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 |
| 0.526 | 0.477 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.663 |
| Model: | OLS | Adj. R-squared: | 0.609 |
| Method: | Least Squares | F-statistic: | 12.44 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.91e-05 |
| Time: | 04:40:56 | Log-Likelihood: | -100.61 |
| No. Observations: | 23 | AIC: | 209.2 |
| Df Residuals: | 19 | BIC: | 213.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 100.2557 | 103.468 | 0.969 | 0.345 | -116.304 316.816 |
| C(dose)[T.1] | 178.6998 | 250.617 | 0.713 | 0.484 | -345.847 703.247 |
| expression | -6.4951 | 14.569 | -0.446 | 0.661 | -36.988 23.998 |
| expression:C(dose)[T.1] | -18.2289 | 35.992 | -0.506 | 0.618 | -93.561 57.103 |
| Omnibus: | 0.181 | Durbin-Watson: | 1.863 |
| Prob(Omnibus): | 0.914 | Jarque-Bera (JB): | 0.391 |
| Skew: | -0.042 | Prob(JB): | 0.822 |
| Kurtosis: | 2.367 | Cond. No. | 467. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.624 |
| Method: | Least Squares | F-statistic: | 19.24 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.19e-05 |
| Time: | 04:40:56 | Log-Likelihood: | -100.76 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 20 | BIC: | 210.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 121.4310 | 92.868 | 1.308 | 0.206 | -72.289 315.151 |
| C(dose)[T.1] | 51.8523 | 8.895 | 5.829 | 0.000 | 33.297 70.408 |
| expression | -9.4820 | 13.072 | -0.725 | 0.477 | -36.750 17.786 |
| Omnibus: | 0.360 | Durbin-Watson: | 1.809 |
| Prob(Omnibus): | 0.835 | Jarque-Bera (JB): | 0.507 |
| Skew: | 0.059 | Prob(JB): | 0.776 |
| Kurtosis: | 2.282 | Cond. No. | 154. |
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: | 04:40:56 | 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: | 0.077 |
| Model: | OLS | Adj. R-squared: | 0.033 |
| Method: | Least Squares | F-statistic: | 1.755 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.200 |
| Time: | 04:40:56 | Log-Likelihood: | -112.18 |
| No. Observations: | 23 | AIC: | 228.4 |
| Df Residuals: | 21 | BIC: | 230.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 269.2332 | 143.233 | 1.880 | 0.074 | -28.636 567.103 |
| expression | -27.0172 | 20.395 | -1.325 | 0.200 | -69.432 15.397 |
| Omnibus: | 4.608 | Durbin-Watson: | 2.435 |
| Prob(Omnibus): | 0.100 | Jarque-Bera (JB): | 1.631 |
| Skew: | 0.165 | Prob(JB): | 0.442 |
| Kurtosis: | 1.738 | Cond. No. | 148. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.175 | 0.300 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.516 |
| Model: | OLS | Adj. R-squared: | 0.384 |
| Method: | Least Squares | F-statistic: | 3.911 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0400 |
| Time: | 04:40:56 | Log-Likelihood: | -69.856 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 11 | BIC: | 150.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 298.3615 | 189.215 | 1.577 | 0.143 | -118.099 714.822 |
| C(dose)[T.1] | -136.8159 | 302.421 | -0.452 | 0.660 | -802.439 528.807 |
| expression | -31.2264 | 25.540 | -1.223 | 0.247 | -87.440 24.987 |
| expression:C(dose)[T.1] | 25.4589 | 39.598 | 0.643 | 0.533 | -61.695 112.612 |
| Omnibus: | 1.988 | Durbin-Watson: | 1.132 |
| Prob(Omnibus): | 0.370 | Jarque-Bera (JB): | 1.433 |
| Skew: | -0.718 | Prob(JB): | 0.488 |
| Kurtosis: | 2.521 | Cond. No. | 391. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.498 |
| Model: | OLS | Adj. R-squared: | 0.414 |
| Method: | Least Squares | F-statistic: | 5.951 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0160 |
| Time: | 04:40:56 | Log-Likelihood: | -70.132 |
| No. Observations: | 15 | AIC: | 146.3 |
| Df Residuals: | 12 | BIC: | 148.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 220.0341 | 141.195 | 1.558 | 0.145 | -87.603 527.671 |
| C(dose)[T.1] | 57.3076 | 16.781 | 3.415 | 0.005 | 20.744 93.871 |
| expression | -20.6351 | 19.034 | -1.084 | 0.300 | -62.108 20.837 |
| Omnibus: | 2.062 | Durbin-Watson: | 1.006 |
| Prob(Omnibus): | 0.357 | Jarque-Bera (JB): | 1.573 |
| Skew: | -0.659 | Prob(JB): | 0.456 |
| Kurtosis: | 2.119 | Cond. No. | 147. |
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: | 04:40:56 | 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: | 0.010 |
| Model: | OLS | Adj. R-squared: | -0.066 |
| Method: | Least Squares | F-statistic: | 0.1318 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.722 |
| Time: | 04:40:56 | Log-Likelihood: | -75.224 |
| No. Observations: | 15 | AIC: | 154.4 |
| Df Residuals: | 13 | BIC: | 155.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 30.1985 | 175.104 | 0.172 | 0.866 | -348.091 408.488 |
| expression | 8.3455 | 22.986 | 0.363 | 0.722 | -41.313 58.004 |
| Omnibus: | 0.110 | Durbin-Watson: | 1.572 |
| Prob(Omnibus): | 0.946 | Jarque-Bera (JB): | 0.313 |
| Skew: | -0.127 | Prob(JB): | 0.855 |
| Kurtosis: | 2.339 | Cond. No. | 134. |