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 |
| 1.385 | 0.253 | 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: | 12.97 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.64e-05 |
| Time: | 05:24:25 | Log-Likelihood: | -100.29 |
| 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 | -133.9159 | 191.926 | -0.698 | 0.494 | -535.621 267.789 |
| C(dose)[T.1] | 82.9853 | 325.930 | 0.255 | 0.802 | -599.194 765.165 |
| expression | 19.7352 | 20.124 | 0.981 | 0.339 | -22.385 61.855 |
| expression:C(dose)[T.1] | -2.9313 | 34.420 | -0.085 | 0.933 | -74.974 69.111 |
| Omnibus: | 0.535 | Durbin-Watson: | 1.440 |
| Prob(Omnibus): | 0.765 | Jarque-Bera (JB): | 0.522 |
| Skew: | -0.312 | Prob(JB): | 0.770 |
| Kurtosis: | 2.607 | Cond. No. | 872. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.672 |
| Model: | OLS | Adj. R-squared: | 0.639 |
| Method: | Least Squares | F-statistic: | 20.47 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.45e-05 |
| Time: | 05:24:25 | Log-Likelihood: | -100.29 |
| No. Observations: | 23 | AIC: | 206.6 |
| Df Residuals: | 20 | BIC: | 210.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -124.3645 | 151.831 | -0.819 | 0.422 | -441.078 192.349 |
| C(dose)[T.1] | 55.2387 | 8.634 | 6.398 | 0.000 | 37.229 73.248 |
| expression | 18.7332 | 15.916 | 1.177 | 0.253 | -14.467 51.933 |
| Omnibus: | 0.561 | Durbin-Watson: | 1.447 |
| Prob(Omnibus): | 0.755 | Jarque-Bera (JB): | 0.513 |
| Skew: | -0.319 | Prob(JB): | 0.774 |
| Kurtosis: | 2.641 | Cond. No. | 344. |
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: | 05:24:25 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.048 |
| Method: | Least Squares | F-statistic: | 0.0001468 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.990 |
| Time: | 05:24:25 | Log-Likelihood: | -113.10 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 82.7781 | 252.687 | 0.328 | 0.746 | -442.714 608.270 |
| expression | -0.3227 | 26.633 | -0.012 | 0.990 | -55.709 55.064 |
| Omnibus: | 3.302 | Durbin-Watson: | 2.491 |
| Prob(Omnibus): | 0.192 | Jarque-Bera (JB): | 1.570 |
| Skew: | 0.289 | Prob(JB): | 0.456 |
| Kurtosis: | 1.859 | Cond. No. | 336. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.582 | 0.460 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.508 |
| Model: | OLS | Adj. R-squared: | 0.374 |
| Method: | Least Squares | F-statistic: | 3.787 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0435 |
| Time: | 05:24:25 | Log-Likelihood: | -69.980 |
| No. Observations: | 15 | AIC: | 148.0 |
| Df Residuals: | 11 | BIC: | 150.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 98.1836 | 269.569 | 0.364 | 0.723 | -495.134 691.501 |
| C(dose)[T.1] | 475.5976 | 481.692 | 0.987 | 0.345 | -584.599 1535.794 |
| expression | -3.1767 | 27.819 | -0.114 | 0.911 | -64.405 58.052 |
| expression:C(dose)[T.1] | -41.8568 | 48.158 | -0.869 | 0.403 | -147.852 64.138 |
| Omnibus: | 3.662 | Durbin-Watson: | 0.753 |
| Prob(Omnibus): | 0.160 | Jarque-Bera (JB): | 2.553 |
| Skew: | -0.995 | Prob(JB): | 0.279 |
| Kurtosis: | 2.652 | Cond. No. | 776. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.474 |
| Model: | OLS | Adj. R-squared: | 0.387 |
| Method: | Least Squares | F-statistic: | 5.413 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0211 |
| Time: | 05:24:25 | Log-Likelihood: | -70.478 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 12 | BIC: | 149.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 233.4079 | 217.886 | 1.071 | 0.305 | -241.326 708.141 |
| C(dose)[T.1] | 57.2517 | 18.649 | 3.070 | 0.010 | 16.618 97.885 |
| expression | -17.1438 | 22.475 | -0.763 | 0.460 | -66.113 31.826 |
| Omnibus: | 3.885 | Durbin-Watson: | 0.869 |
| Prob(Omnibus): | 0.143 | Jarque-Bera (JB): | 2.606 |
| Skew: | -1.014 | Prob(JB): | 0.272 |
| Kurtosis: | 2.766 | Cond. No. | 286. |
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: | 05:24:25 | 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: | 0.061 |
| Model: | OLS | Adj. R-squared: | -0.011 |
| Method: | Least Squares | F-statistic: | 0.8501 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.373 |
| Time: | 05:24:25 | Log-Likelihood: | -74.825 |
| No. Observations: | 15 | AIC: | 153.7 |
| Df Residuals: | 13 | BIC: | 155.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -124.1076 | 236.405 | -0.525 | 0.608 | -634.829 386.614 |
| expression | 21.9261 | 23.781 | 0.922 | 0.373 | -29.450 73.302 |
| Omnibus: | 2.677 | Durbin-Watson: | 1.322 |
| Prob(Omnibus): | 0.262 | Jarque-Bera (JB): | 1.071 |
| Skew: | 0.036 | Prob(JB): | 0.585 |
| Kurtosis: | 1.693 | Cond. No. | 241. |