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 |
| 0.156 | 0.697 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.660 |
| Model: | OLS | Adj. R-squared: | 0.607 |
| Method: | Least Squares | F-statistic: | 12.30 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000106 |
| Time: | 05:25:26 | Log-Likelihood: | -100.69 |
| No. Observations: | 23 | AIC: | 209.4 |
| Df Residuals: | 19 | BIC: | 213.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 206.1803 | 195.702 | 1.054 | 0.305 | -203.429 615.790 |
| C(dose)[T.1] | -118.8445 | 248.347 | -0.479 | 0.638 | -638.640 400.951 |
| expression | -22.5801 | 29.063 | -0.777 | 0.447 | -83.410 38.250 |
| expression:C(dose)[T.1] | 25.7268 | 37.556 | 0.685 | 0.502 | -52.878 104.332 |
| Omnibus: | 0.181 | Durbin-Watson: | 2.039 |
| Prob(Omnibus): | 0.913 | Jarque-Bera (JB): | 0.329 |
| Skew: | 0.173 | Prob(JB): | 0.848 |
| Kurtosis: | 2.528 | Cond. No. | 513. |
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: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.62e-05 |
| Time: | 05:25:26 | Log-Likelihood: | -100.97 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 20 | BIC: | 211.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 102.4837 | 122.380 | 0.837 | 0.412 | -152.796 357.763 |
| C(dose)[T.1] | 51.1285 | 10.372 | 4.929 | 0.000 | 29.492 72.765 |
| expression | -7.1728 | 18.161 | -0.395 | 0.697 | -45.056 30.711 |
| Omnibus: | 0.153 | Durbin-Watson: | 1.984 |
| Prob(Omnibus): | 0.927 | Jarque-Bera (JB): | 0.367 |
| Skew: | 0.071 | Prob(JB): | 0.832 |
| Kurtosis: | 2.398 | Cond. No. | 190. |
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: | 05:25:26 | 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: | 0.229 |
| Model: | OLS | Adj. R-squared: | 0.192 |
| Method: | Least Squares | F-statistic: | 6.227 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0210 |
| Time: | 05:25:26 | Log-Likelihood: | -110.12 |
| No. Observations: | 23 | AIC: | 224.2 |
| Df Residuals: | 21 | BIC: | 226.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 444.6616 | 146.381 | 3.038 | 0.006 | 140.245 749.078 |
| expression | -55.4367 | 22.215 | -2.495 | 0.021 | -101.636 -9.238 |
| Omnibus: | 4.177 | Durbin-Watson: | 2.557 |
| Prob(Omnibus): | 0.124 | Jarque-Bera (JB): | 2.033 |
| Skew: | 0.434 | Prob(JB): | 0.362 |
| Kurtosis: | 1.830 | Cond. No. | 156. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.071 | 0.794 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.453 |
| Model: | OLS | Adj. R-squared: | 0.304 |
| Method: | Least Squares | F-statistic: | 3.034 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0749 |
| Time: | 05:25:26 | Log-Likelihood: | -70.778 |
| No. Observations: | 15 | AIC: | 149.6 |
| Df Residuals: | 11 | BIC: | 152.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 34.6192 | 248.256 | 0.139 | 0.892 | -511.788 581.027 |
| C(dose)[T.1] | -5.2979 | 425.366 | -0.012 | 0.990 | -941.522 930.926 |
| expression | 5.4645 | 41.300 | 0.132 | 0.897 | -85.436 96.365 |
| expression:C(dose)[T.1] | 8.7926 | 69.886 | 0.126 | 0.902 | -145.025 162.610 |
| Omnibus: | 2.256 | Durbin-Watson: | 0.879 |
| Prob(Omnibus): | 0.324 | Jarque-Bera (JB): | 1.586 |
| Skew: | -0.766 | Prob(JB): | 0.452 |
| Kurtosis: | 2.566 | Cond. No. | 405. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.452 |
| Model: | OLS | Adj. R-squared: | 0.361 |
| Method: | Least Squares | F-statistic: | 4.950 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0271 |
| Time: | 05:25:26 | Log-Likelihood: | -70.788 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 16.1826 | 191.999 | 0.084 | 0.934 | -402.147 434.512 |
| C(dose)[T.1] | 48.1768 | 16.150 | 2.983 | 0.011 | 12.990 83.364 |
| expression | 8.5352 | 31.921 | 0.267 | 0.794 | -61.015 78.086 |
| Omnibus: | 2.537 | Durbin-Watson: | 0.911 |
| Prob(Omnibus): | 0.281 | Jarque-Bera (JB): | 1.759 |
| Skew: | -0.814 | Prob(JB): | 0.415 |
| Kurtosis: | 2.598 | Cond. No. | 154. |
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: | 05:25:26 | 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: | 0.046 |
| Model: | OLS | Adj. R-squared: | -0.028 |
| Method: | Least Squares | F-statistic: | 0.6222 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.444 |
| Time: | 05:25:26 | Log-Likelihood: | -74.949 |
| No. Observations: | 15 | AIC: | 153.9 |
| Df Residuals: | 13 | BIC: | 155.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -94.5638 | 238.846 | -0.396 | 0.699 | -610.558 421.431 |
| expression | 31.0214 | 39.329 | 0.789 | 0.444 | -53.944 115.986 |
| Omnibus: | 0.040 | Durbin-Watson: | 1.705 |
| Prob(Omnibus): | 0.980 | Jarque-Bera (JB): | 0.261 |
| Skew: | 0.049 | Prob(JB): | 0.878 |
| Kurtosis: | 2.362 | Cond. No. | 150. |