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 |
| 1.146 | 0.297 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.680 |
| Model: | OLS | Adj. R-squared: | 0.629 |
| Method: | Least Squares | F-statistic: | 13.43 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.13e-05 |
| Time: | 04:03:50 | Log-Likelihood: | -100.02 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 19 | BIC: | 212.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 72.9491 | 64.913 | 1.124 | 0.275 | -62.916 208.814 |
| C(dose)[T.1] | 139.8138 | 103.248 | 1.354 | 0.192 | -76.287 355.914 |
| expression | -2.9101 | 10.037 | -0.290 | 0.775 | -23.919 18.098 |
| expression:C(dose)[T.1] | -13.0086 | 15.730 | -0.827 | 0.418 | -45.931 19.914 |
| Omnibus: | 0.930 | Durbin-Watson: | 2.152 |
| Prob(Omnibus): | 0.628 | Jarque-Bera (JB): | 0.919 |
| Skew: | 0.358 | Prob(JB): | 0.632 |
| Kurtosis: | 2.332 | Cond. No. | 201. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.668 |
| Model: | OLS | Adj. R-squared: | 0.635 |
| Method: | Least Squares | F-statistic: | 20.13 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.62e-05 |
| Time: | 04:03:50 | Log-Likelihood: | -100.42 |
| No. Observations: | 23 | AIC: | 206.8 |
| Df Residuals: | 20 | BIC: | 210.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 107.0617 | 49.725 | 2.153 | 0.044 | 3.337 210.787 |
| C(dose)[T.1] | 54.7304 | 8.628 | 6.344 | 0.000 | 36.733 72.727 |
| expression | -8.2071 | 7.667 | -1.070 | 0.297 | -24.200 7.786 |
| Omnibus: | 0.833 | Durbin-Watson: | 2.149 |
| Prob(Omnibus): | 0.659 | Jarque-Bera (JB): | 0.772 |
| Skew: | 0.188 | Prob(JB): | 0.680 |
| Kurtosis: | 2.184 | Cond. No. | 78.4 |
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, 21 Nov 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 04:03:50 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.004594 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.947 |
| Time: | 04:03:50 | Log-Likelihood: | -113.10 |
| 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 | 85.3914 | 84.021 | 1.016 | 0.321 | -89.339 260.122 |
| expression | -0.8701 | 12.837 | -0.068 | 0.947 | -27.566 25.826 |
| Omnibus: | 3.374 | Durbin-Watson: | 2.503 |
| Prob(Omnibus): | 0.185 | Jarque-Bera (JB): | 1.579 |
| Skew: | 0.287 | Prob(JB): | 0.454 |
| Kurtosis: | 1.851 | Cond. No. | 78.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.123 | 0.310 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.570 |
| Model: | OLS | Adj. R-squared: | 0.453 |
| Method: | Least Squares | F-statistic: | 4.864 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0216 |
| Time: | 04:03:50 | Log-Likelihood: | -68.967 |
| No. Observations: | 15 | AIC: | 145.9 |
| Df Residuals: | 11 | BIC: | 148.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 270.0708 | 125.060 | 2.160 | 0.054 | -5.185 545.327 |
| C(dose)[T.1] | -299.8924 | 249.074 | -1.204 | 0.254 | -848.100 248.315 |
| expression | -24.2824 | 14.932 | -1.626 | 0.132 | -57.147 8.583 |
| expression:C(dose)[T.1] | 42.8189 | 31.060 | 1.379 | 0.195 | -25.544 111.182 |
| Omnibus: | 2.561 | Durbin-Watson: | 1.398 |
| Prob(Omnibus): | 0.278 | Jarque-Bera (JB): | 0.927 |
| Skew: | -0.568 | Prob(JB): | 0.629 |
| Kurtosis: | 3.439 | Cond. No. | 338. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.496 |
| Model: | OLS | Adj. R-squared: | 0.412 |
| Method: | Least Squares | F-statistic: | 5.903 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0164 |
| Time: | 04:03:50 | Log-Likelihood: | -70.162 |
| No. Observations: | 15 | AIC: | 146.3 |
| Df Residuals: | 12 | BIC: | 148.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 187.4863 | 113.823 | 1.647 | 0.125 | -60.514 435.486 |
| C(dose)[T.1] | 42.7981 | 16.217 | 2.639 | 0.022 | 7.464 78.132 |
| expression | -14.3864 | 13.576 | -1.060 | 0.310 | -43.965 15.192 |
| Omnibus: | 3.128 | Durbin-Watson: | 0.991 |
| Prob(Omnibus): | 0.209 | Jarque-Bera (JB): | 1.960 |
| Skew: | -0.881 | Prob(JB): | 0.375 |
| Kurtosis: | 2.827 | Cond. No. | 126. |
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: | 04:03:50 | 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: | 0.203 |
| Model: | OLS | Adj. R-squared: | 0.142 |
| Method: | Least Squares | F-statistic: | 3.319 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0916 |
| Time: | 04:03:50 | Log-Likelihood: | -73.595 |
| No. Observations: | 15 | AIC: | 151.2 |
| Df Residuals: | 13 | BIC: | 152.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 318.4632 | 123.722 | 2.574 | 0.023 | 51.179 585.748 |
| expression | -27.7252 | 15.218 | -1.822 | 0.092 | -60.602 5.152 |
| Omnibus: | 4.921 | Durbin-Watson: | 1.989 |
| Prob(Omnibus): | 0.085 | Jarque-Bera (JB): | 1.715 |
| Skew: | 0.408 | Prob(JB): | 0.424 |
| Kurtosis: | 1.558 | Cond. No. | 113. |