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 |
| 0.074 | 0.788 | 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: | 0.603 |
| Method: | Least Squares | F-statistic: | 12.12 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000116 |
| Time: | 03:32:50 | 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 | -16.4453 | 128.386 | -0.128 | 0.899 | -285.160 252.269 |
| C(dose)[T.1] | 195.8365 | 241.471 | 0.811 | 0.427 | -309.568 701.242 |
| expression | 10.0878 | 18.310 | 0.551 | 0.588 | -28.235 48.410 |
| expression:C(dose)[T.1] | -19.9062 | 33.401 | -0.596 | 0.558 | -89.814 50.002 |
| Omnibus: | 0.260 | Durbin-Watson: | 1.941 |
| Prob(Omnibus): | 0.878 | Jarque-Bera (JB): | 0.384 |
| Skew: | 0.211 | Prob(JB): | 0.825 |
| Kurtosis: | 2.528 | Cond. No. | 475. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 18.60 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.73e-05 |
| Time: | 03:32:51 | Log-Likelihood: | -101.02 |
| 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 | 25.4513 | 105.684 | 0.241 | 0.812 | -195.001 245.903 |
| C(dose)[T.1] | 52.0496 | 9.947 | 5.233 | 0.000 | 31.301 72.798 |
| expression | 4.1059 | 15.065 | 0.273 | 0.788 | -27.318 35.530 |
| Omnibus: | 0.250 | Durbin-Watson: | 1.894 |
| Prob(Omnibus): | 0.882 | Jarque-Bera (JB): | 0.440 |
| Skew: | 0.048 | Prob(JB): | 0.803 |
| Kurtosis: | 2.329 | Cond. No. | 177. |
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: | 03:32:51 | 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: | 0.172 |
| Model: | OLS | Adj. R-squared: | 0.132 |
| Method: | Least Squares | F-statistic: | 4.352 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0493 |
| Time: | 03:32:51 | Log-Likelihood: | -110.94 |
| No. Observations: | 23 | AIC: | 225.9 |
| Df Residuals: | 21 | BIC: | 228.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -217.4725 | 142.610 | -1.525 | 0.142 | -514.046 79.101 |
| expression | 41.5427 | 19.914 | 2.086 | 0.049 | 0.130 82.955 |
| Omnibus: | 6.136 | Durbin-Watson: | 2.001 |
| Prob(Omnibus): | 0.047 | Jarque-Bera (JB): | 1.982 |
| Skew: | 0.269 | Prob(JB): | 0.371 |
| Kurtosis: | 1.667 | Cond. No. | 159. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 5.584 | 0.036 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.631 |
| Model: | OLS | Adj. R-squared: | 0.530 |
| Method: | Least Squares | F-statistic: | 6.268 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00973 |
| Time: | 03:32:51 | Log-Likelihood: | -67.824 |
| No. Observations: | 15 | AIC: | 143.6 |
| Df Residuals: | 11 | BIC: | 146.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -202.9031 | 170.783 | -1.188 | 0.260 | -578.793 172.987 |
| C(dose)[T.1] | 133.7449 | 202.709 | 0.660 | 0.523 | -312.416 579.905 |
| expression | 39.2115 | 24.731 | 1.586 | 0.141 | -15.221 93.644 |
| expression:C(dose)[T.1] | -13.3544 | 28.999 | -0.461 | 0.654 | -77.182 50.473 |
| Omnibus: | 13.424 | Durbin-Watson: | 0.935 |
| Prob(Omnibus): | 0.001 | Jarque-Bera (JB): | 9.885 |
| Skew: | -1.524 | Prob(JB): | 0.00714 |
| Kurtosis: | 5.555 | Cond. No. | 321. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.624 |
| Model: | OLS | Adj. R-squared: | 0.561 |
| Method: | Least Squares | F-statistic: | 9.950 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00283 |
| Time: | 03:32:51 | Log-Likelihood: | -67.968 |
| No. Observations: | 15 | AIC: | 141.9 |
| Df Residuals: | 12 | BIC: | 144.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -135.9438 | 86.589 | -1.570 | 0.142 | -324.605 52.717 |
| C(dose)[T.1] | 40.6178 | 13.500 | 3.009 | 0.011 | 11.204 70.032 |
| expression | 29.4991 | 12.484 | 2.363 | 0.036 | 2.299 56.699 |
| Omnibus: | 14.424 | Durbin-Watson: | 0.911 |
| Prob(Omnibus): | 0.001 | Jarque-Bera (JB): | 10.996 |
| Skew: | -1.627 | Prob(JB): | 0.00410 |
| Kurtosis: | 5.646 | Cond. No. | 96.6 |
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: | 03:32:51 | 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: | 0.340 |
| Model: | OLS | Adj. R-squared: | 0.289 |
| Method: | Least Squares | F-statistic: | 6.698 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0225 |
| Time: | 03:32:51 | Log-Likelihood: | -72.183 |
| No. Observations: | 15 | AIC: | 148.4 |
| Df Residuals: | 13 | BIC: | 149.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -185.4859 | 108.179 | -1.715 | 0.110 | -419.193 48.221 |
| expression | 39.6001 | 15.301 | 2.588 | 0.023 | 6.543 72.657 |
| Omnibus: | 1.465 | Durbin-Watson: | 2.138 |
| Prob(Omnibus): | 0.481 | Jarque-Bera (JB): | 0.835 |
| Skew: | -0.071 | Prob(JB): | 0.659 |
| Kurtosis: | 1.853 | Cond. No. | 94.5 |