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 |
| 0.299 | 0.591 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.657 |
| Model: | OLS | Adj. R-squared: | 0.602 |
| Method: | Least Squares | F-statistic: | 12.11 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000117 |
| Time: | 04:25:44 | Log-Likelihood: | -100.81 |
| No. Observations: | 23 | AIC: | 209.6 |
| Df Residuals: | 19 | BIC: | 214.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 66.6154 | 47.504 | 1.402 | 0.177 | -32.812 166.043 |
| C(dose)[T.1] | 84.6376 | 87.947 | 0.962 | 0.348 | -99.437 268.712 |
| expression | -2.0411 | 7.749 | -0.263 | 0.795 | -18.260 14.178 |
| expression:C(dose)[T.1] | -5.4348 | 14.802 | -0.367 | 0.718 | -36.416 25.546 |
| Omnibus: | 0.010 | Durbin-Watson: | 1.811 |
| Prob(Omnibus): | 0.995 | Jarque-Bera (JB): | 0.199 |
| Skew: | 0.023 | Prob(JB): | 0.905 |
| Kurtosis: | 2.547 | Cond. No. | 144. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.654 |
| Model: | OLS | Adj. R-squared: | 0.620 |
| Method: | Least Squares | F-statistic: | 18.92 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.44e-05 |
| Time: | 04:25:44 | Log-Likelihood: | -100.89 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 20 | BIC: | 211.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 75.6694 | 39.715 | 1.905 | 0.071 | -7.174 158.513 |
| C(dose)[T.1] | 52.5172 | 8.833 | 5.945 | 0.000 | 34.091 70.943 |
| expression | -3.5305 | 6.458 | -0.547 | 0.591 | -17.001 9.940 |
| Omnibus: | 0.127 | Durbin-Watson: | 1.824 |
| Prob(Omnibus): | 0.938 | Jarque-Bera (JB): | 0.343 |
| Skew: | 0.078 | Prob(JB): | 0.843 |
| Kurtosis: | 2.423 | Cond. No. | 56.6 |
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: | 04:25:44 | 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: | 0.043 |
| Model: | OLS | Adj. R-squared: | -0.002 |
| Method: | Least Squares | F-statistic: | 0.9460 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.342 |
| Time: | 04:25:44 | Log-Likelihood: | -112.60 |
| No. Observations: | 23 | AIC: | 229.2 |
| Df Residuals: | 21 | BIC: | 231.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 139.6872 | 62.060 | 2.251 | 0.035 | 10.626 268.748 |
| expression | -10.0491 | 10.332 | -0.973 | 0.342 | -31.535 11.437 |
| Omnibus: | 4.462 | Durbin-Watson: | 2.292 |
| Prob(Omnibus): | 0.107 | Jarque-Bera (JB): | 1.727 |
| Skew: | 0.258 | Prob(JB): | 0.422 |
| Kurtosis: | 1.761 | Cond. No. | 54.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.072 | 0.793 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.500 |
| Model: | OLS | Adj. R-squared: | 0.364 |
| Method: | Least Squares | F-statistic: | 3.672 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0471 |
| Time: | 04:25:44 | Log-Likelihood: | -70.096 |
| No. Observations: | 15 | AIC: | 148.2 |
| Df Residuals: | 11 | BIC: | 151.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -62.2333 | 176.660 | -0.352 | 0.731 | -451.060 326.593 |
| C(dose)[T.1] | 272.4897 | 214.622 | 1.270 | 0.230 | -199.889 744.869 |
| expression | 24.7550 | 33.657 | 0.736 | 0.477 | -49.324 98.834 |
| expression:C(dose)[T.1] | -40.8009 | 39.569 | -1.031 | 0.325 | -127.891 46.289 |
| Omnibus: | 1.377 | Durbin-Watson: | 0.799 |
| Prob(Omnibus): | 0.502 | Jarque-Bera (JB): | 1.137 |
| Skew: | -0.569 | Prob(JB): | 0.566 |
| Kurtosis: | 2.275 | Cond. No. | 234. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.452 |
| Model: | OLS | Adj. R-squared: | 0.361 |
| Method: | Least Squares | F-statistic: | 4.950 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0271 |
| Time: | 04:25:44 | Log-Likelihood: | -70.788 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 92.3892 | 93.643 | 0.987 | 0.343 | -111.640 296.419 |
| C(dose)[T.1] | 52.0435 | 18.938 | 2.748 | 0.018 | 10.782 93.305 |
| expression | -4.7655 | 17.744 | -0.269 | 0.793 | -43.426 33.895 |
| Omnibus: | 2.340 | Durbin-Watson: | 0.859 |
| Prob(Omnibus): | 0.310 | Jarque-Bera (JB): | 1.719 |
| Skew: | -0.785 | Prob(JB): | 0.423 |
| Kurtosis: | 2.468 | Cond. No. | 69.7 |
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: | 04:25:44 | 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: | 0.107 |
| Model: | OLS | Adj. R-squared: | 0.039 |
| Method: | Least Squares | F-statistic: | 1.561 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.234 |
| Time: | 04:25:44 | Log-Likelihood: | -74.449 |
| No. Observations: | 15 | AIC: | 152.9 |
| Df Residuals: | 13 | BIC: | 154.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -31.5248 | 100.652 | -0.313 | 0.759 | -248.970 185.921 |
| expression | 22.5309 | 18.032 | 1.250 | 0.234 | -16.425 61.486 |
| Omnibus: | 0.445 | Durbin-Watson: | 1.433 |
| Prob(Omnibus): | 0.801 | Jarque-Bera (JB): | 0.517 |
| Skew: | -0.003 | Prob(JB): | 0.772 |
| Kurtosis: | 2.091 | Cond. No. | 60.4 |