expression | -21.9386 | 15.740 | -1.394 | 0.187 | -55.943 12.065 |
| Omnibus: | 1.666 | Durbin-Watson: | 1.669 |
| Prob(Omnibus): | 0.435 | Jarque-Bera (JB): | 1.084 |
| Skew: | 0.374 | Prob(JB): | 0.582 |
| Kurtosis: | 1.915 | Cond. No. | 15.9 |