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 |
| 0.505 | 0.486 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.604 |
| Method: | Least Squares | F-statistic: | 12.20 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000112 |
| Time: | 05:10:02 | Log-Likelihood: | -100.76 |
| No. Observations: | 23 | AIC: | 209.5 |
| Df Residuals: | 19 | BIC: | 214.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 69.0054 | 46.387 | 1.488 | 0.153 | -28.084 166.095 |
| C(dose)[T.1] | 62.3371 | 59.936 | 1.040 | 0.311 | -63.110 187.784 |
| expression | -3.3216 | 10.321 | -0.322 | 0.751 | -24.924 18.281 |
| expression:C(dose)[T.1] | -2.2637 | 13.551 | -0.167 | 0.869 | -30.625 26.098 |
| Omnibus: | 0.186 | Durbin-Watson: | 1.730 |
| Prob(Omnibus): | 0.911 | Jarque-Bera (JB): | 0.393 |
| Skew: | -0.083 | Prob(JB): | 0.822 |
| Kurtosis: | 2.381 | Cond. No. | 84.2 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 19.21 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.21e-05 |
| Time: | 05:10:02 | Log-Likelihood: | -100.78 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 20 | BIC: | 211.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 74.8559 | 29.670 | 2.523 | 0.020 | 12.966 136.746 |
| C(dose)[T.1] | 52.4372 | 8.753 | 5.991 | 0.000 | 34.178 70.696 |
| expression | -4.6349 | 6.523 | -0.711 | 0.486 | -18.242 8.972 |
| Omnibus: | 0.269 | Durbin-Watson: | 1.697 |
| Prob(Omnibus): | 0.874 | Jarque-Bera (JB): | 0.453 |
| Skew: | -0.086 | Prob(JB): | 0.797 |
| Kurtosis: | 2.334 | Cond. No. | 32.0 |
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: | 05:10:02 | 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: | 0.043 |
| Model: | OLS | Adj. R-squared: | -0.002 |
| Method: | Least Squares | F-statistic: | 0.9548 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.340 |
| Time: | 05:10:02 | Log-Likelihood: | -112.59 |
| No. Observations: | 23 | AIC: | 229.2 |
| Df Residuals: | 21 | BIC: | 231.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 124.5971 | 46.468 | 2.681 | 0.014 | 27.962 221.232 |
| expression | -10.2889 | 10.529 | -0.977 | 0.340 | -32.186 11.608 |
| Omnibus: | 2.880 | Durbin-Watson: | 2.510 |
| Prob(Omnibus): | 0.237 | Jarque-Bera (JB): | 1.378 |
| Skew: | 0.212 | Prob(JB): | 0.502 |
| Kurtosis: | 1.879 | Cond. No. | 30.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.101 | 0.173 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.537 |
| Model: | OLS | Adj. R-squared: | 0.411 |
| Method: | Least Squares | F-statistic: | 4.251 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0319 |
| Time: | 05:10:02 | Log-Likelihood: | -69.526 |
| 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 | 173.9950 | 75.359 | 2.309 | 0.041 | 8.130 339.860 |
| C(dose)[T.1] | -20.8966 | 179.773 | -0.116 | 0.910 | -416.575 374.782 |
| expression | -19.4753 | 13.625 | -1.429 | 0.181 | -49.463 10.512 |
| expression:C(dose)[T.1] | 12.6262 | 33.485 | 0.377 | 0.713 | -61.074 86.327 |
| Omnibus: | 3.021 | Durbin-Watson: | 1.270 |
| Prob(Omnibus): | 0.221 | Jarque-Bera (JB): | 1.721 |
| Skew: | -0.830 | Prob(JB): | 0.423 |
| Kurtosis: | 2.969 | Cond. No. | 157. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.531 |
| Model: | OLS | Adj. R-squared: | 0.453 |
| Method: | Least Squares | F-statistic: | 6.791 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0107 |
| Time: | 05:10:02 | Log-Likelihood: | -69.623 |
| No. Observations: | 15 | AIC: | 145.2 |
| Df Residuals: | 12 | BIC: | 147.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 162.5572 | 66.473 | 2.445 | 0.031 | 17.724 307.390 |
| C(dose)[T.1] | 46.6481 | 14.626 | 3.189 | 0.008 | 14.782 78.514 |
| expression | -17.3850 | 11.993 | -1.450 | 0.173 | -43.515 8.745 |
| Omnibus: | 2.931 | Durbin-Watson: | 1.246 |
| Prob(Omnibus): | 0.231 | Jarque-Bera (JB): | 1.671 |
| Skew: | -0.817 | Prob(JB): | 0.434 |
| Kurtosis: | 2.956 | Cond. No. | 51.7 |
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: | 05:10:03 | 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: | 0.133 |
| Model: | OLS | Adj. R-squared: | 0.067 |
| Method: | Least Squares | F-statistic: | 1.999 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.181 |
| Time: | 05:10:03 | Log-Likelihood: | -74.227 |
| No. Observations: | 15 | AIC: | 152.5 |
| Df Residuals: | 13 | BIC: | 153.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 212.2338 | 84.397 | 2.515 | 0.026 | 29.906 394.562 |
| expression | -21.9825 | 15.549 | -1.414 | 0.181 | -55.573 11.608 |
| Omnibus: | 3.462 | Durbin-Watson: | 2.126 |
| Prob(Omnibus): | 0.177 | Jarque-Bera (JB): | 1.443 |
| Skew: | 0.366 | Prob(JB): | 0.486 |
| Kurtosis: | 1.668 | Cond. No. | 50.0 |