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 |
| 0.375 | 0.547 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.602 |
| Method: | Least Squares | F-statistic: | 12.10 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000117 |
| Time: | 03:33:28 | Log-Likelihood: | -100.82 |
| 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 | 91.7046 | 153.522 | 0.597 | 0.557 | -229.620 413.029 |
| C(dose)[T.1] | 108.5730 | 219.476 | 0.495 | 0.626 | -350.796 567.942 |
| expression | -4.5500 | 18.614 | -0.244 | 0.810 | -43.510 34.410 |
| expression:C(dose)[T.1] | -5.8689 | 25.623 | -0.229 | 0.821 | -59.498 47.760 |
| Omnibus: | 0.393 | Durbin-Watson: | 1.762 |
| Prob(Omnibus): | 0.822 | Jarque-Bera (JB): | 0.529 |
| Skew: | -0.091 | Prob(JB): | 0.768 |
| Kurtosis: | 2.280 | Cond. No. | 563. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.621 |
| Method: | Least Squares | F-statistic: | 19.03 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.35e-05 |
| Time: | 03:33:28 | Log-Likelihood: | -100.85 |
| No. Observations: | 23 | AIC: | 207.7 |
| Df Residuals: | 20 | BIC: | 211.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 117.2300 | 103.062 | 1.137 | 0.269 | -97.753 332.214 |
| C(dose)[T.1] | 58.3803 | 11.970 | 4.877 | 0.000 | 33.411 83.349 |
| expression | -7.6475 | 12.485 | -0.613 | 0.547 | -33.691 18.396 |
| Omnibus: | 0.401 | Durbin-Watson: | 1.779 |
| Prob(Omnibus): | 0.818 | Jarque-Bera (JB): | 0.535 |
| Skew: | -0.100 | Prob(JB): | 0.765 |
| Kurtosis: | 2.280 | Cond. No. | 207. |
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: | 03:33:28 | 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: | 0.246 |
| Model: | OLS | Adj. R-squared: | 0.210 |
| Method: | Least Squares | F-statistic: | 6.844 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0161 |
| Time: | 03:33:28 | Log-Likelihood: | -109.86 |
| No. Observations: | 23 | AIC: | 223.7 |
| Df Residuals: | 21 | BIC: | 226.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -213.2102 | 112.144 | -1.901 | 0.071 | -446.426 20.006 |
| expression | 34.2355 | 13.086 | 2.616 | 0.016 | 7.021 61.450 |
| Omnibus: | 2.370 | Durbin-Watson: | 2.450 |
| Prob(Omnibus): | 0.306 | Jarque-Bera (JB): | 1.713 |
| Skew: | 0.483 | Prob(JB): | 0.425 |
| Kurtosis: | 2.075 | Cond. No. | 155. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 3.845 | 0.074 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.585 |
| Model: | OLS | Adj. R-squared: | 0.472 |
| Method: | Least Squares | F-statistic: | 5.172 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0180 |
| Time: | 03:33:28 | Log-Likelihood: | -68.701 |
| No. Observations: | 15 | AIC: | 145.4 |
| Df Residuals: | 11 | BIC: | 148.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -109.8562 | 177.295 | -0.620 | 0.548 | -500.081 280.368 |
| C(dose)[T.1] | -20.7281 | 234.349 | -0.088 | 0.931 | -536.528 495.072 |
| expression | 23.8280 | 23.788 | 1.002 | 0.338 | -28.530 76.186 |
| expression:C(dose)[T.1] | 8.1413 | 30.937 | 0.263 | 0.797 | -59.950 76.232 |
| Omnibus: | 1.060 | Durbin-Watson: | 1.417 |
| Prob(Omnibus): | 0.589 | Jarque-Bera (JB): | 0.260 |
| Skew: | 0.318 | Prob(JB): | 0.878 |
| Kurtosis: | 3.103 | Cond. No. | 353. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.583 |
| Model: | OLS | Adj. R-squared: | 0.513 |
| Method: | Least Squares | F-statistic: | 8.372 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00529 |
| Time: | 03:33:28 | Log-Likelihood: | -68.748 |
| No. Observations: | 15 | AIC: | 143.5 |
| Df Residuals: | 12 | BIC: | 145.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -145.6707 | 109.136 | -1.335 | 0.207 | -383.458 92.117 |
| C(dose)[T.1] | 40.8182 | 14.348 | 2.845 | 0.015 | 9.556 72.081 |
| expression | 28.6417 | 14.607 | 1.961 | 0.074 | -3.184 60.467 |
| Omnibus: | 0.568 | Durbin-Watson: | 1.353 |
| Prob(Omnibus): | 0.753 | Jarque-Bera (JB): | 0.118 |
| Skew: | 0.215 | Prob(JB): | 0.943 |
| Kurtosis: | 2.932 | Cond. No. | 124. |
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: | 03:33:28 | 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: | 0.301 |
| Model: | OLS | Adj. R-squared: | 0.247 |
| Method: | Least Squares | F-statistic: | 5.598 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0342 |
| Time: | 03:33:28 | Log-Likelihood: | -72.614 |
| No. Observations: | 15 | AIC: | 149.2 |
| Df Residuals: | 13 | BIC: | 150.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -217.8958 | 131.958 | -1.651 | 0.123 | -502.974 67.182 |
| expression | 41.0156 | 17.336 | 2.366 | 0.034 | 3.564 78.467 |
| Omnibus: | 3.408 | Durbin-Watson: | 2.091 |
| Prob(Omnibus): | 0.182 | Jarque-Bera (JB): | 1.507 |
| Skew: | 0.745 | Prob(JB): | 0.471 |
| Kurtosis: | 3.438 | Cond. No. | 120. |