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 |
| 0.303 | 0.588 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.661 |
| Model: | OLS | Adj. R-squared: | 0.607 |
| Method: | Least Squares | F-statistic: | 12.34 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000104 |
| Time: | 04:38:13 | Log-Likelihood: | -100.67 |
| No. Observations: | 23 | AIC: | 209.3 |
| Df Residuals: | 19 | BIC: | 213.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 48.5022 | 67.299 | 0.721 | 0.480 | -92.356 189.360 |
| C(dose)[T.1] | 103.4933 | 87.290 | 1.186 | 0.250 | -79.206 286.192 |
| expression | 1.0291 | 12.087 | 0.085 | 0.933 | -24.270 26.328 |
| expression:C(dose)[T.1] | -9.7772 | 16.255 | -0.601 | 0.555 | -43.799 24.245 |
| Omnibus: | 1.239 | Durbin-Watson: | 1.889 |
| Prob(Omnibus): | 0.538 | Jarque-Bera (JB): | 1.007 |
| Skew: | 0.280 | Prob(JB): | 0.604 |
| Kurtosis: | 2.142 | Cond. No. | 145. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.654 |
| Model: | OLS | Adj. R-squared: | 0.620 |
| Method: | Least Squares | F-statistic: | 18.93 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.44e-05 |
| Time: | 04:38:13 | Log-Likelihood: | -100.89 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 20 | BIC: | 211.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 78.4789 | 44.499 | 1.764 | 0.093 | -14.343 171.301 |
| C(dose)[T.1] | 51.3081 | 9.452 | 5.428 | 0.000 | 31.591 71.025 |
| expression | -4.3773 | 7.952 | -0.550 | 0.588 | -20.964 12.210 |
| Omnibus: | 0.962 | Durbin-Watson: | 1.767 |
| Prob(Omnibus): | 0.618 | Jarque-Bera (JB): | 0.797 |
| Skew: | 0.138 | Prob(JB): | 0.671 |
| Kurtosis: | 2.131 | Cond. No. | 57.1 |
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: | 04:38:13 | 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: | 0.145 |
| Model: | OLS | Adj. R-squared: | 0.104 |
| Method: | Least Squares | F-statistic: | 3.561 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0730 |
| Time: | 04:38:13 | Log-Likelihood: | -111.30 |
| 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 | 192.6060 | 60.189 | 3.200 | 0.004 | 67.435 317.777 |
| expression | -21.2078 | 11.238 | -1.887 | 0.073 | -44.578 2.162 |
| Omnibus: | 0.519 | Durbin-Watson: | 2.453 |
| Prob(Omnibus): | 0.772 | Jarque-Bera (JB): | 0.613 |
| Skew: | 0.281 | Prob(JB): | 0.736 |
| Kurtosis: | 2.431 | Cond. No. | 50.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.662 | 0.222 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.592 |
| Model: | OLS | Adj. R-squared: | 0.481 |
| Method: | Least Squares | F-statistic: | 5.322 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0165 |
| Time: | 04:38:13 | Log-Likelihood: | -68.575 |
| No. Observations: | 15 | AIC: | 145.2 |
| Df Residuals: | 11 | BIC: | 148.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -234.4637 | 155.512 | -1.508 | 0.160 | -576.743 107.816 |
| C(dose)[T.1] | 323.6711 | 183.904 | 1.760 | 0.106 | -81.099 728.442 |
| expression | 46.0427 | 23.665 | 1.946 | 0.078 | -6.044 98.130 |
| expression:C(dose)[T.1] | -41.4006 | 28.872 | -1.434 | 0.179 | -104.949 22.147 |
| Omnibus: | 4.261 | Durbin-Watson: | 1.536 |
| Prob(Omnibus): | 0.119 | Jarque-Bera (JB): | 2.041 |
| Skew: | -0.864 | Prob(JB): | 0.360 |
| Kurtosis: | 3.528 | Cond. No. | 238. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.516 |
| Model: | OLS | Adj. R-squared: | 0.435 |
| Method: | Least Squares | F-statistic: | 6.392 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0129 |
| Time: | 04:38:13 | Log-Likelihood: | -69.860 |
| No. Observations: | 15 | AIC: | 145.7 |
| Df Residuals: | 12 | BIC: | 147.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -52.0931 | 93.343 | -0.558 | 0.587 | -255.471 151.285 |
| C(dose)[T.1] | 61.0539 | 17.384 | 3.512 | 0.004 | 23.177 98.931 |
| expression | 18.2287 | 14.141 | 1.289 | 0.222 | -12.582 49.039 |
| Omnibus: | 0.693 | Durbin-Watson: | 1.010 |
| Prob(Omnibus): | 0.707 | Jarque-Bera (JB): | 0.269 |
| Skew: | -0.320 | Prob(JB): | 0.874 |
| Kurtosis: | 2.857 | Cond. No. | 81.8 |
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: | 04:38:13 | 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: | 0.018 |
| Model: | OLS | Adj. R-squared: | -0.057 |
| Method: | Least Squares | F-statistic: | 0.2404 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.632 |
| Time: | 04:38:13 | Log-Likelihood: | -75.163 |
| 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 | 143.6523 | 102.443 | 1.402 | 0.184 | -77.662 364.967 |
| expression | -8.0494 | 16.417 | -0.490 | 0.632 | -43.516 27.417 |
| Omnibus: | 0.137 | Durbin-Watson: | 1.470 |
| Prob(Omnibus): | 0.934 | Jarque-Bera (JB): | 0.336 |
| Skew: | -0.140 | Prob(JB): | 0.845 |
| Kurtosis: | 2.322 | Cond. No. | 65.1 |