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 |
| 0.296 | 0.593 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.676 |
| Model: | OLS | Adj. R-squared: | 0.624 |
| Method: | Least Squares | F-statistic: | 13.19 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.88e-05 |
| Time: | 04:05:17 | Log-Likelihood: | -100.16 |
| No. Observations: | 23 | AIC: | 208.3 |
| Df Residuals: | 19 | BIC: | 212.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 155.0562 | 82.131 | 1.888 | 0.074 | -16.847 326.959 |
| C(dose)[T.1] | -60.7189 | 106.072 | -0.572 | 0.574 | -282.730 161.292 |
| expression | -18.0288 | 14.644 | -1.231 | 0.233 | -48.679 12.621 |
| expression:C(dose)[T.1] | 20.0662 | 17.909 | 1.120 | 0.276 | -17.417 57.550 |
| Omnibus: | 0.615 | Durbin-Watson: | 2.471 |
| Prob(Omnibus): | 0.735 | Jarque-Bera (JB): | 0.637 |
| Skew: | -0.083 | Prob(JB): | 0.727 |
| Kurtosis: | 2.201 | Cond. No. | 214. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.654 |
| Model: | OLS | Adj. R-squared: | 0.620 |
| Method: | Least Squares | F-statistic: | 18.92 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.45e-05 |
| Time: | 04:05:17 | Log-Likelihood: | -100.89 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 20 | BIC: | 211.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 80.0072 | 47.834 | 1.673 | 0.110 | -19.774 179.788 |
| C(dose)[T.1] | 57.4376 | 11.519 | 4.987 | 0.000 | 33.410 81.465 |
| expression | -4.6121 | 8.483 | -0.544 | 0.593 | -22.308 13.084 |
| Omnibus: | 0.033 | Durbin-Watson: | 1.953 |
| Prob(Omnibus): | 0.984 | Jarque-Bera (JB): | 0.239 |
| Skew: | -0.040 | Prob(JB): | 0.887 |
| Kurtosis: | 2.507 | Cond. No. | 69.5 |
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: | 04:05:18 | 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: | 0.224 |
| Model: | OLS | Adj. R-squared: | 0.187 |
| Method: | Least Squares | F-statistic: | 6.069 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0225 |
| Time: | 04:05:18 | Log-Likelihood: | -110.19 |
| No. Observations: | 23 | AIC: | 224.4 |
| Df Residuals: | 21 | BIC: | 226.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -59.2463 | 56.766 | -1.044 | 0.308 | -177.298 58.805 |
| expression | 23.0878 | 9.372 | 2.464 | 0.022 | 3.598 42.578 |
| Omnibus: | 0.742 | Durbin-Watson: | 2.087 |
| Prob(Omnibus): | 0.690 | Jarque-Bera (JB): | 0.706 |
| Skew: | 0.369 | Prob(JB): | 0.703 |
| Kurtosis: | 2.560 | Cond. No. | 55.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.302 | 0.276 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.518 |
| Model: | OLS | Adj. R-squared: | 0.387 |
| Method: | Least Squares | F-statistic: | 3.943 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0391 |
| Time: | 04:05:18 | Log-Likelihood: | -69.824 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 11 | BIC: | 150.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 181.1874 | 91.091 | 1.989 | 0.072 | -19.302 381.677 |
| C(dose)[T.1] | -66.6809 | 198.084 | -0.337 | 0.743 | -502.661 369.299 |
| expression | -19.6149 | 15.587 | -1.258 | 0.234 | -53.921 14.691 |
| expression:C(dose)[T.1] | 19.9749 | 33.660 | 0.593 | 0.565 | -54.110 94.060 |
| Omnibus: | 2.995 | Durbin-Watson: | 1.352 |
| Prob(Omnibus): | 0.224 | Jarque-Bera (JB): | 1.821 |
| Skew: | -0.851 | Prob(JB): | 0.402 |
| Kurtosis: | 2.864 | Cond. No. | 186. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.503 |
| Model: | OLS | Adj. R-squared: | 0.420 |
| Method: | Least Squares | F-statistic: | 6.066 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0151 |
| Time: | 04:05:18 | Log-Likelihood: | -70.060 |
| No. Observations: | 15 | AIC: | 146.1 |
| Df Residuals: | 12 | BIC: | 148.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 156.3464 | 78.689 | 1.987 | 0.070 | -15.102 327.795 |
| C(dose)[T.1] | 50.5127 | 14.994 | 3.369 | 0.006 | 17.844 83.182 |
| expression | -15.3317 | 13.437 | -1.141 | 0.276 | -44.608 13.945 |
| Omnibus: | 5.121 | Durbin-Watson: | 1.225 |
| Prob(Omnibus): | 0.077 | Jarque-Bera (JB): | 2.764 |
| Skew: | -1.026 | Prob(JB): | 0.251 |
| Kurtosis: | 3.460 | Cond. No. | 64.0 |
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: | 04:05:18 | 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: | 0.032 |
| Model: | OLS | Adj. R-squared: | -0.042 |
| Method: | Least Squares | F-statistic: | 0.4355 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.521 |
| Time: | 04:05:18 | Log-Likelihood: | -75.053 |
| No. Observations: | 15 | AIC: | 154.1 |
| Df Residuals: | 13 | BIC: | 155.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 162.9288 | 105.425 | 1.545 | 0.146 | -64.829 390.686 |
| expression | -11.8490 | 17.954 | -0.660 | 0.521 | -50.637 26.939 |
| Omnibus: | 1.375 | Durbin-Watson: | 1.919 |
| Prob(Omnibus): | 0.503 | Jarque-Bera (JB): | 0.859 |
| Skew: | 0.189 | Prob(JB): | 0.651 |
| Kurtosis: | 1.891 | Cond. No. | 63.7 |