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 |
| 1.650 | 0.214 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.685 |
| Model: | OLS | Adj. R-squared: | 0.635 |
| Method: | Least Squares | F-statistic: | 13.74 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.31e-05 |
| Time: | 03:51:57 | Log-Likelihood: | -99.837 |
| No. Observations: | 23 | AIC: | 207.7 |
| Df Residuals: | 19 | BIC: | 212.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 237.3649 | 127.210 | 1.866 | 0.078 | -28.889 503.619 |
| C(dose)[T.1] | -91.8750 | 201.272 | -0.456 | 0.653 | -513.143 329.393 |
| expression | -24.9081 | 17.281 | -1.441 | 0.166 | -61.078 11.262 |
| expression:C(dose)[T.1] | 19.7744 | 27.263 | 0.725 | 0.477 | -37.287 76.836 |
| Omnibus: | 0.344 | Durbin-Watson: | 1.932 |
| Prob(Omnibus): | 0.842 | Jarque-Bera (JB): | 0.499 |
| Skew: | 0.067 | Prob(JB): | 0.779 |
| Kurtosis: | 2.291 | Cond. No. | 441. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.676 |
| Model: | OLS | Adj. R-squared: | 0.643 |
| Method: | Least Squares | F-statistic: | 20.85 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.28e-05 |
| Time: | 03:51:57 | Log-Likelihood: | -100.15 |
| No. Observations: | 23 | AIC: | 206.3 |
| Df Residuals: | 20 | BIC: | 209.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 178.9410 | 97.287 | 1.839 | 0.081 | -23.995 381.877 |
| C(dose)[T.1] | 53.9818 | 8.444 | 6.393 | 0.000 | 36.368 71.596 |
| expression | -16.9628 | 13.207 | -1.284 | 0.214 | -44.511 10.586 |
| Omnibus: | 0.998 | Durbin-Watson: | 1.959 |
| Prob(Omnibus): | 0.607 | Jarque-Bera (JB): | 0.806 |
| Skew: | 0.127 | Prob(JB): | 0.668 |
| Kurtosis: | 2.119 | Cond. No. | 174. |
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: | 03:51:57 | 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: | 0.013 |
| Model: | OLS | Adj. R-squared: | -0.034 |
| Method: | Least Squares | F-statistic: | 0.2832 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.600 |
| Time: | 03:51:57 | Log-Likelihood: | -112.95 |
| No. Observations: | 23 | AIC: | 229.9 |
| Df Residuals: | 21 | BIC: | 232.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 167.7650 | 165.604 | 1.013 | 0.323 | -176.628 512.158 |
| expression | -11.9444 | 22.445 | -0.532 | 0.600 | -58.620 34.732 |
| Omnibus: | 3.256 | Durbin-Watson: | 2.511 |
| Prob(Omnibus): | 0.196 | Jarque-Bera (JB): | 1.701 |
| Skew: | 0.368 | Prob(JB): | 0.427 |
| Kurtosis: | 1.889 | Cond. No. | 174. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 5.796 | 0.033 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.630 |
| Model: | OLS | Adj. R-squared: | 0.529 |
| Method: | Least Squares | F-statistic: | 6.235 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00990 |
| Time: | 03:51:57 | Log-Likelihood: | -67.849 |
| No. Observations: | 15 | AIC: | 143.7 |
| Df Residuals: | 11 | BIC: | 146.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 379.6322 | 231.216 | 1.642 | 0.129 | -129.270 888.535 |
| C(dose)[T.1] | 130.6866 | 311.812 | 0.419 | 0.683 | -555.606 816.980 |
| expression | -42.4449 | 31.406 | -1.351 | 0.204 | -111.569 26.679 |
| expression:C(dose)[T.1] | -8.4709 | 41.436 | -0.204 | 0.842 | -99.671 82.729 |
| Omnibus: | 0.979 | Durbin-Watson: | 1.593 |
| Prob(Omnibus): | 0.613 | Jarque-Bera (JB): | 0.701 |
| Skew: | 0.021 | Prob(JB): | 0.704 |
| Kurtosis: | 1.942 | Cond. No. | 491. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.628 |
| Model: | OLS | Adj. R-squared: | 0.566 |
| Method: | Least Squares | F-statistic: | 10.14 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00264 |
| Time: | 03:51:57 | Log-Likelihood: | -67.878 |
| No. Observations: | 15 | AIC: | 141.8 |
| Df Residuals: | 12 | BIC: | 143.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 415.4262 | 144.857 | 2.868 | 0.014 | 99.809 731.043 |
| C(dose)[T.1] | 67.0210 | 14.895 | 4.499 | 0.001 | 34.567 99.475 |
| expression | -47.3112 | 19.652 | -2.407 | 0.033 | -90.129 -4.493 |
| Omnibus: | 1.219 | Durbin-Watson: | 1.658 |
| Prob(Omnibus): | 0.544 | Jarque-Bera (JB): | 0.767 |
| Skew: | 0.011 | Prob(JB): | 0.681 |
| Kurtosis: | 1.892 | Cond. No. | 174. |
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: | 03:51:57 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.076 |
| Method: | Least Squares | F-statistic: | 0.01565 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.902 |
| Time: | 03:51:57 | Log-Likelihood: | -75.291 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 119.0512 | 203.190 | 0.586 | 0.568 | -319.915 558.017 |
| expression | -3.3593 | 26.856 | -0.125 | 0.902 | -61.378 54.660 |
| Omnibus: | 0.646 | Durbin-Watson: | 1.652 |
| Prob(Omnibus): | 0.724 | Jarque-Bera (JB): | 0.601 |
| Skew: | 0.083 | Prob(JB): | 0.740 |
| Kurtosis: | 2.033 | Cond. No. | 154. |