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 |
| 0.408 | 0.530 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.602 |
| Method: | Least Squares | F-statistic: | 12.08 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000118 |
| Time: | 04:50:37 | Log-Likelihood: | -100.83 |
| No. Observations: | 23 | AIC: | 209.7 |
| Df Residuals: | 19 | BIC: | 214.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 29.2815 | 61.889 | 0.473 | 0.642 | -100.255 158.817 |
| C(dose)[T.1] | 47.0547 | 90.448 | 0.520 | 0.609 | -142.255 236.365 |
| expression | 4.1457 | 10.242 | 0.405 | 0.690 | -17.291 25.583 |
| expression:C(dose)[T.1] | 0.6379 | 14.358 | 0.044 | 0.965 | -29.413 30.689 |
| Omnibus: | 0.138 | Durbin-Watson: | 1.981 |
| Prob(Omnibus): | 0.933 | Jarque-Bera (JB): | 0.348 |
| Skew: | -0.093 | Prob(JB): | 0.840 |
| Kurtosis: | 2.427 | Cond. No. | 171. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.622 |
| Method: | Least Squares | F-statistic: | 19.08 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.32e-05 |
| Time: | 04:50:38 | Log-Likelihood: | -100.83 |
| No. Observations: | 23 | AIC: | 207.7 |
| Df Residuals: | 20 | BIC: | 211.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 27.3296 | 42.492 | 0.643 | 0.527 | -61.308 115.967 |
| C(dose)[T.1] | 51.0507 | 9.390 | 5.437 | 0.000 | 31.463 70.638 |
| expression | 4.4704 | 6.996 | 0.639 | 0.530 | -10.124 19.064 |
| Omnibus: | 0.156 | Durbin-Watson: | 1.985 |
| Prob(Omnibus): | 0.925 | Jarque-Bera (JB): | 0.367 |
| Skew: | -0.083 | Prob(JB): | 0.832 |
| Kurtosis: | 2.404 | Cond. No. | 63.6 |
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: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 04:50:38 | 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: | 0.148 |
| Model: | OLS | Adj. R-squared: | 0.107 |
| Method: | Least Squares | F-statistic: | 3.643 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0701 |
| Time: | 04:50:38 | Log-Likelihood: | -111.27 |
| No. Observations: | 23 | AIC: | 226.5 |
| Df Residuals: | 21 | BIC: | 228.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -38.9504 | 62.531 | -0.623 | 0.540 | -168.991 91.091 |
| expression | 18.9649 | 9.937 | 1.909 | 0.070 | -1.699 39.629 |
| Omnibus: | 4.464 | Durbin-Watson: | 2.652 |
| Prob(Omnibus): | 0.107 | Jarque-Bera (JB): | 1.557 |
| Skew: | 0.098 | Prob(JB): | 0.459 |
| Kurtosis: | 1.740 | Cond. No. | 60.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.523 | 0.483 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.524 |
| Model: | OLS | Adj. R-squared: | 0.395 |
| Method: | Least Squares | F-statistic: | 4.043 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0366 |
| Time: | 04:50:38 | Log-Likelihood: | -69.726 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 11 | BIC: | 150.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 150.2350 | 66.887 | 2.246 | 0.046 | 3.018 297.452 |
| C(dose)[T.1] | -68.1086 | 107.139 | -0.636 | 0.538 | -303.919 167.702 |
| expression | -14.7194 | 11.723 | -1.256 | 0.235 | -40.522 11.083 |
| expression:C(dose)[T.1] | 20.9069 | 18.954 | 1.103 | 0.294 | -20.811 62.625 |
| Omnibus: | 3.019 | Durbin-Watson: | 1.155 |
| Prob(Omnibus): | 0.221 | Jarque-Bera (JB): | 1.644 |
| Skew: | -0.811 | Prob(JB): | 0.440 |
| Kurtosis: | 3.037 | Cond. No. | 104. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.472 |
| Model: | OLS | Adj. R-squared: | 0.384 |
| Method: | Least Squares | F-statistic: | 5.359 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0217 |
| Time: | 04:50:38 | Log-Likelihood: | -70.513 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 12 | BIC: | 149.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 105.2425 | 53.486 | 1.968 | 0.073 | -11.293 221.778 |
| C(dose)[T.1] | 48.8595 | 15.415 | 3.170 | 0.008 | 15.274 82.445 |
| expression | -6.7217 | 9.295 | -0.723 | 0.483 | -26.973 13.530 |
| Omnibus: | 2.487 | Durbin-Watson: | 0.988 |
| Prob(Omnibus): | 0.288 | Jarque-Bera (JB): | 1.714 |
| Skew: | -0.804 | Prob(JB): | 0.424 |
| Kurtosis: | 2.608 | Cond. No. | 40.8 |
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: | 04:50:38 | 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: | 0.030 |
| Model: | OLS | Adj. R-squared: | -0.045 |
| Method: | Least Squares | F-statistic: | 0.3958 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.540 |
| Time: | 04:50:38 | Log-Likelihood: | -75.075 |
| 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 | 136.2863 | 68.476 | 1.990 | 0.068 | -11.646 284.219 |
| expression | -7.6121 | 12.099 | -0.629 | 0.540 | -33.750 18.526 |
| Omnibus: | 1.589 | Durbin-Watson: | 1.779 |
| Prob(Omnibus): | 0.452 | Jarque-Bera (JB): | 0.963 |
| Skew: | 0.264 | Prob(JB): | 0.618 |
| Kurtosis: | 1.877 | Cond. No. | 39.9 |