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 |
| 0.027 | 0.870 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.654 |
| Model: | OLS | Adj. R-squared: | 0.600 |
| Method: | Least Squares | F-statistic: | 11.99 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000124 |
| Time: | 05:12:52 | Log-Likelihood: | -100.89 |
| No. Observations: | 23 | AIC: | 209.8 |
| Df Residuals: | 19 | BIC: | 214.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 31.8366 | 130.632 | 0.244 | 0.810 | -241.580 305.253 |
| C(dose)[T.1] | 164.5320 | 218.616 | 0.753 | 0.461 | -293.036 622.100 |
| expression | 3.3233 | 19.384 | 0.171 | 0.866 | -37.247 43.894 |
| expression:C(dose)[T.1] | -16.6919 | 32.724 | -0.510 | 0.616 | -85.184 51.800 |
| Omnibus: | 0.565 | Durbin-Watson: | 1.878 |
| Prob(Omnibus): | 0.754 | Jarque-Bera (JB): | 0.610 |
| Skew: | 0.054 | Prob(JB): | 0.737 |
| Kurtosis: | 2.210 | Cond. No. | 408. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.53 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.80e-05 |
| Time: | 05:12:52 | Log-Likelihood: | -101.05 |
| 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 | 71.2620 | 103.346 | 0.690 | 0.498 | -144.314 286.838 |
| C(dose)[T.1] | 53.1153 | 8.866 | 5.991 | 0.000 | 34.621 71.609 |
| expression | -2.5333 | 15.326 | -0.165 | 0.870 | -34.502 29.435 |
| Omnibus: | 0.303 | Durbin-Watson: | 1.896 |
| Prob(Omnibus): | 0.859 | Jarque-Bera (JB): | 0.475 |
| Skew: | 0.083 | Prob(JB): | 0.789 |
| Kurtosis: | 2.316 | Cond. No. | 162. |
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: | 05:12:52 | 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: | 0.021 |
| Model: | OLS | Adj. R-squared: | -0.026 |
| Method: | Least Squares | F-statistic: | 0.4419 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.513 |
| Time: | 05:12:52 | Log-Likelihood: | -112.87 |
| No. Observations: | 23 | AIC: | 229.7 |
| Df Residuals: | 21 | BIC: | 232.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 189.6208 | 165.490 | 1.146 | 0.265 | -154.534 533.775 |
| expression | -16.4284 | 24.714 | -0.665 | 0.513 | -67.825 34.968 |
| Omnibus: | 3.602 | Durbin-Watson: | 2.386 |
| Prob(Omnibus): | 0.165 | Jarque-Bera (JB): | 1.597 |
| Skew: | 0.270 | Prob(JB): | 0.450 |
| Kurtosis: | 1.828 | Cond. No. | 159. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.899 | 0.362 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.496 |
| Model: | OLS | Adj. R-squared: | 0.359 |
| Method: | Least Squares | F-statistic: | 3.614 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0491 |
| Time: | 05:12:52 | Log-Likelihood: | -70.155 |
| No. Observations: | 15 | AIC: | 148.3 |
| Df Residuals: | 11 | BIC: | 151.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -130.2585 | 216.883 | -0.601 | 0.560 | -607.616 347.099 |
| C(dose)[T.1] | 174.0939 | 269.679 | 0.646 | 0.532 | -419.465 767.653 |
| expression | 25.8929 | 28.367 | 0.913 | 0.381 | -36.543 88.329 |
| expression:C(dose)[T.1] | -16.0271 | 35.700 | -0.449 | 0.662 | -94.603 62.549 |
| Omnibus: | 1.492 | Durbin-Watson: | 0.995 |
| Prob(Omnibus): | 0.474 | Jarque-Bera (JB): | 1.214 |
| Skew: | -0.594 | Prob(JB): | 0.545 |
| Kurtosis: | 2.273 | Cond. No. | 371. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.487 |
| Model: | OLS | Adj. R-squared: | 0.402 |
| Method: | Least Squares | F-statistic: | 5.700 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0182 |
| Time: | 05:12:52 | Log-Likelihood: | -70.291 |
| No. Observations: | 15 | AIC: | 146.6 |
| Df Residuals: | 12 | BIC: | 148.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -52.9999 | 127.526 | -0.416 | 0.685 | -330.855 224.855 |
| C(dose)[T.1] | 53.2484 | 15.772 | 3.376 | 0.006 | 18.885 87.612 |
| expression | 15.7737 | 16.640 | 0.948 | 0.362 | -20.482 52.029 |
| Omnibus: | 1.655 | Durbin-Watson: | 0.961 |
| Prob(Omnibus): | 0.437 | Jarque-Bera (JB): | 1.310 |
| Skew: | -0.584 | Prob(JB): | 0.519 |
| Kurtosis: | 2.145 | Cond. No. | 129. |
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: | 05:12:52 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.077 |
| Method: | Least Squares | F-statistic: | 0.0006496 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.980 |
| Time: | 05:12:52 | Log-Likelihood: | -75.300 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 89.5602 | 161.438 | 0.555 | 0.588 | -259.205 438.326 |
| expression | 0.5477 | 21.489 | 0.025 | 0.980 | -45.876 46.971 |
| Omnibus: | 0.639 | Durbin-Watson: | 1.625 |
| Prob(Omnibus): | 0.726 | Jarque-Bera (JB): | 0.595 |
| Skew: | 0.052 | Prob(JB): | 0.743 |
| Kurtosis: | 2.030 | Cond. No. | 121. |