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 |
| 0.083 | 0.777 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.661 |
| Model: | OLS | Adj. R-squared: | 0.607 |
| Method: | Least Squares | F-statistic: | 12.34 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.000104 |
| Time: | 22:50:01 | Log-Likelihood: | -100.67 |
| No. Observations: | 23 | AIC: | 209.3 |
| Df Residuals: | 19 | BIC: | 213.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 88.7049 | 78.677 | 1.127 | 0.274 | -75.969 253.378 |
| C(dose)[T.1] | -16.6469 | 94.663 | -0.176 | 0.862 | -214.780 181.486 |
| expression | -5.1579 | 11.728 | -0.440 | 0.665 | -29.705 19.390 |
| expression:C(dose)[T.1] | 11.0505 | 14.589 | 0.757 | 0.458 | -19.484 41.585 |
| Omnibus: | 0.011 | Durbin-Watson: | 1.758 |
| Prob(Omnibus): | 0.995 | Jarque-Bera (JB): | 0.159 |
| Skew: | 0.044 | Prob(JB): | 0.923 |
| Kurtosis: | 2.602 | Cond. No. | 194. |
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: | 18.61 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.72e-05 |
| Time: | 22:50:01 | Log-Likelihood: | -101.02 |
| 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 | 40.9385 | 46.545 | 0.880 | 0.390 | -56.152 138.029 |
| C(dose)[T.1] | 54.6578 | 9.884 | 5.530 | 0.000 | 34.041 75.275 |
| expression | 1.9841 | 6.900 | 0.288 | 0.777 | -12.410 16.378 |
| Omnibus: | 0.093 | Durbin-Watson: | 1.911 |
| Prob(Omnibus): | 0.954 | Jarque-Bera (JB): | 0.315 |
| Skew: | 0.049 | Prob(JB): | 0.854 |
| Kurtosis: | 2.435 | Cond. No. | 70.4 |
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, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 22:50:01 | 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: | 2.758 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.112 |
| Time: | 22:50:01 | 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 | 180.0300 | 60.781 | 2.962 | 0.007 | 53.629 306.431 |
| expression | -15.7483 | 9.483 | -1.661 | 0.112 | -35.468 3.972 |
| Omnibus: | 4.703 | Durbin-Watson: | 2.151 |
| Prob(Omnibus): | 0.095 | Jarque-Bera (JB): | 2.109 |
| Skew: | 0.427 | Prob(JB): | 0.348 |
| Kurtosis: | 1.787 | Cond. No. | 58.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.024 | 0.879 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.462 |
| Model: | OLS | Adj. R-squared: | 0.315 |
| Method: | Least Squares | F-statistic: | 3.148 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0687 |
| Time: | 22:50:01 | Log-Likelihood: | -70.651 |
| No. Observations: | 15 | AIC: | 149.3 |
| Df Residuals: | 11 | BIC: | 152.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -45.0262 | 223.084 | -0.202 | 0.844 | -536.031 445.978 |
| C(dose)[T.1] | 177.9263 | 260.117 | 0.684 | 0.508 | -394.588 750.440 |
| expression | 18.8162 | 37.274 | 0.505 | 0.624 | -63.224 100.856 |
| expression:C(dose)[T.1] | -21.4976 | 43.264 | -0.497 | 0.629 | -116.720 73.725 |
| Omnibus: | 2.675 | Durbin-Watson: | 0.866 |
| Prob(Omnibus): | 0.263 | Jarque-Bera (JB): | 1.775 |
| Skew: | -0.828 | Prob(JB): | 0.412 |
| Kurtosis: | 2.687 | Cond. No. | 295. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.450 |
| Model: | OLS | Adj. R-squared: | 0.358 |
| Method: | Least Squares | F-statistic: | 4.907 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0277 |
| Time: | 22:50:01 | Log-Likelihood: | -70.818 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 50.3422 | 110.085 | 0.457 | 0.656 | -189.512 290.196 |
| C(dose)[T.1] | 48.9300 | 15.816 | 3.094 | 0.009 | 14.470 83.390 |
| expression | 2.8589 | 18.319 | 0.156 | 0.879 | -37.055 42.773 |
| Omnibus: | 2.413 | Durbin-Watson: | 0.807 |
| Prob(Omnibus): | 0.299 | Jarque-Bera (JB): | 1.715 |
| Skew: | -0.796 | Prob(JB): | 0.424 |
| Kurtosis: | 2.545 | Cond. No. | 87.4 |
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, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 22:50:01 | 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: | 0.011 |
| Model: | OLS | Adj. R-squared: | -0.065 |
| Method: | Least Squares | F-statistic: | 0.1464 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.708 |
| Time: | 22:50:01 | Log-Likelihood: | -75.216 |
| 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 | 39.5778 | 141.734 | 0.279 | 0.784 | -266.619 345.775 |
| expression | 8.9757 | 23.460 | 0.383 | 0.708 | -41.706 59.658 |
| Omnibus: | 0.615 | Durbin-Watson: | 1.516 |
| Prob(Omnibus): | 0.735 | Jarque-Bera (JB): | 0.605 |
| Skew: | 0.152 | Prob(JB): | 0.739 |
| Kurtosis: | 2.064 | Cond. No. | 87.1 |