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 |
| 0.536 | 0.473 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.604 |
| Method: | Least Squares | F-statistic: | 12.20 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000111 |
| Time: | 05:01:44 | Log-Likelihood: | -100.76 |
| No. Observations: | 23 | AIC: | 209.5 |
| Df Residuals: | 19 | BIC: | 214.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 3.7304 | 88.190 | 0.042 | 0.967 | -180.853 188.314 |
| C(dose)[T.1] | 68.3431 | 120.760 | 0.566 | 0.578 | -184.410 321.096 |
| expression | 6.4379 | 11.220 | 0.574 | 0.573 | -17.047 29.923 |
| expression:C(dose)[T.1] | -1.2602 | 16.432 | -0.077 | 0.940 | -35.654 33.133 |
| Omnibus: | 1.119 | Durbin-Watson: | 1.701 |
| Prob(Omnibus): | 0.572 | Jarque-Bera (JB): | 0.832 |
| Skew: | 0.088 | Prob(JB): | 0.660 |
| Kurtosis: | 2.085 | Cond. No. | 262. |
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: | 19.26 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.18e-05 |
| Time: | 05:01:44 | 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 | 8.3371 | 62.941 | 0.132 | 0.896 | -122.956 139.630 |
| C(dose)[T.1] | 59.1285 | 11.725 | 5.043 | 0.000 | 34.670 83.587 |
| expression | 5.8504 | 7.991 | 0.732 | 0.473 | -10.819 22.520 |
| Omnibus: | 1.161 | Durbin-Watson: | 1.696 |
| Prob(Omnibus): | 0.560 | Jarque-Bera (JB): | 0.848 |
| Skew: | 0.095 | Prob(JB): | 0.654 |
| Kurtosis: | 2.079 | Cond. No. | 111. |
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: | 05:01:44 | 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: | 0.224 |
| Model: | OLS | Adj. R-squared: | 0.187 |
| Method: | Least Squares | F-statistic: | 6.049 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0227 |
| Time: | 05:01:44 | Log-Likelihood: | -110.19 |
| No. Observations: | 23 | AIC: | 224.4 |
| Df Residuals: | 21 | BIC: | 226.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 236.9151 | 64.232 | 3.688 | 0.001 | 103.338 370.492 |
| expression | -21.3373 | 8.676 | -2.459 | 0.023 | -39.380 -3.295 |
| Omnibus: | 0.223 | Durbin-Watson: | 2.485 |
| Prob(Omnibus): | 0.895 | Jarque-Bera (JB): | 0.363 |
| Skew: | 0.192 | Prob(JB): | 0.834 |
| Kurtosis: | 2.520 | Cond. No. | 76.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.667 | 0.430 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.493 |
| Model: | OLS | Adj. R-squared: | 0.355 |
| Method: | Least Squares | F-statistic: | 3.568 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0507 |
| Time: | 05:01:44 | Log-Likelihood: | -70.203 |
| No. Observations: | 15 | AIC: | 148.4 |
| Df Residuals: | 11 | BIC: | 151.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 263.0937 | 216.773 | 1.214 | 0.250 | -214.020 740.208 |
| C(dose)[T.1] | -96.7855 | 252.645 | -0.383 | 0.709 | -652.854 459.283 |
| expression | -30.2885 | 33.509 | -0.904 | 0.385 | -104.040 43.463 |
| expression:C(dose)[T.1] | 22.5899 | 39.043 | 0.579 | 0.575 | -63.343 108.523 |
| Omnibus: | 3.149 | Durbin-Watson: | 0.999 |
| Prob(Omnibus): | 0.207 | Jarque-Bera (JB): | 2.097 |
| Skew: | -0.905 | Prob(JB): | 0.350 |
| Kurtosis: | 2.715 | Cond. No. | 314. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.478 |
| Model: | OLS | Adj. R-squared: | 0.391 |
| Method: | Least Squares | F-statistic: | 5.490 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0203 |
| Time: | 05:01:44 | Log-Likelihood: | -70.427 |
| No. Observations: | 15 | AIC: | 146.9 |
| Df Residuals: | 12 | BIC: | 149.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 155.6015 | 108.553 | 1.433 | 0.177 | -80.915 392.118 |
| C(dose)[T.1] | 49.1079 | 15.320 | 3.205 | 0.008 | 15.728 82.488 |
| expression | -13.6489 | 16.714 | -0.817 | 0.430 | -50.066 22.768 |
| Omnibus: | 2.730 | Durbin-Watson: | 0.858 |
| Prob(Omnibus): | 0.255 | Jarque-Bera (JB): | 1.870 |
| Skew: | -0.845 | Prob(JB): | 0.393 |
| Kurtosis: | 2.631 | Cond. No. | 94.4 |
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: | 05:01:44 | 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: | 0.031 |
| Model: | OLS | Adj. R-squared: | -0.044 |
| Method: | Least Squares | F-statistic: | 0.4111 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.533 |
| Time: | 05:01:44 | Log-Likelihood: | -75.067 |
| No. Observations: | 15 | AIC: | 154.1 |
| Df Residuals: | 13 | BIC: | 155.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 184.2413 | 141.613 | 1.301 | 0.216 | -121.694 490.177 |
| expression | -14.0282 | 21.878 | -0.641 | 0.533 | -61.293 33.237 |
| Omnibus: | 0.575 | Durbin-Watson: | 1.694 |
| Prob(Omnibus): | 0.750 | Jarque-Bera (JB): | 0.585 |
| Skew: | 0.132 | Prob(JB): | 0.747 |
| Kurtosis: | 2.070 | Cond. No. | 93.8 |