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 |
0.336 | 0.568 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.666 |
Model: | OLS | Adj. R-squared: | 0.614 |
Method: | Least Squares | F-statistic: | 12.65 |
Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 8.93e-05 |
Time: | 19:42:32 | Log-Likelihood: | -100.48 |
No. Observations: | 23 | AIC: | 209.0 |
Df Residuals: | 19 | BIC: | 213.5 |
Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 76.9335 | 137.240 | 0.561 | 0.582 | -210.313 364.180 |
C(dose)[T.1] | 316.3718 | 322.799 | 0.980 | 0.339 | -359.255 991.998 |
expression | -2.9139 | 17.580 | -0.166 | 0.870 | -39.710 33.882 |
expression:C(dose)[T.1] | -32.6648 | 40.395 | -0.809 | 0.429 | -117.212 51.882 |
Omnibus: | 0.584 | Durbin-Watson: | 1.794 |
Prob(Omnibus): | 0.747 | Jarque-Bera (JB): | 0.360 |
Skew: | -0.293 | Prob(JB): | 0.835 |
Kurtosis: | 2.821 | Cond. No. | 688. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.655 |
Model: | OLS | Adj. R-squared: | 0.620 |
Method: | Least Squares | F-statistic: | 18.97 |
Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 2.40e-05 |
Time: | 19:42:32 | Log-Likelihood: | -100.87 |
No. Observations: | 23 | AIC: | 207.7 |
Df Residuals: | 20 | BIC: | 211.1 |
Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 125.1849 | 122.515 | 1.022 | 0.319 | -130.377 380.747 |
C(dose)[T.1] | 55.4569 | 9.434 | 5.879 | 0.000 | 35.779 75.135 |
expression | -9.1009 | 15.690 | -0.580 | 0.568 | -41.831 23.629 |
Omnibus: | 0.093 | Durbin-Watson: | 1.987 |
Prob(Omnibus): | 0.955 | Jarque-Bera (JB): | 0.318 |
Skew: | -0.020 | Prob(JB): | 0.853 |
Kurtosis: | 2.426 | Cond. No. | 227. |
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: | Tue, 28 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
Time: | 19:42:32 | 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: | 0.058 |
Model: | OLS | Adj. R-squared: | 0.014 |
Method: | Least Squares | F-statistic: | 1.305 |
Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.266 |
Time: | 19:42:32 | Log-Likelihood: | -112.41 |
No. Observations: | 23 | AIC: | 228.8 |
Df Residuals: | 21 | BIC: | 231.1 |
Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | -130.9461 | 184.565 | -0.709 | 0.486 | -514.769 252.877 |
expression | 26.6318 | 23.316 | 1.142 | 0.266 | -21.856 75.119 |
Omnibus: | 0.524 | Durbin-Watson: | 2.294 |
Prob(Omnibus): | 0.769 | Jarque-Bera (JB): | 0.631 |
Skew: | 0.243 | Prob(JB): | 0.730 |
Kurtosis: | 2.351 | Cond. No. | 212. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
F-statistic | p-value | df difference |
0.136 | 0.719 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.456 |
Model: | OLS | Adj. R-squared: | 0.307 |
Method: | Least Squares | F-statistic: | 3.072 |
Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0728 |
Time: | 19:42:32 | Log-Likelihood: | -70.736 |
No. Observations: | 15 | AIC: | 149.5 |
Df Residuals: | 11 | BIC: | 152.3 |
Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | -0.5888 | 223.888 | -0.003 | 0.998 | -493.362 492.185 |
C(dose)[T.1] | 86.3874 | 262.613 | 0.329 | 0.748 | -491.620 664.395 |
expression | 9.4004 | 30.899 | 0.304 | 0.767 | -58.607 77.408 |
expression:C(dose)[T.1] | -5.0009 | 36.550 | -0.137 | 0.894 | -85.448 75.446 |
Omnibus: | 2.350 | Durbin-Watson: | 0.909 |
Prob(Omnibus): | 0.309 | Jarque-Bera (JB): | 1.737 |
Skew: | -0.787 | Prob(JB): | 0.420 |
Kurtosis: | 2.451 | Cond. No. | 342. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.455 |
Model: | OLS | Adj. R-squared: | 0.364 |
Method: | Least Squares | F-statistic: | 5.008 |
Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0262 |
Time: | 19:42:32 | Log-Likelihood: | -70.749 |
No. Observations: | 15 | AIC: | 147.5 |
Df Residuals: | 12 | BIC: | 149.6 |
Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 25.2705 | 115.009 | 0.220 | 0.830 | -225.313 275.854 |
C(dose)[T.1] | 50.5295 | 16.064 | 3.145 | 0.008 | 15.528 85.531 |
expression | 5.8265 | 15.816 | 0.368 | 0.719 | -28.634 40.287 |
Omnibus: | 2.349 | Durbin-Watson: | 0.897 |
Prob(Omnibus): | 0.309 | Jarque-Bera (JB): | 1.747 |
Skew: | -0.786 | Prob(JB): | 0.418 |
Kurtosis: | 2.434 | Cond. No. | 107. |
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: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.00629 |
Time: | 19:42:32 | 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: | 0.006 |
Model: | OLS | Adj. R-squared: | -0.071 |
Method: | Least Squares | F-statistic: | 0.07238 |
Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.792 |
Time: | 19:42:32 | Log-Likelihood: | -75.258 |
No. Observations: | 15 | AIC: | 154.5 |
Df Residuals: | 13 | BIC: | 155.9 |
Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 131.9390 | 142.617 | 0.925 | 0.372 | -176.166 440.044 |
expression | -5.3802 | 19.998 | -0.269 | 0.792 | -48.583 37.822 |
Omnibus: | 0.401 | Durbin-Watson: | 1.561 |
Prob(Omnibus): | 0.818 | Jarque-Bera (JB): | 0.498 |
Skew: | 0.021 | Prob(JB): | 0.779 |
Kurtosis: | 2.108 | Cond. No. | 102. |