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 |
| 0.106 | 0.748 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.596 |
| Method: | Least Squares | F-statistic: | 11.81 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000136 |
| Time: | 03:32:49 | Log-Likelihood: | -101.00 |
| No. Observations: | 23 | AIC: | 210.0 |
| Df Residuals: | 19 | BIC: | 214.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 65.9169 | 41.337 | 1.595 | 0.127 | -20.602 152.435 |
| C(dose)[T.1] | 50.1536 | 73.373 | 0.684 | 0.503 | -103.418 203.726 |
| expression | -2.0569 | 7.180 | -0.286 | 0.778 | -17.084 12.970 |
| expression:C(dose)[T.1] | 0.4623 | 13.366 | 0.035 | 0.973 | -27.512 28.437 |
| Omnibus: | 0.534 | Durbin-Watson: | 1.878 |
| Prob(Omnibus): | 0.766 | Jarque-Bera (JB): | 0.604 |
| Skew: | 0.102 | Prob(JB): | 0.739 |
| Kurtosis: | 2.233 | Cond. No. | 112. |
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: | 18.65 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.69e-05 |
| Time: | 03:32:49 | Log-Likelihood: | -101.00 |
| 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 | 65.1575 | 34.140 | 1.909 | 0.071 | -6.057 136.372 |
| C(dose)[T.1] | 52.6716 | 8.982 | 5.864 | 0.000 | 33.936 71.408 |
| expression | -1.9235 | 5.903 | -0.326 | 0.748 | -14.236 10.389 |
| Omnibus: | 0.522 | Durbin-Watson: | 1.878 |
| Prob(Omnibus): | 0.770 | Jarque-Bera (JB): | 0.600 |
| Skew: | 0.109 | Prob(JB): | 0.741 |
| Kurtosis: | 2.240 | Cond. No. | 45.2 |
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: | 03:32:49 | 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: | 0.051 |
| Model: | OLS | Adj. R-squared: | 0.005 |
| Method: | Least Squares | F-statistic: | 1.121 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.302 |
| Time: | 03:32:49 | Log-Likelihood: | -112.51 |
| No. Observations: | 23 | AIC: | 229.0 |
| Df Residuals: | 21 | BIC: | 231.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 133.8449 | 51.607 | 2.594 | 0.017 | 26.521 241.169 |
| expression | -9.7935 | 9.251 | -1.059 | 0.302 | -29.031 9.444 |
| Omnibus: | 1.200 | Durbin-Watson: | 2.526 |
| Prob(Omnibus): | 0.549 | Jarque-Bera (JB): | 0.997 |
| Skew: | 0.286 | Prob(JB): | 0.607 |
| Kurtosis: | 2.155 | Cond. No. | 42.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.009 | 0.924 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.460 |
| Model: | OLS | Adj. R-squared: | 0.313 |
| Method: | Least Squares | F-statistic: | 3.125 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0699 |
| Time: | 03:32:49 | Log-Likelihood: | -70.677 |
| No. Observations: | 15 | AIC: | 149.4 |
| Df Residuals: | 11 | BIC: | 152.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 2.3930 | 190.086 | 0.013 | 0.990 | -415.984 420.770 |
| C(dose)[T.1] | 158.5623 | 231.291 | 0.686 | 0.507 | -350.506 667.631 |
| expression | 9.5071 | 27.733 | 0.343 | 0.738 | -51.533 70.547 |
| expression:C(dose)[T.1] | -15.6673 | 33.197 | -0.472 | 0.646 | -88.733 57.398 |
| Omnibus: | 4.828 | Durbin-Watson: | 0.854 |
| Prob(Omnibus): | 0.089 | Jarque-Bera (JB): | 2.752 |
| Skew: | -1.041 | Prob(JB): | 0.253 |
| Kurtosis: | 3.264 | Cond. No. | 298. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.357 |
| Method: | Least Squares | F-statistic: | 4.893 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0279 |
| Time: | 03:32:49 | Log-Likelihood: | -70.827 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 77.1937 | 101.488 | 0.761 | 0.462 | -143.931 298.318 |
| C(dose)[T.1] | 49.7040 | 16.584 | 2.997 | 0.011 | 13.571 85.837 |
| expression | -1.4275 | 14.741 | -0.097 | 0.924 | -33.544 30.689 |
| Omnibus: | 2.867 | Durbin-Watson: | 0.820 |
| Prob(Omnibus): | 0.238 | Jarque-Bera (JB): | 1.886 |
| Skew: | -0.857 | Prob(JB): | 0.389 |
| Kurtosis: | 2.717 | Cond. No. | 93.4 |
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: | 03:32:49 | 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: | 0.037 |
| Model: | OLS | Adj. R-squared: | -0.037 |
| Method: | Least Squares | F-statistic: | 0.4979 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.493 |
| Time: | 03:32:49 | Log-Likelihood: | -75.018 |
| No. Observations: | 15 | AIC: | 154.0 |
| Df Residuals: | 13 | BIC: | 155.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 5.5305 | 125.308 | 0.044 | 0.965 | -265.181 276.242 |
| expression | 12.5365 | 17.767 | 0.706 | 0.493 | -25.847 50.920 |
| Omnibus: | 0.598 | Durbin-Watson: | 1.524 |
| Prob(Omnibus): | 0.742 | Jarque-Bera (JB): | 0.591 |
| Skew: | 0.120 | Prob(JB): | 0.744 |
| Kurtosis: | 2.058 | Cond. No. | 90.4 |