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 |
| 0.005 | 0.945 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.598 |
| Method: | Least Squares | F-statistic: | 11.90 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000130 |
| Time: | 04:13:19 | Log-Likelihood: | -100.94 |
| No. Observations: | 23 | AIC: | 209.9 |
| Df Residuals: | 19 | BIC: | 214.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 12.4463 | 112.918 | 0.110 | 0.913 | -223.894 248.787 |
| C(dose)[T.1] | 120.2370 | 151.943 | 0.791 | 0.439 | -197.784 438.258 |
| expression | 5.5044 | 14.861 | 0.370 | 0.715 | -25.600 36.608 |
| expression:C(dose)[T.1] | -8.8971 | 20.208 | -0.440 | 0.665 | -51.192 33.398 |
| Omnibus: | 0.306 | Durbin-Watson: | 1.973 |
| Prob(Omnibus): | 0.858 | Jarque-Bera (JB): | 0.475 |
| Skew: | 0.056 | Prob(JB): | 0.788 |
| Kurtosis: | 2.305 | Cond. No. | 343. |
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: | 18.50 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.83e-05 |
| Time: | 04:13:19 | 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 | 48.9525 | 75.092 | 0.652 | 0.522 | -107.686 205.591 |
| C(dose)[T.1] | 53.4601 | 8.942 | 5.979 | 0.000 | 34.808 72.112 |
| expression | 0.6927 | 9.865 | 0.070 | 0.945 | -19.885 21.271 |
| Omnibus: | 0.335 | Durbin-Watson: | 1.899 |
| Prob(Omnibus): | 0.846 | Jarque-Bera (JB): | 0.493 |
| Skew: | 0.061 | Prob(JB): | 0.782 |
| Kurtosis: | 2.293 | Cond. No. | 131. |
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: | 04:13:19 | 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: | 0.022 |
| Model: | OLS | Adj. R-squared: | -0.024 |
| Method: | Least Squares | F-statistic: | 0.4743 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.499 |
| Time: | 04:13:19 | Log-Likelihood: | -112.85 |
| 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 | 161.1510 | 118.461 | 1.360 | 0.188 | -85.203 407.505 |
| expression | -10.8548 | 15.762 | -0.689 | 0.499 | -43.633 21.924 |
| Omnibus: | 2.495 | Durbin-Watson: | 2.479 |
| Prob(Omnibus): | 0.287 | Jarque-Bera (JB): | 1.344 |
| Skew: | 0.254 | Prob(JB): | 0.511 |
| Kurtosis: | 1.930 | Cond. No. | 127. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 5.658 | 0.035 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.648 |
| Model: | OLS | Adj. R-squared: | 0.552 |
| Method: | Least Squares | F-statistic: | 6.753 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00757 |
| Time: | 04:13:19 | Log-Likelihood: | -67.467 |
| No. Observations: | 15 | AIC: | 142.9 |
| Df Residuals: | 11 | BIC: | 145.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -435.6814 | 284.926 | -1.529 | 0.154 | -1062.799 191.436 |
| C(dose)[T.1] | 320.8792 | 313.825 | 1.022 | 0.329 | -369.846 1011.604 |
| expression | 69.2288 | 39.184 | 1.767 | 0.105 | -17.015 155.472 |
| expression:C(dose)[T.1] | -36.5236 | 43.352 | -0.842 | 0.417 | -131.940 58.892 |
| Omnibus: | 0.691 | Durbin-Watson: | 1.275 |
| Prob(Omnibus): | 0.708 | Jarque-Bera (JB): | 0.428 |
| Skew: | 0.383 | Prob(JB): | 0.807 |
| Kurtosis: | 2.688 | Cond. No. | 534. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.625 |
| Model: | OLS | Adj. R-squared: | 0.563 |
| Method: | Least Squares | F-statistic: | 10.02 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00276 |
| Time: | 04:13:19 | Log-Likelihood: | -67.936 |
| No. Observations: | 15 | AIC: | 141.9 |
| Df Residuals: | 12 | BIC: | 144.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -218.8312 | 120.716 | -1.813 | 0.095 | -481.849 44.186 |
| C(dose)[T.1] | 56.7273 | 13.356 | 4.247 | 0.001 | 27.627 85.827 |
| expression | 39.3898 | 16.559 | 2.379 | 0.035 | 3.310 75.470 |
| Omnibus: | 1.192 | Durbin-Watson: | 1.167 |
| Prob(Omnibus): | 0.551 | Jarque-Bera (JB): | 0.706 |
| Skew: | 0.515 | Prob(JB): | 0.703 |
| Kurtosis: | 2.735 | Cond. No. | 137. |
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: | 04:13:19 | 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: | 0.062 |
| Model: | OLS | Adj. R-squared: | -0.010 |
| Method: | Least Squares | F-statistic: | 0.8629 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.370 |
| Time: | 04:13:19 | Log-Likelihood: | -74.818 |
| No. Observations: | 15 | AIC: | 153.6 |
| Df Residuals: | 13 | BIC: | 155.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -69.1106 | 175.506 | -0.394 | 0.700 | -448.268 310.047 |
| expression | 22.7172 | 24.455 | 0.929 | 0.370 | -30.115 75.549 |
| Omnibus: | 0.526 | Durbin-Watson: | 1.714 |
| Prob(Omnibus): | 0.769 | Jarque-Bera (JB): | 0.587 |
| Skew: | 0.327 | Prob(JB): | 0.746 |
| Kurtosis: | 2.285 | Cond. No. | 130. |