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 |
| 0.845 | 0.369 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.664 |
| Model: | OLS | Adj. R-squared: | 0.611 |
| Method: | Least Squares | F-statistic: | 12.52 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.53e-05 |
| Time: | 05:02:22 | Log-Likelihood: | -100.56 |
| No. Observations: | 23 | AIC: | 209.1 |
| Df Residuals: | 19 | BIC: | 213.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 27.7087 | 53.732 | 0.516 | 0.612 | -84.753 140.171 |
| C(dose)[T.1] | 39.7143 | 74.926 | 0.530 | 0.602 | -117.108 196.537 |
| expression | 4.0437 | 8.146 | 0.496 | 0.625 | -13.007 21.094 |
| expression:C(dose)[T.1] | 2.3568 | 11.607 | 0.203 | 0.841 | -21.937 26.651 |
| Omnibus: | 1.725 | Durbin-Watson: | 1.860 |
| Prob(Omnibus): | 0.422 | Jarque-Bera (JB): | 0.994 |
| Skew: | -0.010 | Prob(JB): | 0.608 |
| Kurtosis: | 1.982 | Cond. No. | 146. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.663 |
| Model: | OLS | Adj. R-squared: | 0.630 |
| Method: | Least Squares | F-statistic: | 19.70 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.87e-05 |
| Time: | 05:02:22 | Log-Likelihood: | -100.59 |
| No. Observations: | 23 | AIC: | 207.2 |
| Df Residuals: | 20 | BIC: | 210.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 20.1010 | 37.578 | 0.535 | 0.599 | -58.286 98.488 |
| C(dose)[T.1] | 54.8185 | 8.740 | 6.272 | 0.000 | 36.587 73.050 |
| expression | 5.2046 | 5.662 | 0.919 | 0.369 | -6.606 17.016 |
| Omnibus: | 1.672 | Durbin-Watson: | 1.876 |
| Prob(Omnibus): | 0.433 | Jarque-Bera (JB): | 0.987 |
| Skew: | -0.056 | Prob(JB): | 0.610 |
| Kurtosis: | 1.991 | Cond. No. | 58.2 |
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: | 05:02:22 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.02063 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.887 |
| Time: | 05:02:22 | Log-Likelihood: | -113.09 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 88.3392 | 60.462 | 1.461 | 0.159 | -37.399 214.077 |
| expression | -1.3435 | 9.355 | -0.144 | 0.887 | -20.798 18.111 |
| Omnibus: | 3.424 | Durbin-Watson: | 2.486 |
| Prob(Omnibus): | 0.181 | Jarque-Bera (JB): | 1.609 |
| Skew: | 0.299 | Prob(JB): | 0.447 |
| Kurtosis: | 1.850 | Cond. No. | 55.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.963 | 0.346 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.505 |
| Model: | OLS | Adj. R-squared: | 0.370 |
| Method: | Least Squares | F-statistic: | 3.744 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0448 |
| Time: | 05:02:22 | Log-Likelihood: | -70.023 |
| No. Observations: | 15 | AIC: | 148.0 |
| Df Residuals: | 11 | BIC: | 150.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 93.4383 | 71.102 | 1.314 | 0.216 | -63.056 249.933 |
| C(dose)[T.1] | 114.5961 | 112.459 | 1.019 | 0.330 | -132.924 362.116 |
| expression | -4.3701 | 11.793 | -0.371 | 0.718 | -30.326 21.585 |
| expression:C(dose)[T.1] | -10.9716 | 18.701 | -0.587 | 0.569 | -52.132 30.189 |
| Omnibus: | 2.361 | Durbin-Watson: | 0.823 |
| Prob(Omnibus): | 0.307 | Jarque-Bera (JB): | 1.500 |
| Skew: | -0.545 | Prob(JB): | 0.472 |
| Kurtosis: | 1.898 | Cond. No. | 114. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.490 |
| Model: | OLS | Adj. R-squared: | 0.405 |
| Method: | Least Squares | F-statistic: | 5.758 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0177 |
| Time: | 05:02:22 | Log-Likelihood: | -70.254 |
| No. Observations: | 15 | AIC: | 146.5 |
| Df Residuals: | 12 | BIC: | 148.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 119.4048 | 54.105 | 2.207 | 0.048 | 1.519 237.290 |
| C(dose)[T.1] | 49.2535 | 15.144 | 3.252 | 0.007 | 16.258 82.249 |
| expression | -8.7330 | 8.899 | -0.981 | 0.346 | -28.122 10.656 |
| Omnibus: | 2.377 | Durbin-Watson: | 0.954 |
| Prob(Omnibus): | 0.305 | Jarque-Bera (JB): | 1.546 |
| Skew: | -0.570 | Prob(JB): | 0.462 |
| Kurtosis: | 1.916 | Cond. No. | 44.4 |
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: | 05:02:22 | 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: | 0.040 |
| Model: | OLS | Adj. R-squared: | -0.034 |
| Method: | Least Squares | F-statistic: | 0.5405 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.475 |
| Time: | 05:02:22 | Log-Likelihood: | -74.995 |
| No. Observations: | 15 | AIC: | 154.0 |
| Df Residuals: | 13 | BIC: | 155.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 145.0119 | 70.545 | 2.056 | 0.060 | -7.390 297.414 |
| expression | -8.6219 | 11.727 | -0.735 | 0.475 | -33.957 16.713 |
| Omnibus: | 1.577 | Durbin-Watson: | 1.757 |
| Prob(Omnibus): | 0.455 | Jarque-Bera (JB): | 0.874 |
| Skew: | 0.113 | Prob(JB): | 0.646 |
| Kurtosis: | 1.839 | Cond. No. | 43.8 |