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 |
| 0.223 | 0.642 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.669 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 12.77 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 8.40e-05 |
| Time: | 11:38:06 | Log-Likelihood: | -100.41 |
| No. Observations: | 23 | AIC: | 208.8 |
| Df Residuals: | 19 | BIC: | 213.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 52.8152 | 40.220 | 1.313 | 0.205 | -31.367 136.997 |
| C(dose)[T.1] | -34.4965 | 93.778 | -0.368 | 0.717 | -230.776 161.783 |
| expression | 0.2313 | 6.603 | 0.035 | 0.972 | -13.589 14.052 |
| expression:C(dose)[T.1] | 15.0159 | 15.874 | 0.946 | 0.356 | -18.210 48.242 |
| Omnibus: | 0.429 | Durbin-Watson: | 1.874 |
| Prob(Omnibus): | 0.807 | Jarque-Bera (JB): | 0.550 |
| Skew: | -0.097 | Prob(JB): | 0.760 |
| Kurtosis: | 2.268 | Cond. No. | 151. |
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: | 18.81 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 2.54e-05 |
| Time: | 11:38:06 | Log-Likelihood: | -100.94 |
| 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 | 37.1701 | 36.565 | 1.017 | 0.322 | -39.104 113.444 |
| C(dose)[T.1] | 53.8180 | 8.781 | 6.129 | 0.000 | 35.502 72.134 |
| expression | 2.8294 | 5.989 | 0.472 | 0.642 | -9.663 15.322 |
| Omnibus: | 0.482 | Durbin-Watson: | 1.963 |
| Prob(Omnibus): | 0.786 | Jarque-Bera (JB): | 0.568 |
| Skew: | -0.020 | Prob(JB): | 0.753 |
| Kurtosis: | 2.232 | Cond. No. | 51.8 |
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: | Tue, 03 Dec 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 11:38:06 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.02095 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.886 |
| Time: | 11:38:06 | Log-Likelihood: | -113.09 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 88.1857 | 58.952 | 1.496 | 0.150 | -34.411 210.783 |
| expression | -1.4255 | 9.849 | -0.145 | 0.886 | -21.907 19.056 |
| Omnibus: | 3.228 | Durbin-Watson: | 2.487 |
| Prob(Omnibus): | 0.199 | Jarque-Bera (JB): | 1.577 |
| Skew: | 0.303 | Prob(JB): | 0.455 |
| Kurtosis: | 1.870 | Cond. No. | 50.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.369 | 0.265 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.636 |
| Model: | OLS | Adj. R-squared: | 0.537 |
| Method: | Least Squares | F-statistic: | 6.411 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.00902 |
| Time: | 11:38:06 | Log-Likelihood: | -67.717 |
| No. Observations: | 15 | AIC: | 143.4 |
| Df Residuals: | 11 | BIC: | 146.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 38.9818 | 74.230 | 0.525 | 0.610 | -124.397 202.361 |
| C(dose)[T.1] | -266.5990 | 164.551 | -1.620 | 0.133 | -628.773 95.575 |
| expression | 4.1884 | 10.835 | 0.387 | 0.706 | -19.658 28.035 |
| expression:C(dose)[T.1] | 53.1129 | 26.696 | 1.990 | 0.072 | -5.644 111.869 |
| Omnibus: | 0.534 | Durbin-Watson: | 1.420 |
| Prob(Omnibus): | 0.766 | Jarque-Bera (JB): | 0.031 |
| Skew: | -0.111 | Prob(JB): | 0.985 |
| Kurtosis: | 3.002 | Cond. No. | 188. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.505 |
| Model: | OLS | Adj. R-squared: | 0.423 |
| Method: | Least Squares | F-statistic: | 6.127 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0147 |
| Time: | 11:38:06 | Log-Likelihood: | -70.023 |
| No. Observations: | 15 | AIC: | 146.0 |
| Df Residuals: | 12 | BIC: | 148.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -20.4381 | 75.873 | -0.269 | 0.792 | -185.750 144.874 |
| C(dose)[T.1] | 59.3416 | 17.249 | 3.440 | 0.005 | 21.760 96.924 |
| expression | 12.9372 | 11.056 | 1.170 | 0.265 | -11.151 37.025 |
| Omnibus: | 1.985 | Durbin-Watson: | 1.315 |
| Prob(Omnibus): | 0.371 | Jarque-Bera (JB): | 1.457 |
| Skew: | -0.719 | Prob(JB): | 0.483 |
| Kurtosis: | 2.486 | Cond. No. | 67.8 |
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: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 11:38:07 | 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: | 0.017 |
| Model: | OLS | Adj. R-squared: | -0.058 |
| Method: | Least Squares | F-statistic: | 0.2280 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.641 |
| Time: | 11:38:07 | Log-Likelihood: | -75.170 |
| No. Observations: | 15 | AIC: | 154.3 |
| Df Residuals: | 13 | BIC: | 155.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 133.0536 | 83.098 | 1.601 | 0.133 | -46.468 312.575 |
| expression | -6.1798 | 12.942 | -0.478 | 0.641 | -34.139 21.779 |
| Omnibus: | 0.939 | Durbin-Watson: | 1.358 |
| Prob(Omnibus): | 0.625 | Jarque-Bera (JB): | 0.689 |
| Skew: | -0.004 | Prob(JB): | 0.709 |
| Kurtosis: | 1.950 | Cond. No. | 54.2 |