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 |
| 0.133 | 0.719 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.692 |
| Model: | OLS | Adj. R-squared: | 0.643 |
| Method: | Least Squares | F-statistic: | 14.21 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.28e-05 |
| Time: | 04:33:28 | Log-Likelihood: | -99.572 |
| No. Observations: | 23 | AIC: | 207.1 |
| Df Residuals: | 19 | BIC: | 211.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 191.5012 | 98.026 | 1.954 | 0.066 | -13.671 396.673 |
| C(dose)[T.1] | -155.2572 | 131.660 | -1.179 | 0.253 | -430.825 120.310 |
| expression | -15.3067 | 10.910 | -1.403 | 0.177 | -38.141 7.527 |
| expression:C(dose)[T.1] | 23.7442 | 15.056 | 1.577 | 0.131 | -7.768 55.256 |
| Omnibus: | 0.026 | Durbin-Watson: | 1.514 |
| Prob(Omnibus): | 0.987 | Jarque-Bera (JB): | 0.197 |
| Skew: | 0.065 | Prob(JB): | 0.906 |
| Kurtosis: | 2.566 | Cond. No. | 363. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.617 |
| Method: | Least Squares | F-statistic: | 18.68 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.65e-05 |
| Time: | 04:33:28 | Log-Likelihood: | -100.99 |
| 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 | 79.6781 | 70.159 | 1.136 | 0.270 | -66.672 226.028 |
| C(dose)[T.1] | 51.8636 | 9.631 | 5.385 | 0.000 | 31.774 71.953 |
| expression | -2.8396 | 7.793 | -0.364 | 0.719 | -19.095 13.416 |
| Omnibus: | 0.307 | Durbin-Watson: | 1.822 |
| Prob(Omnibus): | 0.858 | Jarque-Bera (JB): | 0.474 |
| Skew: | 0.007 | Prob(JB): | 0.789 |
| Kurtosis: | 2.296 | Cond. No. | 143. |
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: | 04:33:28 | 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: | 0.146 |
| Model: | OLS | Adj. R-squared: | 0.105 |
| Method: | Least Squares | F-statistic: | 3.587 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0721 |
| Time: | 04:33:28 | Log-Likelihood: | -111.29 |
| No. Observations: | 23 | AIC: | 226.6 |
| Df Residuals: | 21 | BIC: | 228.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 258.1566 | 94.457 | 2.733 | 0.012 | 61.721 454.592 |
| expression | -20.4602 | 10.804 | -1.894 | 0.072 | -42.928 2.007 |
| Omnibus: | 2.898 | Durbin-Watson: | 2.160 |
| Prob(Omnibus): | 0.235 | Jarque-Bera (JB): | 1.891 |
| Skew: | 0.492 | Prob(JB): | 0.388 |
| Kurtosis: | 1.998 | Cond. No. | 125. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.027 | 0.871 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.621 |
| Model: | OLS | Adj. R-squared: | 0.518 |
| Method: | Least Squares | F-statistic: | 6.020 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0111 |
| Time: | 04:33:28 | Log-Likelihood: | -68.014 |
| No. Observations: | 15 | AIC: | 144.0 |
| Df Residuals: | 11 | BIC: | 146.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -2841.6953 | 1684.150 | -1.687 | 0.120 | -6548.484 865.094 |
| C(dose)[T.1] | 4612.2978 | 2044.814 | 2.256 | 0.045 | 111.692 9112.904 |
| expression | 226.2972 | 131.006 | 1.727 | 0.112 | -62.044 514.639 |
| expression:C(dose)[T.1] | -355.4832 | 159.269 | -2.232 | 0.047 | -706.032 -4.934 |
| Omnibus: | 0.132 | Durbin-Watson: | 1.637 |
| Prob(Omnibus): | 0.936 | Jarque-Bera (JB): | 0.224 |
| Skew: | -0.176 | Prob(JB): | 0.894 |
| Kurtosis: | 2.515 | Cond. No. | 5.58e+03 |
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: | 4.910 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0277 |
| Time: | 04:33:28 | Log-Likelihood: | -70.816 |
| 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 | 250.1581 | 1105.358 | 0.226 | 0.825 | -2158.211 2658.527 |
| C(dose)[T.1] | 48.4536 | 16.351 | 2.963 | 0.012 | 12.827 84.080 |
| expression | -14.2143 | 85.980 | -0.165 | 0.871 | -201.548 173.120 |
| Omnibus: | 2.572 | Durbin-Watson: | 0.743 |
| Prob(Omnibus): | 0.276 | Jarque-Bera (JB): | 1.851 |
| Skew: | -0.826 | Prob(JB): | 0.396 |
| Kurtosis: | 2.517 | Cond. No. | 1.82e+03 |
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: | 04:33:28 | 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: | 0.048 |
| Model: | OLS | Adj. R-squared: | -0.026 |
| Method: | Least Squares | F-statistic: | 0.6494 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.435 |
| Time: | 04:33:28 | Log-Likelihood: | -74.934 |
| No. Observations: | 15 | AIC: | 153.9 |
| Df Residuals: | 13 | BIC: | 155.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 1174.1279 | 1340.789 | 0.876 | 0.397 | -1722.470 4070.725 |
| expression | -84.2304 | 104.522 | -0.806 | 0.435 | -310.037 141.576 |
| Omnibus: | 0.202 | Durbin-Watson: | 1.427 |
| Prob(Omnibus): | 0.904 | Jarque-Bera (JB): | 0.255 |
| Skew: | -0.217 | Prob(JB): | 0.880 |
| Kurtosis: | 2.532 | Cond. No. | 1.74e+03 |