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 |
| 0.211 | 0.651 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.672 |
| Model: | OLS | Adj. R-squared: | 0.620 |
| Method: | Least Squares | F-statistic: | 12.98 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.62e-05 |
| Time: | 05:11:23 | Log-Likelihood: | -100.28 |
| No. Observations: | 23 | AIC: | 208.6 |
| Df Residuals: | 19 | BIC: | 213.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 20.8405 | 74.768 | 0.279 | 0.783 | -135.651 177.332 |
| C(dose)[T.1] | 164.4021 | 104.767 | 1.569 | 0.133 | -54.877 383.681 |
| expression | 6.9872 | 15.606 | 0.448 | 0.659 | -25.676 39.650 |
| expression:C(dose)[T.1] | -22.7793 | 21.550 | -1.057 | 0.304 | -67.884 22.325 |
| Omnibus: | 0.527 | Durbin-Watson: | 2.201 |
| Prob(Omnibus): | 0.768 | Jarque-Bera (JB): | 0.564 |
| Skew: | 0.307 | Prob(JB): | 0.754 |
| Kurtosis: | 2.541 | Cond. No. | 160. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.618 |
| Method: | Least Squares | F-statistic: | 18.80 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.55e-05 |
| Time: | 05:11:23 | Log-Likelihood: | -100.94 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 20 | BIC: | 211.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 77.8884 | 51.897 | 1.501 | 0.149 | -30.368 186.145 |
| C(dose)[T.1] | 54.0534 | 8.862 | 6.099 | 0.000 | 35.567 72.540 |
| expression | -4.9586 | 10.794 | -0.459 | 0.651 | -27.474 17.556 |
| Omnibus: | 0.276 | Durbin-Watson: | 1.839 |
| Prob(Omnibus): | 0.871 | Jarque-Bera (JB): | 0.422 |
| Skew: | 0.206 | Prob(JB): | 0.810 |
| Kurtosis: | 2.480 | Cond. No. | 60.7 |
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: | 05:11:23 | 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: | 0.007 |
| Model: | OLS | Adj. R-squared: | -0.041 |
| Method: | Least Squares | F-statistic: | 0.1427 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.709 |
| Time: | 05:11:23 | Log-Likelihood: | -113.03 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 47.6280 | 85.261 | 0.559 | 0.582 | -129.681 224.937 |
| expression | 6.6237 | 17.536 | 0.378 | 0.709 | -29.845 43.092 |
| Omnibus: | 2.909 | Durbin-Watson: | 2.525 |
| Prob(Omnibus): | 0.233 | Jarque-Bera (JB): | 1.526 |
| Skew: | 0.314 | Prob(JB): | 0.466 |
| Kurtosis: | 1.905 | Cond. No. | 60.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.020 | 0.332 | 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: | 0.369 |
| Method: | Least Squares | F-statistic: | 3.734 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0451 |
| Time: | 05:11:23 | Log-Likelihood: | -70.032 |
| No. Observations: | 15 | AIC: | 148.1 |
| Df Residuals: | 11 | BIC: | 150.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 15.1266 | 118.277 | 0.128 | 0.901 | -245.200 275.453 |
| C(dose)[T.1] | -57.0586 | 195.538 | -0.292 | 0.776 | -487.434 373.317 |
| expression | 10.7082 | 24.103 | 0.444 | 0.665 | -42.343 63.759 |
| expression:C(dose)[T.1] | 20.6726 | 39.067 | 0.529 | 0.607 | -65.313 106.659 |
| Omnibus: | 0.956 | Durbin-Watson: | 0.717 |
| Prob(Omnibus): | 0.620 | Jarque-Bera (JB): | 0.859 |
| Skew: | -0.407 | Prob(JB): | 0.651 |
| Kurtosis: | 2.156 | Cond. No. | 165. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.492 |
| Model: | OLS | Adj. R-squared: | 0.407 |
| Method: | Least Squares | F-statistic: | 5.810 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0172 |
| Time: | 05:11:23 | Log-Likelihood: | -70.221 |
| No. Observations: | 15 | AIC: | 146.4 |
| Df Residuals: | 12 | BIC: | 148.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -23.3088 | 90.503 | -0.258 | 0.801 | -220.498 173.880 |
| C(dose)[T.1] | 46.0683 | 15.424 | 2.987 | 0.011 | 12.461 79.675 |
| expression | 18.5774 | 18.391 | 1.010 | 0.332 | -21.493 58.648 |
| Omnibus: | 1.415 | Durbin-Watson: | 0.651 |
| Prob(Omnibus): | 0.493 | Jarque-Bera (JB): | 1.164 |
| Skew: | -0.553 | Prob(JB): | 0.559 |
| Kurtosis: | 2.200 | Cond. No. | 62.7 |
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: | 05:11:23 | 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: | 0.114 |
| Model: | OLS | Adj. R-squared: | 0.046 |
| Method: | Least Squares | F-statistic: | 1.678 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.218 |
| Time: | 05:11:23 | Log-Likelihood: | -74.390 |
| No. Observations: | 15 | AIC: | 152.8 |
| Df Residuals: | 13 | BIC: | 154.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -53.5947 | 114.086 | -0.470 | 0.646 | -300.063 192.874 |
| expression | 29.6056 | 22.855 | 1.295 | 0.218 | -19.770 78.981 |
| Omnibus: | 0.678 | Durbin-Watson: | 1.179 |
| Prob(Omnibus): | 0.713 | Jarque-Bera (JB): | 0.634 |
| Skew: | 0.169 | Prob(JB): | 0.728 |
| Kurtosis: | 2.051 | Cond. No. | 61.9 |