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 |
| 7.775 | 0.011 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.758 |
| Model: | OLS | Adj. R-squared: | 0.720 |
| Method: | Least Squares | F-statistic: | 19.85 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.45e-06 |
| Time: | 04:48:23 | Log-Likelihood: | -96.781 |
| No. Observations: | 23 | AIC: | 201.6 |
| Df Residuals: | 19 | BIC: | 206.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -13.9333 | 42.432 | -0.328 | 0.746 | -102.744 74.877 |
| C(dose)[T.1] | -4.7751 | 67.136 | -0.071 | 0.944 | -145.292 135.742 |
| expression | 15.5530 | 9.613 | 1.618 | 0.122 | -4.567 35.673 |
| expression:C(dose)[T.1] | 14.3955 | 15.591 | 0.923 | 0.367 | -18.237 47.028 |
| Omnibus: | 0.109 | Durbin-Watson: | 1.745 |
| Prob(Omnibus): | 0.947 | Jarque-Bera (JB): | 0.328 |
| Skew: | 0.063 | Prob(JB): | 0.849 |
| Kurtosis: | 2.428 | Cond. No. | 101. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.747 |
| Model: | OLS | Adj. R-squared: | 0.722 |
| Method: | Least Squares | F-statistic: | 29.57 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.06e-06 |
| Time: | 04:48:23 | Log-Likelihood: | -97.286 |
| No. Observations: | 23 | AIC: | 200.6 |
| Df Residuals: | 20 | BIC: | 204.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -37.9097 | 33.434 | -1.134 | 0.270 | -107.652 31.833 |
| C(dose)[T.1] | 56.8181 | 7.546 | 7.530 | 0.000 | 41.078 72.558 |
| expression | 21.0255 | 7.540 | 2.788 | 0.011 | 5.297 36.754 |
| Omnibus: | 0.134 | Durbin-Watson: | 1.815 |
| Prob(Omnibus): | 0.935 | Jarque-Bera (JB): | 0.344 |
| Skew: | 0.096 | Prob(JB): | 0.842 |
| Kurtosis: | 2.432 | Cond. No. | 41.2 |
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: | 04:48:23 | 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: | 0.031 |
| Model: | OLS | Adj. R-squared: | -0.015 |
| Method: | Least Squares | F-statistic: | 0.6700 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.422 |
| Time: | 04:48:23 | Log-Likelihood: | -112.74 |
| No. Observations: | 23 | AIC: | 229.5 |
| Df Residuals: | 21 | BIC: | 231.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 29.6735 | 61.550 | 0.482 | 0.635 | -98.327 157.674 |
| expression | 11.6325 | 14.212 | 0.819 | 0.422 | -17.922 41.187 |
| Omnibus: | 4.626 | Durbin-Watson: | 2.609 |
| Prob(Omnibus): | 0.099 | Jarque-Bera (JB): | 1.754 |
| Skew: | 0.259 | Prob(JB): | 0.416 |
| Kurtosis: | 1.750 | Cond. No. | 39.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.187 | 0.673 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.557 |
| Model: | OLS | Adj. R-squared: | 0.436 |
| Method: | Least Squares | F-statistic: | 4.610 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0253 |
| Time: | 04:48:23 | Log-Likelihood: | -69.194 |
| No. Observations: | 15 | AIC: | 146.4 |
| Df Residuals: | 11 | BIC: | 149.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 184.9410 | 78.930 | 2.343 | 0.039 | 11.217 358.664 |
| C(dose)[T.1] | -110.1052 | 101.995 | -1.080 | 0.303 | -334.595 114.384 |
| expression | -21.8795 | 14.559 | -1.503 | 0.161 | -53.923 10.164 |
| expression:C(dose)[T.1] | 29.8194 | 18.946 | 1.574 | 0.144 | -11.880 71.519 |
| Omnibus: | 3.242 | Durbin-Watson: | 1.277 |
| Prob(Omnibus): | 0.198 | Jarque-Bera (JB): | 1.327 |
| Skew: | -0.682 | Prob(JB): | 0.515 |
| Kurtosis: | 3.514 | Cond. No. | 107. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.457 |
| Model: | OLS | Adj. R-squared: | 0.367 |
| Method: | Least Squares | F-statistic: | 5.055 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0256 |
| Time: | 04:48:23 | Log-Likelihood: | -70.717 |
| No. Observations: | 15 | AIC: | 147.4 |
| Df Residuals: | 12 | BIC: | 149.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 90.3691 | 54.241 | 1.666 | 0.122 | -27.812 208.550 |
| C(dose)[T.1] | 48.7362 | 15.655 | 3.113 | 0.009 | 14.628 82.844 |
| expression | -4.2713 | 9.873 | -0.433 | 0.673 | -25.783 17.241 |
| Omnibus: | 2.509 | Durbin-Watson: | 0.847 |
| Prob(Omnibus): | 0.285 | Jarque-Bera (JB): | 1.839 |
| Skew: | -0.816 | Prob(JB): | 0.399 |
| Kurtosis: | 2.470 | Cond. No. | 38.9 |
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: | 04:48:23 | 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: | 0.019 |
| Model: | OLS | Adj. R-squared: | -0.057 |
| Method: | Least Squares | F-statistic: | 0.2498 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.626 |
| Time: | 04:48:23 | Log-Likelihood: | -75.157 |
| No. Observations: | 15 | AIC: | 154.3 |
| Df Residuals: | 13 | BIC: | 155.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 127.4608 | 68.355 | 1.865 | 0.085 | -20.211 275.133 |
| expression | -6.3601 | 12.724 | -0.500 | 0.626 | -33.849 21.129 |
| Omnibus: | 2.351 | Durbin-Watson: | 1.686 |
| Prob(Omnibus): | 0.309 | Jarque-Bera (JB): | 1.114 |
| Skew: | 0.244 | Prob(JB): | 0.573 |
| Kurtosis: | 1.757 | Cond. No. | 37.7 |