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 |
| 0.202 | 0.658 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.688 |
| Model: | OLS | Adj. R-squared: | 0.638 |
| Method: | Least Squares | F-statistic: | 13.94 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.85e-05 |
| Time: | 05:16:35 | Log-Likelihood: | -99.725 |
| No. Observations: | 23 | AIC: | 207.4 |
| Df Residuals: | 19 | BIC: | 212.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -14.8169 | 47.748 | -0.310 | 0.760 | -114.755 85.121 |
| C(dose)[T.1] | 138.5778 | 59.180 | 2.342 | 0.030 | 14.713 262.442 |
| expression | 12.7343 | 8.742 | 1.457 | 0.162 | -5.563 31.032 |
| expression:C(dose)[T.1] | -15.6682 | 10.734 | -1.460 | 0.161 | -38.134 6.797 |
| Omnibus: | 0.578 | Durbin-Watson: | 1.625 |
| Prob(Omnibus): | 0.749 | Jarque-Bera (JB): | 0.626 |
| Skew: | -0.106 | Prob(JB): | 0.731 |
| Kurtosis: | 2.220 | Cond. No. | 111. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.618 |
| Method: | Least Squares | F-statistic: | 18.78 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.56e-05 |
| Time: | 05:16:35 | Log-Likelihood: | -100.95 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 20 | BIC: | 211.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 41.5198 | 28.898 | 1.437 | 0.166 | -18.760 101.800 |
| C(dose)[T.1] | 53.0881 | 8.744 | 6.072 | 0.000 | 34.849 71.327 |
| expression | 2.3409 | 5.214 | 0.449 | 0.658 | -8.535 13.217 |
| Omnibus: | 0.128 | Durbin-Watson: | 1.863 |
| Prob(Omnibus): | 0.938 | Jarque-Bera (JB): | 0.348 |
| Skew: | -0.046 | Prob(JB): | 0.840 |
| Kurtosis: | 2.404 | Cond. No. | 38.0 |
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: | 05:16:35 | 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: | 0.012 |
| Model: | OLS | Adj. R-squared: | -0.035 |
| Method: | Least Squares | F-statistic: | 0.2579 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.617 |
| Time: | 05:16:35 | Log-Likelihood: | -112.96 |
| No. Observations: | 23 | AIC: | 229.9 |
| Df Residuals: | 21 | BIC: | 232.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 55.9244 | 47.393 | 1.180 | 0.251 | -42.634 154.483 |
| expression | 4.3487 | 8.562 | 0.508 | 0.617 | -13.458 22.155 |
| Omnibus: | 3.396 | Durbin-Watson: | 2.440 |
| Prob(Omnibus): | 0.183 | Jarque-Bera (JB): | 1.682 |
| Skew: | 0.343 | Prob(JB): | 0.431 |
| Kurtosis: | 1.867 | Cond. No. | 37.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.036 | 0.329 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.496 |
| Model: | OLS | Adj. R-squared: | 0.358 |
| Method: | Least Squares | F-statistic: | 3.608 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0493 |
| Time: | 05:16:35 | Log-Likelihood: | -70.162 |
| No. Observations: | 15 | AIC: | 148.3 |
| Df Residuals: | 11 | BIC: | 151.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 114.8519 | 87.295 | 1.316 | 0.215 | -77.284 306.988 |
| C(dose)[T.1] | 86.8774 | 132.939 | 0.654 | 0.527 | -205.720 379.475 |
| expression | -7.9152 | 14.443 | -0.548 | 0.595 | -39.705 23.875 |
| expression:C(dose)[T.1] | -5.8651 | 21.660 | -0.271 | 0.792 | -53.538 41.808 |
| Omnibus: | 2.555 | Durbin-Watson: | 0.990 |
| Prob(Omnibus): | 0.279 | Jarque-Bera (JB): | 1.784 |
| Skew: | -0.676 | Prob(JB): | 0.410 |
| Kurtosis: | 1.987 | Cond. No. | 138. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.493 |
| Model: | OLS | Adj. R-squared: | 0.408 |
| Method: | Least Squares | F-statistic: | 5.824 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0171 |
| Time: | 05:16:35 | Log-Likelihood: | -70.212 |
| No. Observations: | 15 | AIC: | 146.4 |
| Df Residuals: | 12 | BIC: | 148.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 130.4775 | 62.922 | 2.074 | 0.060 | -6.619 267.574 |
| C(dose)[T.1] | 51.1364 | 15.221 | 3.360 | 0.006 | 17.972 84.300 |
| expression | -10.5231 | 10.339 | -1.018 | 0.329 | -33.051 12.005 |
| Omnibus: | 2.495 | Durbin-Watson: | 1.114 |
| Prob(Omnibus): | 0.287 | Jarque-Bera (JB): | 1.824 |
| Skew: | -0.712 | Prob(JB): | 0.402 |
| Kurtosis: | 2.056 | Cond. No. | 52.8 |
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: | 05:16:35 | 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: | 0.015 |
| Model: | OLS | Adj. R-squared: | -0.060 |
| Method: | Least Squares | F-statistic: | 0.2022 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.660 |
| Time: | 05:16:35 | Log-Likelihood: | -75.184 |
| No. Observations: | 15 | AIC: | 154.4 |
| Df Residuals: | 13 | BIC: | 155.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 131.2610 | 84.214 | 1.559 | 0.143 | -50.672 313.194 |
| expression | -6.1733 | 13.729 | -0.450 | 0.660 | -35.834 23.487 |
| Omnibus: | 0.549 | Durbin-Watson: | 1.716 |
| Prob(Omnibus): | 0.760 | Jarque-Bera (JB): | 0.567 |
| Skew: | 0.095 | Prob(JB): | 0.753 |
| Kurtosis: | 2.067 | Cond. No. | 52.6 |