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 |
| 0.009 | 0.926 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.665 |
| Model: | OLS | Adj. R-squared: | 0.612 |
| Method: | Least Squares | F-statistic: | 12.59 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.19e-05 |
| Time: | 04:08:17 | Log-Likelihood: | -100.52 |
| No. Observations: | 23 | AIC: | 209.0 |
| Df Residuals: | 19 | BIC: | 213.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -25.5160 | 137.034 | -0.186 | 0.854 | -312.330 261.299 |
| C(dose)[T.1] | 309.9934 | 268.987 | 1.152 | 0.263 | -253.003 872.989 |
| expression | 9.6979 | 16.653 | 0.582 | 0.567 | -25.157 44.553 |
| expression:C(dose)[T.1] | -30.3363 | 31.713 | -0.957 | 0.351 | -96.713 36.040 |
| Omnibus: | 2.909 | Durbin-Watson: | 1.920 |
| Prob(Omnibus): | 0.234 | Jarque-Bera (JB): | 1.348 |
| Skew: | 0.178 | Prob(JB): | 0.510 |
| Kurtosis: | 1.869 | Cond. No. | 621. |
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: | 18.51 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.82e-05 |
| Time: | 04:08:17 | 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 | 43.2490 | 116.416 | 0.372 | 0.714 | -199.591 286.089 |
| C(dose)[T.1] | 52.8676 | 10.084 | 5.243 | 0.000 | 31.833 73.902 |
| expression | 1.3331 | 14.142 | 0.094 | 0.926 | -28.167 30.833 |
| Omnibus: | 0.284 | Durbin-Watson: | 1.912 |
| Prob(Omnibus): | 0.868 | Jarque-Bera (JB): | 0.462 |
| Skew: | 0.045 | Prob(JB): | 0.794 |
| Kurtosis: | 2.312 | Cond. No. | 227. |
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: | 04:08:17 | 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: | 0.167 |
| Model: | OLS | Adj. R-squared: | 0.127 |
| Method: | Least Squares | F-statistic: | 4.214 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0528 |
| Time: | 04:08:17 | Log-Likelihood: | -111.00 |
| No. Observations: | 23 | AIC: | 226.0 |
| Df Residuals: | 21 | BIC: | 228.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -238.7059 | 155.261 | -1.537 | 0.139 | -561.588 84.176 |
| expression | 37.9563 | 18.491 | 2.053 | 0.053 | -0.497 76.410 |
| Omnibus: | 0.937 | Durbin-Watson: | 2.401 |
| Prob(Omnibus): | 0.626 | Jarque-Bera (JB): | 0.862 |
| Skew: | 0.257 | Prob(JB): | 0.650 |
| Kurtosis: | 2.203 | Cond. No. | 201. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 5.129 | 0.043 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.617 |
| Model: | OLS | Adj. R-squared: | 0.512 |
| Method: | Least Squares | F-statistic: | 5.895 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0119 |
| Time: | 04:08:17 | Log-Likelihood: | -68.112 |
| No. Observations: | 15 | AIC: | 144.2 |
| Df Residuals: | 11 | BIC: | 147.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -140.1622 | 166.324 | -0.843 | 0.417 | -506.239 225.915 |
| C(dose)[T.1] | 94.3468 | 189.406 | 0.498 | 0.628 | -322.533 511.227 |
| expression | 26.4773 | 21.175 | 1.250 | 0.237 | -20.129 73.084 |
| expression:C(dose)[T.1] | -6.6437 | 23.864 | -0.278 | 0.786 | -59.168 45.881 |
| Omnibus: | 0.664 | Durbin-Watson: | 1.260 |
| Prob(Omnibus): | 0.718 | Jarque-Bera (JB): | 0.680 |
| Skew: | -0.362 | Prob(JB): | 0.712 |
| Kurtosis: | 2.249 | Cond. No. | 343. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.614 |
| Model: | OLS | Adj. R-squared: | 0.549 |
| Method: | Least Squares | F-statistic: | 9.537 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00332 |
| Time: | 04:08:18 | Log-Likelihood: | -68.164 |
| No. Observations: | 15 | AIC: | 142.3 |
| Df Residuals: | 12 | BIC: | 144.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -99.1495 | 74.183 | -1.337 | 0.206 | -260.781 62.482 |
| C(dose)[T.1] | 41.7636 | 13.577 | 3.076 | 0.010 | 12.182 71.345 |
| expression | 21.2463 | 9.382 | 2.265 | 0.043 | 0.805 41.688 |
| Omnibus: | 0.604 | Durbin-Watson: | 1.133 |
| Prob(Omnibus): | 0.740 | Jarque-Bera (JB): | 0.591 |
| Skew: | -0.382 | Prob(JB): | 0.744 |
| Kurtosis: | 2.399 | Cond. No. | 92.6 |
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: | 04:08:18 | 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: | 0.309 |
| Model: | OLS | Adj. R-squared: | 0.256 |
| Method: | Least Squares | F-statistic: | 5.822 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0313 |
| Time: | 04:08:18 | Log-Likelihood: | -72.525 |
| No. Observations: | 15 | AIC: | 149.0 |
| Df Residuals: | 13 | BIC: | 150.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -132.8757 | 94.270 | -1.410 | 0.182 | -336.534 70.783 |
| expression | 28.2228 | 11.697 | 2.413 | 0.031 | 2.953 53.493 |
| Omnibus: | 0.396 | Durbin-Watson: | 1.425 |
| Prob(Omnibus): | 0.820 | Jarque-Bera (JB): | 0.424 |
| Skew: | 0.312 | Prob(JB): | 0.809 |
| Kurtosis: | 2.462 | Cond. No. | 91.3 |