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 |
0.028 | 0.870 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.656 |
Model: | OLS | Adj. R-squared: | 0.601 |
Method: | Least Squares | F-statistic: | 12.06 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000120 |
Time: | 03:34:36 | Log-Likelihood: | -100.84 |
No. Observations: | 23 | AIC: | 209.7 |
Df Residuals: | 19 | BIC: | 214.2 |
Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | -25.2656 | 232.933 | -0.108 | 0.915 | -512.800 462.269 |
C(dose)[T.1] | 233.7699 | 309.126 | 0.756 | 0.459 | -413.237 880.777 |
expression | 8.7797 | 25.724 | 0.341 | 0.737 | -45.061 62.620 |
expression:C(dose)[T.1] | -19.4314 | 33.481 | -0.580 | 0.568 | -89.508 50.645 |
Omnibus: | 0.223 | Durbin-Watson: | 1.847 |
Prob(Omnibus): | 0.894 | Jarque-Bera (JB): | 0.420 |
Skew: | 0.099 | Prob(JB): | 0.810 |
Kurtosis: | 2.368 | Cond. No. | 879. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.650 |
Model: | OLS | Adj. R-squared: | 0.614 |
Method: | Least Squares | F-statistic: | 18.53 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.79e-05 |
Time: | 03:34:36 | 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 | 78.5631 | 146.677 | 0.536 | 0.598 | -227.401 384.527 |
C(dose)[T.1] | 54.4839 | 11.154 | 4.885 | 0.000 | 31.216 77.751 |
expression | -2.6906 | 16.190 | -0.166 | 0.870 | -36.462 31.081 |
Omnibus: | 0.456 | Durbin-Watson: | 1.858 |
Prob(Omnibus): | 0.796 | Jarque-Bera (JB): | 0.558 |
Skew: | 0.060 | Prob(JB): | 0.757 |
Kurtosis: | 2.246 | Cond. No. | 315. |
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: | 03:34:36 | 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: | 0.231 |
Model: | OLS | Adj. R-squared: | 0.195 |
Method: | Least Squares | F-statistic: | 6.325 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0201 |
Time: | 03:34:36 | Log-Likelihood: | -110.08 |
No. Observations: | 23 | AIC: | 224.2 |
Df Residuals: | 21 | BIC: | 226.4 |
Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | -348.1890 | 170.267 | -2.045 | 0.054 | -702.279 5.901 |
expression | 46.2311 | 18.383 | 2.515 | 0.020 | 8.002 84.461 |
Omnibus: | 1.826 | Durbin-Watson: | 2.404 |
Prob(Omnibus): | 0.401 | Jarque-Bera (JB): | 1.511 |
Skew: | 0.485 | Prob(JB): | 0.470 |
Kurtosis: | 2.202 | Cond. No. | 252. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
F-statistic | p-value | df difference |
0.399 | 0.540 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.487 |
Model: | OLS | Adj. R-squared: | 0.347 |
Method: | Least Squares | F-statistic: | 3.482 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0539 |
Time: | 03:34:36 | Log-Likelihood: | -70.292 |
No. Observations: | 15 | AIC: | 148.6 |
Df Residuals: | 11 | BIC: | 151.4 |
Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 259.0122 | 215.663 | 1.201 | 0.255 | -215.658 733.682 |
C(dose)[T.1] | -194.1866 | 366.350 | -0.530 | 0.607 | -1000.518 612.145 |
expression | -22.5364 | 25.332 | -0.890 | 0.393 | -78.292 33.219 |
expression:C(dose)[T.1] | 28.6330 | 43.070 | 0.665 | 0.520 | -66.163 123.429 |
Omnibus: | 2.569 | Durbin-Watson: | 0.692 |
Prob(Omnibus): | 0.277 | Jarque-Bera (JB): | 1.811 |
Skew: | -0.823 | Prob(JB): | 0.404 |
Kurtosis: | 2.566 | Cond. No. | 497. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.466 |
Model: | OLS | Adj. R-squared: | 0.378 |
Method: | Least Squares | F-statistic: | 5.246 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0231 |
Time: | 03:34:36 | Log-Likelihood: | -70.588 |
No. Observations: | 15 | AIC: | 147.2 |
Df Residuals: | 12 | BIC: | 149.3 |
Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 174.8070 | 170.441 | 1.026 | 0.325 | -196.553 546.167 |
C(dose)[T.1] | 49.1360 | 15.485 | 3.173 | 0.008 | 15.397 82.875 |
expression | -12.6311 | 20.005 | -0.631 | 0.540 | -56.219 30.956 |
Omnibus: | 3.538 | Durbin-Watson: | 0.720 |
Prob(Omnibus): | 0.171 | Jarque-Bera (JB): | 2.217 |
Skew: | -0.939 | Prob(JB): | 0.330 |
Kurtosis: | 2.872 | Cond. No. | 191. |
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: | 03:34:36 | 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: | 0.019 |
Model: | OLS | Adj. R-squared: | -0.057 |
Method: | Least Squares | F-statistic: | 0.2497 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.626 |
Time: | 03:34:36 | Log-Likelihood: | -75.157 |
No. Observations: | 15 | AIC: | 154.3 |
Df Residuals: | 13 | BIC: | 155.7 |
Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 204.3472 | 221.741 | 0.922 | 0.374 | -274.695 683.390 |
expression | -13.0235 | 26.065 | -0.500 | 0.626 | -69.333 43.286 |
Omnibus: | 0.972 | Durbin-Watson: | 1.708 |
Prob(Omnibus): | 0.615 | Jarque-Bera (JB): | 0.716 |
Skew: | 0.116 | Prob(JB): | 0.699 |
Kurtosis: | 1.955 | Cond. No. | 190. |