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 |
| 0.089 | 0.768 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.685 |
| Model: | OLS | Adj. R-squared: | 0.635 |
| Method: | Least Squares | F-statistic: | 13.76 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.25e-05 |
| Time: | 04:15:17 | Log-Likelihood: | -99.825 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 19 | BIC: | 212.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 93.0212 | 63.715 | 1.460 | 0.161 | -40.335 226.378 |
| C(dose)[T.1] | -95.0719 | 104.042 | -0.914 | 0.372 | -312.834 122.691 |
| expression | -6.3883 | 10.442 | -0.612 | 0.548 | -28.244 15.467 |
| expression:C(dose)[T.1] | 25.1353 | 17.489 | 1.437 | 0.167 | -11.470 61.741 |
| Omnibus: | 0.024 | Durbin-Watson: | 1.665 |
| Prob(Omnibus): | 0.988 | Jarque-Bera (JB): | 0.232 |
| Skew: | 0.016 | Prob(JB): | 0.891 |
| Kurtosis: | 2.509 | Cond. No. | 183. |
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: | 18.62 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.71e-05 |
| Time: | 04:15:17 | Log-Likelihood: | -101.01 |
| 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 | 38.5841 | 52.580 | 0.734 | 0.472 | -71.097 148.265 |
| C(dose)[T.1] | 53.9275 | 8.970 | 6.012 | 0.000 | 35.216 72.639 |
| expression | 2.5716 | 8.597 | 0.299 | 0.768 | -15.361 20.504 |
| Omnibus: | 0.446 | Durbin-Watson: | 1.934 |
| Prob(Omnibus): | 0.800 | Jarque-Bera (JB): | 0.551 |
| Skew: | 0.038 | Prob(JB): | 0.759 |
| Kurtosis: | 2.245 | Cond. No. | 74.3 |
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: | 04:15:17 | 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: | 0.019 |
| Model: | OLS | Adj. R-squared: | -0.027 |
| Method: | Least Squares | F-statistic: | 0.4118 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.528 |
| Time: | 04:15:17 | Log-Likelihood: | -112.88 |
| No. Observations: | 23 | AIC: | 229.8 |
| Df Residuals: | 21 | BIC: | 232.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 132.2117 | 82.115 | 1.610 | 0.122 | -38.557 302.980 |
| expression | -8.7992 | 13.712 | -0.642 | 0.528 | -37.315 19.717 |
| Omnibus: | 2.293 | Durbin-Watson: | 2.488 |
| Prob(Omnibus): | 0.318 | Jarque-Bera (JB): | 1.527 |
| Skew: | 0.401 | Prob(JB): | 0.466 |
| Kurtosis: | 2.026 | Cond. No. | 70.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.059 | 0.812 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.459 |
| Model: | OLS | Adj. R-squared: | 0.312 |
| Method: | Least Squares | F-statistic: | 3.113 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0706 |
| Time: | 04:15:17 | Log-Likelihood: | -70.691 |
| 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 | 23.9674 | 97.489 | 0.246 | 0.810 | -190.605 238.540 |
| C(dose)[T.1] | 101.4401 | 135.193 | 0.750 | 0.469 | -196.118 398.998 |
| expression | 8.5104 | 18.947 | 0.449 | 0.662 | -33.192 50.213 |
| expression:C(dose)[T.1] | -10.1567 | 25.747 | -0.394 | 0.701 | -66.826 46.513 |
| Omnibus: | 2.109 | Durbin-Watson: | 0.854 |
| Prob(Omnibus): | 0.348 | Jarque-Bera (JB): | 1.604 |
| Skew: | -0.739 | Prob(JB): | 0.449 |
| Kurtosis: | 2.382 | Cond. No. | 123. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.451 |
| Model: | OLS | Adj. R-squared: | 0.360 |
| Method: | Least Squares | F-statistic: | 4.939 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0272 |
| Time: | 04:15:17 | Log-Likelihood: | -70.796 |
| 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 | 52.0561 | 64.202 | 0.811 | 0.433 | -87.828 191.940 |
| C(dose)[T.1] | 48.5108 | 15.952 | 3.041 | 0.010 | 13.755 83.267 |
| expression | 3.0102 | 12.370 | 0.243 | 0.812 | -23.941 29.961 |
| Omnibus: | 3.382 | Durbin-Watson: | 0.827 |
| Prob(Omnibus): | 0.184 | Jarque-Bera (JB): | 2.106 |
| Skew: | -0.915 | Prob(JB): | 0.349 |
| Kurtosis: | 2.866 | Cond. No. | 45.0 |
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: | 04:15:17 | 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: | 0.029 |
| Model: | OLS | Adj. R-squared: | -0.046 |
| Method: | Least Squares | F-statistic: | 0.3847 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.546 |
| Time: | 04:15:17 | Log-Likelihood: | -75.081 |
| No. Observations: | 15 | AIC: | 154.2 |
| Df Residuals: | 13 | BIC: | 155.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 43.1915 | 81.996 | 0.527 | 0.607 | -133.950 220.332 |
| expression | 9.6542 | 15.566 | 0.620 | 0.546 | -23.973 43.282 |
| Omnibus: | 1.228 | Durbin-Watson: | 1.618 |
| Prob(Omnibus): | 0.541 | Jarque-Bera (JB): | 0.770 |
| Skew: | -0.018 | Prob(JB): | 0.681 |
| Kurtosis: | 1.891 | Cond. No. | 44.7 |