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 |
| 0.580 | 0.455 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.609 |
| Method: | Least Squares | F-statistic: | 12.40 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000101 |
| Time: | 04:24:35 | Log-Likelihood: | -100.63 |
| No. Observations: | 23 | AIC: | 209.3 |
| Df Residuals: | 19 | BIC: | 213.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 208.8689 | 198.092 | 1.054 | 0.305 | -205.742 623.479 |
| C(dose)[T.1] | -48.0731 | 252.833 | -0.190 | 0.851 | -577.259 481.112 |
| expression | -20.2429 | 25.915 | -0.781 | 0.444 | -74.484 33.998 |
| expression:C(dose)[T.1] | 13.4697 | 32.715 | 0.412 | 0.685 | -55.004 81.944 |
| Omnibus: | 0.968 | Durbin-Watson: | 1.957 |
| Prob(Omnibus): | 0.616 | Jarque-Bera (JB): | 0.766 |
| Skew: | 0.024 | Prob(JB): | 0.682 |
| Kurtosis: | 2.107 | Cond. No. | 620. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.659 |
| Model: | OLS | Adj. R-squared: | 0.625 |
| Method: | Least Squares | F-statistic: | 19.32 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.13e-05 |
| Time: | 04:24:35 | Log-Likelihood: | -100.73 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 20 | BIC: | 210.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 144.2938 | 118.462 | 1.218 | 0.237 | -102.813 391.401 |
| C(dose)[T.1] | 55.9503 | 9.302 | 6.015 | 0.000 | 36.547 75.353 |
| expression | -11.7909 | 15.485 | -0.761 | 0.455 | -44.093 20.511 |
| Omnibus: | 0.845 | Durbin-Watson: | 1.901 |
| Prob(Omnibus): | 0.655 | Jarque-Bera (JB): | 0.755 |
| Skew: | 0.143 | Prob(JB): | 0.686 |
| Kurtosis: | 2.160 | Cond. No. | 217. |
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: | 04:24:35 | 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: | 0.042 |
| Model: | OLS | Adj. R-squared: | -0.004 |
| Method: | Least Squares | F-statistic: | 0.9197 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.348 |
| Time: | 04:24:35 | Log-Likelihood: | -112.61 |
| No. Observations: | 23 | AIC: | 229.2 |
| Df Residuals: | 21 | BIC: | 231.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -95.1596 | 182.491 | -0.521 | 0.608 | -474.670 284.351 |
| expression | 22.5758 | 23.541 | 0.959 | 0.348 | -26.380 71.532 |
| Omnibus: | 4.752 | Durbin-Watson: | 2.371 |
| Prob(Omnibus): | 0.093 | Jarque-Bera (JB): | 1.722 |
| Skew: | 0.224 | Prob(JB): | 0.423 |
| Kurtosis: | 1.736 | Cond. No. | 204. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.086 | 0.775 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.454 |
| Model: | OLS | Adj. R-squared: | 0.305 |
| Method: | Least Squares | F-statistic: | 3.043 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0744 |
| Time: | 04:24:35 | Log-Likelihood: | -70.768 |
| No. Observations: | 15 | AIC: | 149.5 |
| Df Residuals: | 11 | BIC: | 152.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 85.2281 | 126.490 | 0.674 | 0.514 | -193.175 363.631 |
| C(dose)[T.1] | 81.1626 | 220.338 | 0.368 | 0.720 | -403.798 566.124 |
| expression | -2.6958 | 19.071 | -0.141 | 0.890 | -44.672 39.280 |
| expression:C(dose)[T.1] | -4.1951 | 31.391 | -0.134 | 0.896 | -73.286 64.896 |
| Omnibus: | 2.209 | Durbin-Watson: | 0.796 |
| Prob(Omnibus): | 0.331 | Jarque-Bera (JB): | 1.582 |
| Skew: | -0.760 | Prob(JB): | 0.453 |
| Kurtosis: | 2.529 | Cond. No. | 242. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.453 |
| Model: | OLS | Adj. R-squared: | 0.361 |
| Method: | Least Squares | F-statistic: | 4.962 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0269 |
| Time: | 04:24:35 | Log-Likelihood: | -70.780 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 95.4523 | 96.521 | 0.989 | 0.342 | -114.849 305.754 |
| C(dose)[T.1] | 51.8245 | 18.077 | 2.867 | 0.014 | 12.439 91.210 |
| expression | -4.2443 | 14.515 | -0.292 | 0.775 | -35.870 27.381 |
| Omnibus: | 2.337 | Durbin-Watson: | 0.816 |
| Prob(Omnibus): | 0.311 | Jarque-Bera (JB): | 1.644 |
| Skew: | -0.781 | Prob(JB): | 0.440 |
| Kurtosis: | 2.565 | Cond. No. | 88.2 |
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: | 04:24:35 | 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: | 0.078 |
| Model: | OLS | Adj. R-squared: | 0.007 |
| Method: | Least Squares | F-statistic: | 1.097 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.314 |
| Time: | 04:24:35 | Log-Likelihood: | -74.693 |
| No. Observations: | 15 | AIC: | 153.4 |
| Df Residuals: | 13 | BIC: | 154.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -20.3559 | 109.326 | -0.186 | 0.855 | -256.539 215.828 |
| expression | 16.4464 | 15.706 | 1.047 | 0.314 | -17.484 50.377 |
| Omnibus: | 0.837 | Durbin-Watson: | 1.332 |
| Prob(Omnibus): | 0.658 | Jarque-Bera (JB): | 0.670 |
| Skew: | -0.101 | Prob(JB): | 0.715 |
| Kurtosis: | 1.984 | Cond. No. | 79.6 |