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 |
| 0.889 | 0.357 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.671 |
| Model: | OLS | Adj. R-squared: | 0.620 |
| Method: | Least Squares | F-statistic: | 12.95 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.73e-05 |
| Time: | 05:09:47 | Log-Likelihood: | -100.30 |
| No. Observations: | 23 | AIC: | 208.6 |
| Df Residuals: | 19 | BIC: | 213.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 115.3595 | 57.269 | 2.014 | 0.058 | -4.505 235.224 |
| C(dose)[T.1] | 6.7978 | 69.243 | 0.098 | 0.923 | -138.129 151.724 |
| expression | -15.1181 | 14.080 | -1.074 | 0.296 | -44.587 14.351 |
| expression:C(dose)[T.1] | 11.3428 | 17.226 | 0.658 | 0.518 | -24.711 47.397 |
| Omnibus: | 0.238 | Durbin-Watson: | 1.524 |
| Prob(Omnibus): | 0.888 | Jarque-Bera (JB): | 0.432 |
| Skew: | -0.007 | Prob(JB): | 0.806 |
| Kurtosis: | 2.329 | Cond. No. | 94.4 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.664 |
| Model: | OLS | Adj. R-squared: | 0.630 |
| Method: | Least Squares | F-statistic: | 19.76 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.83e-05 |
| Time: | 05:09:47 | Log-Likelihood: | -100.56 |
| No. Observations: | 23 | AIC: | 207.1 |
| Df Residuals: | 20 | BIC: | 210.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 84.7073 | 32.883 | 2.576 | 0.018 | 16.115 153.299 |
| C(dose)[T.1] | 52.0211 | 8.694 | 5.984 | 0.000 | 33.886 70.156 |
| expression | -7.5401 | 7.996 | -0.943 | 0.357 | -24.219 9.139 |
| Omnibus: | 0.192 | Durbin-Watson: | 1.786 |
| Prob(Omnibus): | 0.909 | Jarque-Bera (JB): | 0.400 |
| Skew: | -0.012 | Prob(JB): | 0.819 |
| Kurtosis: | 2.354 | Cond. No. | 32.8 |
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: | 05:09:47 | 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: | 0.062 |
| Model: | OLS | Adj. R-squared: | 0.018 |
| Method: | Least Squares | F-statistic: | 1.400 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.250 |
| Time: | 05:09:47 | Log-Likelihood: | -112.36 |
| No. Observations: | 23 | AIC: | 228.7 |
| Df Residuals: | 21 | BIC: | 231.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 140.0108 | 51.442 | 2.722 | 0.013 | 33.031 246.990 |
| expression | -15.2201 | 12.865 | -1.183 | 0.250 | -41.975 11.535 |
| Omnibus: | 4.154 | Durbin-Watson: | 2.361 |
| Prob(Omnibus): | 0.125 | Jarque-Bera (JB): | 1.839 |
| Skew: | 0.350 | Prob(JB): | 0.399 |
| Kurtosis: | 1.805 | Cond. No. | 31.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.024 | 0.881 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.457 |
| Model: | OLS | Adj. R-squared: | 0.309 |
| Method: | Least Squares | F-statistic: | 3.087 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0720 |
| Time: | 05:09:47 | Log-Likelihood: | -70.719 |
| No. Observations: | 15 | AIC: | 149.4 |
| Df Residuals: | 11 | BIC: | 152.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 57.7006 | 75.734 | 0.762 | 0.462 | -108.989 224.390 |
| C(dose)[T.1] | 87.5582 | 106.167 | 0.825 | 0.427 | -146.113 321.230 |
| expression | 2.3463 | 18.039 | 0.130 | 0.899 | -37.358 42.050 |
| expression:C(dose)[T.1] | -10.8393 | 28.311 | -0.383 | 0.709 | -73.151 51.472 |
| Omnibus: | 2.080 | Durbin-Watson: | 0.817 |
| Prob(Omnibus): | 0.353 | Jarque-Bera (JB): | 1.606 |
| Skew: | -0.726 | Prob(JB): | 0.448 |
| Kurtosis: | 2.321 | Cond. No. | 67.4 |
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: | 4.906 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0277 |
| Time: | 05:09:47 | 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 | 75.9465 | 56.729 | 1.339 | 0.205 | -47.655 199.548 |
| C(dose)[T.1] | 47.6052 | 18.840 | 2.527 | 0.027 | 6.555 88.655 |
| expression | -2.0545 | 13.399 | -0.153 | 0.881 | -31.249 27.140 |
| Omnibus: | 2.330 | Durbin-Watson: | 0.780 |
| Prob(Omnibus): | 0.312 | Jarque-Bera (JB): | 1.684 |
| Skew: | -0.783 | Prob(JB): | 0.431 |
| Kurtosis: | 2.508 | Cond. No. | 30.0 |
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: | 05:09:47 | 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: | 0.157 |
| Model: | OLS | Adj. R-squared: | 0.092 |
| Method: | Least Squares | F-statistic: | 2.424 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.144 |
| Time: | 05:09:47 | Log-Likelihood: | -74.018 |
| No. Observations: | 15 | AIC: | 152.0 |
| Df Residuals: | 13 | BIC: | 153.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 170.9565 | 50.513 | 3.384 | 0.005 | 61.829 280.084 |
| expression | -20.7049 | 13.299 | -1.557 | 0.144 | -49.436 8.026 |
| Omnibus: | 1.142 | Durbin-Watson: | 1.133 |
| Prob(Omnibus): | 0.565 | Jarque-Bera (JB): | 0.808 |
| Skew: | 0.215 | Prob(JB): | 0.667 |
| Kurtosis: | 1.947 | Cond. No. | 21.9 |