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 |
| 1.421 | 0.247 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.674 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 13.12 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.12e-05 |
| Time: | 05:12:22 | Log-Likelihood: | -100.20 |
| No. Observations: | 23 | AIC: | 208.4 |
| Df Residuals: | 19 | BIC: | 212.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -16.2099 | 73.377 | -0.221 | 0.828 | -169.790 137.370 |
| C(dose)[T.1] | -31.9708 | 222.095 | -0.144 | 0.887 | -496.821 432.880 |
| expression | 7.9568 | 8.263 | 0.963 | 0.348 | -9.339 25.252 |
| expression:C(dose)[T.1] | 8.0018 | 23.008 | 0.348 | 0.732 | -40.154 56.157 |
| Omnibus: | 0.322 | Durbin-Watson: | 1.984 |
| Prob(Omnibus): | 0.851 | Jarque-Bera (JB): | 0.161 |
| Skew: | -0.188 | Prob(JB): | 0.923 |
| Kurtosis: | 2.835 | Cond. No. | 563. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.672 |
| Model: | OLS | Adj. R-squared: | 0.640 |
| Method: | Least Squares | F-statistic: | 20.52 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.43e-05 |
| Time: | 05:12:22 | Log-Likelihood: | -100.27 |
| No. Observations: | 23 | AIC: | 206.5 |
| Df Residuals: | 20 | BIC: | 210.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -25.3450 | 66.992 | -0.378 | 0.709 | -165.088 114.398 |
| C(dose)[T.1] | 45.1745 | 10.895 | 4.146 | 0.000 | 22.448 67.901 |
| expression | 8.9890 | 7.541 | 1.192 | 0.247 | -6.741 24.719 |
| Omnibus: | 0.153 | Durbin-Watson: | 1.923 |
| Prob(Omnibus): | 0.926 | Jarque-Bera (JB): | 0.202 |
| Skew: | -0.159 | Prob(JB): | 0.904 |
| Kurtosis: | 2.669 | Cond. No. | 150. |
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: | 05:12:22 | 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: | 0.391 |
| Model: | OLS | Adj. R-squared: | 0.362 |
| Method: | Least Squares | F-statistic: | 13.46 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00143 |
| Time: | 05:12:22 | Log-Likelihood: | -107.41 |
| No. Observations: | 23 | AIC: | 218.8 |
| Df Residuals: | 21 | BIC: | 221.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -186.1916 | 72.688 | -2.562 | 0.018 | -337.354 -35.029 |
| expression | 28.6406 | 7.806 | 3.669 | 0.001 | 12.408 44.873 |
| Omnibus: | 0.524 | Durbin-Watson: | 1.743 |
| Prob(Omnibus): | 0.770 | Jarque-Bera (JB): | 0.435 |
| Skew: | -0.301 | Prob(JB): | 0.805 |
| Kurtosis: | 2.699 | Cond. No. | 122. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.532 | 0.480 | 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: | 0.354 |
| Method: | Least Squares | F-statistic: | 3.561 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0509 |
| Time: | 05:12:22 | Log-Likelihood: | -70.210 |
| No. Observations: | 15 | AIC: | 148.4 |
| Df Residuals: | 11 | BIC: | 151.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 76.2990 | 124.718 | 0.612 | 0.553 | -198.204 350.802 |
| C(dose)[T.1] | -52.4343 | 157.332 | -0.333 | 0.745 | -398.719 293.850 |
| expression | -0.9893 | 13.851 | -0.071 | 0.944 | -31.475 29.496 |
| expression:C(dose)[T.1] | 11.8864 | 17.810 | 0.667 | 0.518 | -27.313 51.086 |
| Omnibus: | 2.368 | Durbin-Watson: | 1.025 |
| Prob(Omnibus): | 0.306 | Jarque-Bera (JB): | 1.621 |
| Skew: | -0.782 | Prob(JB): | 0.445 |
| Kurtosis: | 2.617 | Cond. No. | 247. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.472 |
| Model: | OLS | Adj. R-squared: | 0.384 |
| Method: | Least Squares | F-statistic: | 5.367 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0216 |
| Time: | 05:12:22 | Log-Likelihood: | -70.508 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 12 | BIC: | 149.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 11.8407 | 77.066 | 0.154 | 0.880 | -156.072 179.754 |
| C(dose)[T.1] | 52.0083 | 15.878 | 3.276 | 0.007 | 17.414 86.603 |
| expression | 6.1999 | 8.503 | 0.729 | 0.480 | -12.327 24.727 |
| Omnibus: | 2.761 | Durbin-Watson: | 0.946 |
| Prob(Omnibus): | 0.251 | Jarque-Bera (JB): | 1.898 |
| Skew: | -0.851 | Prob(JB): | 0.387 |
| Kurtosis: | 2.625 | Cond. No. | 89.5 |
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: | 05:12:22 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.077 |
| Method: | Least Squares | F-statistic: | 0.002688 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.959 |
| Time: | 05:12:22 | Log-Likelihood: | -75.299 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 98.5999 | 95.695 | 1.030 | 0.322 | -108.137 305.337 |
| expression | -0.5655 | 10.907 | -0.052 | 0.959 | -24.129 22.998 |
| Omnibus: | 0.638 | Durbin-Watson: | 1.616 |
| Prob(Omnibus): | 0.727 | Jarque-Bera (JB): | 0.594 |
| Skew: | 0.050 | Prob(JB): | 0.743 |
| Kurtosis: | 2.030 | Cond. No. | 83.7 |