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 |
| 0.918 | 0.350 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.692 |
| Model: | OLS | Adj. R-squared: | 0.644 |
| Method: | Least Squares | F-statistic: | 14.24 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.23e-05 |
| Time: | 04:54:29 | Log-Likelihood: | -99.555 |
| No. Observations: | 23 | AIC: | 207.1 |
| Df Residuals: | 19 | BIC: | 211.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 63.1181 | 56.678 | 1.114 | 0.279 | -55.510 181.746 |
| C(dose)[T.1] | 213.0062 | 118.450 | 1.798 | 0.088 | -34.912 460.925 |
| expression | -1.3603 | 8.607 | -0.158 | 0.876 | -19.375 16.655 |
| expression:C(dose)[T.1] | -21.7712 | 16.645 | -1.308 | 0.206 | -56.610 13.068 |
| Omnibus: | 0.158 | Durbin-Watson: | 1.720 |
| Prob(Omnibus): | 0.924 | Jarque-Bera (JB): | 0.374 |
| Skew: | 0.040 | Prob(JB): | 0.829 |
| Kurtosis: | 2.380 | Cond. No. | 238. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.664 |
| Model: | OLS | Adj. R-squared: | 0.631 |
| Method: | Least Squares | F-statistic: | 19.80 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.81e-05 |
| Time: | 04:54:29 | Log-Likelihood: | -100.55 |
| No. Observations: | 23 | AIC: | 207.1 |
| Df Residuals: | 20 | BIC: | 210.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 101.2482 | 49.462 | 2.047 | 0.054 | -1.927 204.423 |
| C(dose)[T.1] | 58.6363 | 10.205 | 5.746 | 0.000 | 37.350 79.923 |
| expression | -7.1817 | 7.497 | -0.958 | 0.350 | -22.820 8.457 |
| Omnibus: | 0.351 | Durbin-Watson: | 1.905 |
| Prob(Omnibus): | 0.839 | Jarque-Bera (JB): | 0.505 |
| Skew: | 0.198 | Prob(JB): | 0.777 |
| Kurtosis: | 2.391 | Cond. No. | 82.4 |
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: | 04:54:29 | 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: | 0.111 |
| Model: | OLS | Adj. R-squared: | 0.068 |
| Method: | Least Squares | F-statistic: | 2.609 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.121 |
| Time: | 04:54:29 | Log-Likelihood: | -111.76 |
| No. Observations: | 23 | AIC: | 227.5 |
| Df Residuals: | 21 | BIC: | 229.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -31.9000 | 69.431 | -0.459 | 0.651 | -176.290 112.490 |
| expression | 16.1697 | 10.010 | 1.615 | 0.121 | -4.647 36.986 |
| Omnibus: | 0.587 | Durbin-Watson: | 2.231 |
| Prob(Omnibus): | 0.746 | Jarque-Bera (JB): | 0.661 |
| Skew: | 0.202 | Prob(JB): | 0.718 |
| Kurtosis: | 2.275 | Cond. No. | 72.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.020 | 0.890 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.535 |
| Model: | OLS | Adj. R-squared: | 0.409 |
| Method: | Least Squares | F-statistic: | 4.224 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0324 |
| Time: | 04:54:29 | Log-Likelihood: | -69.552 |
| No. Observations: | 15 | AIC: | 147.1 |
| Df Residuals: | 11 | BIC: | 149.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 123.3297 | 92.280 | 1.336 | 0.208 | -79.777 326.436 |
| C(dose)[T.1] | -207.9663 | 181.037 | -1.149 | 0.275 | -606.426 190.493 |
| expression | -9.4330 | 15.460 | -0.610 | 0.554 | -43.461 24.595 |
| expression:C(dose)[T.1] | 42.5979 | 29.914 | 1.424 | 0.182 | -23.243 108.439 |
| Omnibus: | 1.977 | Durbin-Watson: | 0.899 |
| Prob(Omnibus): | 0.372 | Jarque-Bera (JB): | 1.525 |
| Skew: | -0.710 | Prob(JB): | 0.467 |
| Kurtosis: | 2.349 | Cond. No. | 181. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.450 |
| Model: | OLS | Adj. R-squared: | 0.358 |
| Method: | Least Squares | F-statistic: | 4.903 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0278 |
| Time: | 04:54:29 | Log-Likelihood: | -70.821 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 55.9023 | 82.528 | 0.677 | 0.511 | -123.910 235.714 |
| C(dose)[T.1] | 48.9194 | 15.849 | 3.087 | 0.009 | 14.388 83.451 |
| expression | 1.9450 | 13.791 | 0.141 | 0.890 | -28.102 31.992 |
| Omnibus: | 2.677 | Durbin-Watson: | 0.800 |
| Prob(Omnibus): | 0.262 | Jarque-Bera (JB): | 1.873 |
| Skew: | -0.840 | Prob(JB): | 0.392 |
| Kurtosis: | 2.584 | Cond. No. | 65.4 |
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: | 04:54:29 | 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: | 0.013 |
| Model: | OLS | Adj. R-squared: | -0.063 |
| Method: | Least Squares | F-statistic: | 0.1681 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.688 |
| Time: | 04:54:29 | Log-Likelihood: | -75.204 |
| No. Observations: | 15 | AIC: | 154.4 |
| Df Residuals: | 13 | BIC: | 155.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 50.3336 | 106.174 | 0.474 | 0.643 | -179.042 279.709 |
| expression | 7.2197 | 17.609 | 0.410 | 0.688 | -30.823 45.263 |
| Omnibus: | 0.316 | Durbin-Watson: | 1.520 |
| Prob(Omnibus): | 0.854 | Jarque-Bera (JB): | 0.461 |
| Skew: | -0.075 | Prob(JB): | 0.794 |
| Kurtosis: | 2.154 | Cond. No. | 65.1 |