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 |
| 3.609 | 0.072 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.703 |
| Model: | OLS | Adj. R-squared: | 0.656 |
| Method: | Least Squares | F-statistic: | 14.97 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.05e-05 |
| Time: | 04:12:52 | Log-Likelihood: | -99.154 |
| No. Observations: | 23 | AIC: | 206.3 |
| Df Residuals: | 19 | BIC: | 210.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 0.7840 | 36.958 | 0.021 | 0.983 | -76.570 78.138 |
| C(dose)[T.1] | 61.6160 | 54.605 | 1.128 | 0.273 | -52.674 175.906 |
| expression | 8.5062 | 5.813 | 1.463 | 0.160 | -3.661 20.674 |
| expression:C(dose)[T.1] | -0.4388 | 9.179 | -0.048 | 0.962 | -19.650 18.773 |
| Omnibus: | 0.186 | Durbin-Watson: | 1.740 |
| Prob(Omnibus): | 0.911 | Jarque-Bera (JB): | 0.032 |
| Skew: | -0.064 | Prob(JB): | 0.984 |
| Kurtosis: | 2.871 | Cond. No. | 102. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.703 |
| Model: | OLS | Adj. R-squared: | 0.673 |
| Method: | Least Squares | F-statistic: | 23.64 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.39e-06 |
| Time: | 04:12:52 | Log-Likelihood: | -99.155 |
| No. Observations: | 23 | AIC: | 204.3 |
| Df Residuals: | 20 | BIC: | 207.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 1.8896 | 28.101 | 0.067 | 0.947 | -56.729 60.508 |
| C(dose)[T.1] | 59.0397 | 8.612 | 6.856 | 0.000 | 41.075 77.004 |
| expression | 8.3302 | 4.385 | 1.900 | 0.072 | -0.817 17.477 |
| Omnibus: | 0.201 | Durbin-Watson: | 1.727 |
| Prob(Omnibus): | 0.904 | Jarque-Bera (JB): | 0.032 |
| Skew: | -0.070 | Prob(JB): | 0.984 |
| Kurtosis: | 2.880 | Cond. No. | 43.7 |
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: | 04:12:52 | 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: | 0.004 |
| Model: | OLS | Adj. R-squared: | -0.043 |
| Method: | Least Squares | F-statistic: | 0.08569 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.773 |
| Time: | 04:12:52 | Log-Likelihood: | -113.06 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 92.5107 | 44.294 | 2.089 | 0.049 | 0.396 184.625 |
| expression | -2.1490 | 7.341 | -0.293 | 0.773 | -17.416 13.118 |
| Omnibus: | 2.609 | Durbin-Watson: | 2.465 |
| Prob(Omnibus): | 0.271 | Jarque-Bera (JB): | 1.383 |
| Skew: | 0.263 | Prob(JB): | 0.501 |
| Kurtosis: | 1.920 | Cond. No. | 38.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.653 | 0.435 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.581 |
| Model: | OLS | Adj. R-squared: | 0.467 |
| Method: | Least Squares | F-statistic: | 5.087 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0189 |
| Time: | 04:12:52 | Log-Likelihood: | -68.773 |
| No. Observations: | 15 | AIC: | 145.5 |
| Df Residuals: | 11 | BIC: | 148.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 96.1376 | 95.094 | 1.011 | 0.334 | -113.163 305.438 |
| C(dose)[T.1] | -194.2761 | 150.895 | -1.287 | 0.224 | -526.394 137.842 |
| expression | -4.4478 | 14.643 | -0.304 | 0.767 | -36.677 27.781 |
| expression:C(dose)[T.1] | 40.2093 | 24.338 | 1.652 | 0.127 | -13.359 93.778 |
| Omnibus: | 3.194 | Durbin-Watson: | 1.010 |
| Prob(Omnibus): | 0.202 | Jarque-Bera (JB): | 1.960 |
| Skew: | -0.883 | Prob(JB): | 0.375 |
| Kurtosis: | 2.871 | Cond. No. | 169. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.477 |
| Model: | OLS | Adj. R-squared: | 0.390 |
| Method: | Least Squares | F-statistic: | 5.477 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0204 |
| Time: | 04:12:52 | Log-Likelihood: | -70.436 |
| No. Observations: | 15 | AIC: | 146.9 |
| Df Residuals: | 12 | BIC: | 149.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 2.1912 | 81.526 | 0.027 | 0.979 | -175.438 179.820 |
| C(dose)[T.1] | 53.7368 | 16.326 | 3.291 | 0.006 | 18.165 89.309 |
| expression | 10.1070 | 12.511 | 0.808 | 0.435 | -17.152 37.366 |
| Omnibus: | 2.387 | Durbin-Watson: | 0.792 |
| Prob(Omnibus): | 0.303 | Jarque-Bera (JB): | 1.748 |
| Skew: | -0.794 | Prob(JB): | 0.417 |
| Kurtosis: | 2.475 | Cond. No. | 68.7 |
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: | 04:12:52 | 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: | 0.005 |
| Model: | OLS | Adj. R-squared: | -0.071 |
| Method: | Least Squares | F-statistic: | 0.06831 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.798 |
| Time: | 04:12:52 | Log-Likelihood: | -75.261 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 155.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 118.9537 | 97.281 | 1.223 | 0.243 | -91.210 329.117 |
| expression | -4.0686 | 15.567 | -0.261 | 0.798 | -37.700 29.562 |
| Omnibus: | 0.589 | Durbin-Watson: | 1.548 |
| Prob(Omnibus): | 0.745 | Jarque-Bera (JB): | 0.580 |
| Skew: | 0.081 | Prob(JB): | 0.748 |
| Kurtosis: | 2.050 | Cond. No. | 61.5 |