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 |
| 0.006 | 0.939 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.681 |
| Model: | OLS | Adj. R-squared: | 0.631 |
| Method: | Least Squares | F-statistic: | 13.53 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.86e-05 |
| Time: | 03:35:02 | Log-Likelihood: | -99.959 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 19 | BIC: | 212.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 0.4651 | 56.073 | 0.008 | 0.993 | -116.896 117.826 |
| C(dose)[T.1] | 164.7650 | 80.995 | 2.034 | 0.056 | -4.759 334.289 |
| expression | 15.0583 | 15.623 | 0.964 | 0.347 | -17.641 47.757 |
| expression:C(dose)[T.1] | -32.9771 | 23.875 | -1.381 | 0.183 | -82.947 16.993 |
| Omnibus: | 2.083 | Durbin-Watson: | 2.049 |
| Prob(Omnibus): | 0.353 | Jarque-Bera (JB): | 1.093 |
| Skew: | -0.063 | Prob(JB): | 0.579 |
| Kurtosis: | 1.939 | Cond. No. | 88.3 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.50 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.83e-05 |
| Time: | 03:35:02 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 50.8626 | 43.534 | 1.168 | 0.256 | -39.947 141.672 |
| C(dose)[T.1] | 53.6650 | 9.733 | 5.513 | 0.000 | 33.361 73.969 |
| expression | 0.9374 | 12.079 | 0.078 | 0.939 | -24.258 26.133 |
| Omnibus: | 0.345 | Durbin-Watson: | 1.905 |
| Prob(Omnibus): | 0.841 | Jarque-Bera (JB): | 0.498 |
| Skew: | 0.055 | Prob(JB): | 0.779 |
| Kurtosis: | 2.287 | Cond. No. | 37.3 |
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: | 03:35:02 | 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: | 0.116 |
| Model: | OLS | Adj. R-squared: | 0.074 |
| Method: | Least Squares | F-statistic: | 2.753 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.112 |
| Time: | 03:35:02 | Log-Likelihood: | -111.69 |
| No. Observations: | 23 | AIC: | 227.4 |
| Df Residuals: | 21 | BIC: | 229.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 174.8685 | 57.743 | 3.028 | 0.006 | 54.786 294.951 |
| expression | -27.9715 | 16.857 | -1.659 | 0.112 | -63.028 7.085 |
| Omnibus: | 1.942 | Durbin-Watson: | 2.243 |
| Prob(Omnibus): | 0.379 | Jarque-Bera (JB): | 1.050 |
| Skew: | 0.022 | Prob(JB): | 0.591 |
| Kurtosis: | 1.954 | Cond. No. | 31.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.083 | 0.779 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.468 |
| Model: | OLS | Adj. R-squared: | 0.323 |
| Method: | Least Squares | F-statistic: | 3.228 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0648 |
| Time: | 03:35:02 | Log-Likelihood: | -70.564 |
| No. Observations: | 15 | AIC: | 149.1 |
| Df Residuals: | 11 | BIC: | 152.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -127.5841 | 308.771 | -0.413 | 0.687 | -807.184 552.016 |
| C(dose)[T.1] | 237.5471 | 339.572 | 0.700 | 0.499 | -509.845 984.939 |
| expression | 48.2368 | 76.319 | 0.632 | 0.540 | -119.741 216.214 |
| expression:C(dose)[T.1] | -46.7894 | 82.228 | -0.569 | 0.581 | -227.772 134.194 |
| Omnibus: | 2.364 | Durbin-Watson: | 0.640 |
| Prob(Omnibus): | 0.307 | Jarque-Bera (JB): | 1.665 |
| Skew: | -0.786 | Prob(JB): | 0.435 |
| Kurtosis: | 2.564 | Cond. No. | 306. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.453 |
| Model: | OLS | Adj. R-squared: | 0.361 |
| Method: | Least Squares | F-statistic: | 4.960 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0269 |
| Time: | 03:35:02 | Log-Likelihood: | -70.782 |
| 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 | 35.3681 | 112.153 | 0.315 | 0.758 | -208.992 279.729 |
| C(dose)[T.1] | 44.7553 | 22.020 | 2.032 | 0.065 | -3.222 92.733 |
| expression | 7.9302 | 27.596 | 0.287 | 0.779 | -52.197 68.057 |
| Omnibus: | 3.845 | Durbin-Watson: | 0.780 |
| Prob(Omnibus): | 0.146 | Jarque-Bera (JB): | 2.306 |
| Skew: | -0.960 | Prob(JB): | 0.316 |
| Kurtosis: | 2.996 | Cond. No. | 67.0 |
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: | 03:35:02 | 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: | 0.264 |
| Model: | OLS | Adj. R-squared: | 0.207 |
| Method: | Least Squares | F-statistic: | 4.665 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0500 |
| Time: | 03:35:02 | Log-Likelihood: | -73.000 |
| No. Observations: | 15 | AIC: | 150.0 |
| Df Residuals: | 13 | BIC: | 151.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -111.6656 | 95.467 | -1.170 | 0.263 | -317.909 94.578 |
| expression | 47.2952 | 21.898 | 2.160 | 0.050 | -0.011 94.602 |
| Omnibus: | 2.546 | Durbin-Watson: | 1.053 |
| Prob(Omnibus): | 0.280 | Jarque-Bera (JB): | 1.257 |
| Skew: | -0.708 | Prob(JB): | 0.533 |
| Kurtosis: | 3.084 | Cond. No. | 50.2 |