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 |
| 0.005 | 0.945 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.594 |
| Method: | Least Squares | F-statistic: | 11.72 |
| Date: | Thu, 30 Jan 2025 | Prob (F-statistic): | 0.000142 |
| Time: | 01:25:39 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 210.1 |
| Df Residuals: | 19 | BIC: | 214.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 66.4241 | 156.362 | 0.425 | 0.676 | -260.845 393.693 |
| C(dose)[T.1] | 44.0443 | 192.285 | 0.229 | 0.821 | -358.414 446.502 |
| expression | -1.5981 | 20.440 | -0.078 | 0.938 | -44.379 41.183 |
| expression:C(dose)[T.1] | 1.1868 | 25.787 | 0.046 | 0.964 | -52.787 55.161 |
| Omnibus: | 0.314 | Durbin-Watson: | 1.907 |
| Prob(Omnibus): | 0.855 | Jarque-Bera (JB): | 0.481 |
| Skew: | 0.070 | Prob(JB): | 0.786 |
| Kurtosis: | 2.305 | Cond. No. | 442. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.50 |
| Date: | Thu, 30 Jan 2025 | Prob (F-statistic): | 2.83e-05 |
| Time: | 01:25:39 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 60.7247 | 93.050 | 0.653 | 0.521 | -133.373 254.823 |
| C(dose)[T.1] | 52.8786 | 10.935 | 4.836 | 0.000 | 30.069 75.688 |
| expression | -0.8525 | 12.147 | -0.070 | 0.945 | -26.192 24.487 |
| Omnibus: | 0.366 | Durbin-Watson: | 1.906 |
| Prob(Omnibus): | 0.833 | Jarque-Bera (JB): | 0.512 |
| Skew: | 0.075 | Prob(JB): | 0.774 |
| Kurtosis: | 2.285 | Cond. No. | 161. |
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, 30 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 01:25:39 | 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: | 0.239 |
| Model: | OLS | Adj. R-squared: | 0.203 |
| Method: | Least Squares | F-statistic: | 6.592 |
| Date: | Thu, 30 Jan 2025 | Prob (F-statistic): | 0.0179 |
| Time: | 01:25:39 | Log-Likelihood: | -109.97 |
| No. Observations: | 23 | AIC: | 223.9 |
| Df Residuals: | 21 | BIC: | 226.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 345.2456 | 103.614 | 3.332 | 0.003 | 129.768 560.723 |
| expression | -35.9475 | 14.001 | -2.567 | 0.018 | -65.065 -6.830 |
| Omnibus: | 1.901 | Durbin-Watson: | 2.446 |
| Prob(Omnibus): | 0.387 | Jarque-Bera (JB): | 1.624 |
| Skew: | 0.539 | Prob(JB): | 0.444 |
| Kurtosis: | 2.271 | Cond. No. | 124. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.733 | 0.409 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.504 |
| Model: | OLS | Adj. R-squared: | 0.369 |
| Method: | Least Squares | F-statistic: | 3.726 |
| Date: | Thu, 30 Jan 2025 | Prob (F-statistic): | 0.0454 |
| Time: | 01:25:39 | Log-Likelihood: | -70.041 |
| No. Observations: | 15 | AIC: | 148.1 |
| Df Residuals: | 11 | BIC: | 150.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 357.5131 | 281.145 | 1.272 | 0.230 | -261.284 976.310 |
| C(dose)[T.1] | -178.7619 | 321.664 | -0.556 | 0.590 | -886.740 529.217 |
| expression | -39.2849 | 38.043 | -1.033 | 0.324 | -123.017 44.447 |
| expression:C(dose)[T.1] | 31.1682 | 43.154 | 0.722 | 0.485 | -63.813 126.149 |
| Omnibus: | 2.010 | Durbin-Watson: | 1.220 |
| Prob(Omnibus): | 0.366 | Jarque-Bera (JB): | 0.999 |
| Skew: | -0.632 | Prob(JB): | 0.607 |
| Kurtosis: | 2.981 | Cond. No. | 478. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.480 |
| Model: | OLS | Adj. R-squared: | 0.394 |
| Method: | Least Squares | F-statistic: | 5.549 |
| Date: | Thu, 30 Jan 2025 | Prob (F-statistic): | 0.0197 |
| Time: | 01:25:39 | Log-Likelihood: | -70.389 |
| No. Observations: | 15 | AIC: | 146.8 |
| Df Residuals: | 12 | BIC: | 148.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 178.6487 | 130.416 | 1.370 | 0.196 | -105.502 462.800 |
| C(dose)[T.1] | 53.2636 | 16.002 | 3.329 | 0.006 | 18.399 88.129 |
| expression | -15.0621 | 17.597 | -0.856 | 0.409 | -53.402 23.278 |
| Omnibus: | 2.592 | Durbin-Watson: | 1.158 |
| Prob(Omnibus): | 0.274 | Jarque-Bera (JB): | 1.159 |
| Skew: | -0.673 | Prob(JB): | 0.560 |
| Kurtosis: | 3.209 | Cond. No. | 132. |
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, 30 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 01:25:39 | 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: | 0.01084 |
| Date: | Thu, 30 Jan 2025 | Prob (F-statistic): | 0.919 |
| Time: | 01:25:39 | Log-Likelihood: | -75.294 |
| 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 | 76.1209 | 168.852 | 0.451 | 0.660 | -288.662 440.904 |
| expression | 2.3307 | 22.389 | 0.104 | 0.919 | -46.037 50.699 |
| Omnibus: | 0.526 | Durbin-Watson: | 1.580 |
| Prob(Omnibus): | 0.769 | Jarque-Bera (JB): | 0.551 |
| Skew: | 0.024 | Prob(JB): | 0.759 |
| Kurtosis: | 2.063 | Cond. No. | 128. |