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 |
| 0.014 | 0.907 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.690 |
| Model: | OLS | Adj. R-squared: | 0.641 |
| Method: | Least Squares | F-statistic: | 14.11 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.49e-05 |
| Time: | 04:47:14 | Log-Likelihood: | -99.630 |
| No. Observations: | 23 | AIC: | 207.3 |
| Df Residuals: | 19 | BIC: | 211.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 113.1013 | 54.867 | 2.061 | 0.053 | -1.737 227.939 |
| C(dose)[T.1] | -84.6144 | 87.496 | -0.967 | 0.346 | -267.746 98.517 |
| expression | -10.3923 | 9.627 | -1.080 | 0.294 | -30.541 9.757 |
| expression:C(dose)[T.1] | 24.5467 | 15.504 | 1.583 | 0.130 | -7.904 56.997 |
| Omnibus: | 1.657 | Durbin-Watson: | 1.666 |
| Prob(Omnibus): | 0.437 | Jarque-Bera (JB): | 0.979 |
| Skew: | 0.037 | Prob(JB): | 0.613 |
| Kurtosis: | 1.992 | Cond. No. | 149. |
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: | 18.51 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.81e-05 |
| Time: | 04:47:14 | Log-Likelihood: | -101.05 |
| 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 | 59.4706 | 44.758 | 1.329 | 0.199 | -33.893 152.834 |
| C(dose)[T.1] | 53.2614 | 8.790 | 6.059 | 0.000 | 34.926 71.597 |
| expression | -0.9286 | 7.825 | -0.119 | 0.907 | -17.252 15.395 |
| Omnibus: | 0.259 | Durbin-Watson: | 1.885 |
| Prob(Omnibus): | 0.878 | Jarque-Bera (JB): | 0.446 |
| Skew: | 0.025 | Prob(JB): | 0.800 |
| Kurtosis: | 2.320 | Cond. No. | 59.8 |
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: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 04:47:14 | 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: | 0.005 |
| Model: | OLS | Adj. R-squared: | -0.042 |
| Method: | Least Squares | F-statistic: | 0.1160 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.737 |
| Time: | 04:47:14 | Log-Likelihood: | -113.04 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 104.3080 | 72.543 | 1.438 | 0.165 | -46.554 255.170 |
| expression | -4.3693 | 12.826 | -0.341 | 0.737 | -31.043 22.304 |
| Omnibus: | 3.398 | Durbin-Watson: | 2.469 |
| Prob(Omnibus): | 0.183 | Jarque-Bera (JB): | 1.642 |
| Skew: | 0.321 | Prob(JB): | 0.440 |
| Kurtosis: | 1.859 | Cond. No. | 58.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.778 | 0.395 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.513 |
| Model: | OLS | Adj. R-squared: | 0.380 |
| Method: | Least Squares | F-statistic: | 3.859 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0414 |
| Time: | 04:47:14 | Log-Likelihood: | -69.907 |
| No. Observations: | 15 | AIC: | 147.8 |
| Df Residuals: | 11 | BIC: | 150.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -147.7573 | 179.578 | -0.823 | 0.428 | -543.006 247.492 |
| C(dose)[T.1] | 253.8139 | 256.116 | 0.991 | 0.343 | -309.895 817.522 |
| expression | 32.9890 | 27.476 | 1.201 | 0.255 | -27.485 93.463 |
| expression:C(dose)[T.1] | -31.4703 | 37.964 | -0.829 | 0.425 | -115.028 52.088 |
| Omnibus: | 2.441 | Durbin-Watson: | 1.244 |
| Prob(Omnibus): | 0.295 | Jarque-Bera (JB): | 1.521 |
| Skew: | -0.772 | Prob(JB): | 0.467 |
| Kurtosis: | 2.771 | Cond. No. | 309. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.482 |
| Model: | OLS | Adj. R-squared: | 0.396 |
| Method: | Least Squares | F-statistic: | 5.591 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0192 |
| Time: | 04:47:14 | Log-Likelihood: | -70.362 |
| No. Observations: | 15 | AIC: | 146.7 |
| Df Residuals: | 12 | BIC: | 148.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -40.2341 | 122.563 | -0.328 | 0.748 | -307.275 226.807 |
| C(dose)[T.1] | 42.0033 | 17.296 | 2.428 | 0.032 | 4.318 79.688 |
| expression | 16.5052 | 18.712 | 0.882 | 0.395 | -24.264 57.275 |
| Omnibus: | 1.732 | Durbin-Watson: | 0.929 |
| Prob(Omnibus): | 0.421 | Jarque-Bera (JB): | 1.354 |
| Skew: | -0.661 | Prob(JB): | 0.508 |
| Kurtosis: | 2.355 | Cond. No. | 112. |
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: | 04:47:14 | 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: | 0.228 |
| Model: | OLS | Adj. R-squared: | 0.169 |
| Method: | Least Squares | F-statistic: | 3.838 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0719 |
| Time: | 04:47:14 | Log-Likelihood: | -73.360 |
| No. Observations: | 15 | AIC: | 150.7 |
| Df Residuals: | 13 | BIC: | 152.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -162.5651 | 131.101 | -1.240 | 0.237 | -445.791 120.661 |
| expression | 37.9300 | 19.362 | 1.959 | 0.072 | -3.899 79.759 |
| Omnibus: | 1.493 | Durbin-Watson: | 1.624 |
| Prob(Omnibus): | 0.474 | Jarque-Bera (JB): | 1.213 |
| Skew: | 0.563 | Prob(JB): | 0.545 |
| Kurtosis: | 2.181 | Cond. No. | 102. |