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 |
| 0.087 | 0.772 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.665 |
| Model: | OLS | Adj. R-squared: | 0.613 |
| Method: | Least Squares | F-statistic: | 12.59 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.19e-05 |
| Time: | 04:30:30 | Log-Likelihood: | -100.52 |
| No. Observations: | 23 | AIC: | 209.0 |
| Df Residuals: | 19 | BIC: | 213.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -3.5916 | 67.987 | -0.053 | 0.958 | -145.890 138.707 |
| C(dose)[T.1] | 141.8213 | 97.256 | 1.458 | 0.161 | -61.739 345.381 |
| expression | 10.7681 | 12.615 | 0.854 | 0.404 | -15.636 37.172 |
| expression:C(dose)[T.1] | -16.3902 | 17.893 | -0.916 | 0.371 | -53.841 21.060 |
| Omnibus: | 0.140 | Durbin-Watson: | 1.830 |
| Prob(Omnibus): | 0.932 | Jarque-Bera (JB): | 0.291 |
| Skew: | 0.154 | Prob(JB): | 0.865 |
| Kurtosis: | 2.543 | Cond. No. | 161. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 18.62 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.71e-05 |
| Time: | 04:30:30 | Log-Likelihood: | -101.01 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 40.1402 | 48.209 | 0.833 | 0.415 | -60.422 140.703 |
| C(dose)[T.1] | 53.1010 | 8.788 | 6.043 | 0.000 | 34.770 71.432 |
| expression | 2.6209 | 8.910 | 0.294 | 0.772 | -15.966 21.207 |
| Omnibus: | 0.568 | Durbin-Watson: | 1.886 |
| Prob(Omnibus): | 0.753 | Jarque-Bera (JB): | 0.618 |
| Skew: | 0.092 | Prob(JB): | 0.734 |
| Kurtosis: | 2.218 | Cond. No. | 62.2 |
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: | 04:30:30 | 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: | 0.2683 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.610 |
| Time: | 04:30:30 | Log-Likelihood: | -112.96 |
| 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 | 38.9226 | 79.085 | 0.492 | 0.628 | -125.543 203.389 |
| expression | 7.5395 | 14.556 | 0.518 | 0.610 | -22.731 37.810 |
| Omnibus: | 4.350 | Durbin-Watson: | 2.532 |
| Prob(Omnibus): | 0.114 | Jarque-Bera (JB): | 1.914 |
| Skew: | 0.370 | Prob(JB): | 0.384 |
| Kurtosis: | 1.795 | Cond. No. | 61.9 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.099 | 0.315 | 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: | 0.368 |
| Method: | Least Squares | F-statistic: | 3.720 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0456 |
| Time: | 04:30:30 | Log-Likelihood: | -70.047 |
| 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 | -191.9910 | 260.798 | -0.736 | 0.477 | -766.004 382.023 |
| C(dose)[T.1] | 199.2076 | 348.789 | 0.571 | 0.579 | -568.471 966.887 |
| expression | 41.6418 | 41.823 | 0.996 | 0.341 | -50.410 133.694 |
| expression:C(dose)[T.1] | -24.2740 | 55.659 | -0.436 | 0.671 | -146.779 98.231 |
| Omnibus: | 1.579 | Durbin-Watson: | 0.588 |
| Prob(Omnibus): | 0.454 | Jarque-Bera (JB): | 1.268 |
| Skew: | -0.580 | Prob(JB): | 0.530 |
| Kurtosis: | 2.173 | Cond. No. | 394. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.495 |
| Model: | OLS | Adj. R-squared: | 0.411 |
| Method: | Least Squares | F-statistic: | 5.882 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0166 |
| Time: | 04:30:30 | Log-Likelihood: | -70.176 |
| No. Observations: | 15 | AIC: | 146.4 |
| Df Residuals: | 12 | BIC: | 148.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -106.6077 | 166.381 | -0.641 | 0.534 | -469.120 255.904 |
| C(dose)[T.1] | 47.2493 | 15.179 | 3.113 | 0.009 | 14.177 80.322 |
| expression | 27.9361 | 26.649 | 1.048 | 0.315 | -30.127 85.999 |
| Omnibus: | 1.424 | Durbin-Watson: | 0.651 |
| Prob(Omnibus): | 0.491 | Jarque-Bera (JB): | 1.091 |
| Skew: | -0.452 | Prob(JB): | 0.580 |
| Kurtosis: | 2.037 | Cond. No. | 143. |
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: | 04:30:30 | 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: | 0.087 |
| Model: | OLS | Adj. R-squared: | 0.017 |
| Method: | Least Squares | F-statistic: | 1.243 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.285 |
| Time: | 04:30:30 | Log-Likelihood: | -74.615 |
| No. Observations: | 15 | AIC: | 153.2 |
| Df Residuals: | 13 | BIC: | 154.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -145.0208 | 214.318 | -0.677 | 0.510 | -608.027 317.985 |
| expression | 38.0866 | 34.163 | 1.115 | 0.285 | -35.718 111.891 |
| Omnibus: | 1.027 | Durbin-Watson: | 1.438 |
| Prob(Omnibus): | 0.599 | Jarque-Bera (JB): | 0.897 |
| Skew: | 0.413 | Prob(JB): | 0.639 |
| Kurtosis: | 2.133 | Cond. No. | 142. |