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 |
| 0.496 | 0.489 | 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: | 12.43 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.95e-05 |
| Time: | 05:19:35 | Log-Likelihood: | -100.62 |
| No. Observations: | 23 | AIC: | 209.2 |
| Df Residuals: | 19 | BIC: | 213.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 180.4041 | 145.757 | 1.238 | 0.231 | -124.670 485.478 |
| C(dose)[T.1] | -64.0115 | 228.901 | -0.280 | 0.783 | -543.107 415.084 |
| expression | -15.6755 | 18.090 | -0.867 | 0.397 | -53.537 22.186 |
| expression:C(dose)[T.1] | 14.6127 | 27.861 | 0.524 | 0.606 | -43.701 72.926 |
| Omnibus: | 0.579 | Durbin-Watson: | 1.625 |
| Prob(Omnibus): | 0.749 | Jarque-Bera (JB): | 0.628 |
| Skew: | -0.112 | Prob(JB): | 0.731 |
| Kurtosis: | 2.222 | Cond. No. | 541. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 19.20 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.22e-05 |
| Time: | 05:19:35 | Log-Likelihood: | -100.78 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 20 | BIC: | 211.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 130.8114 | 108.898 | 1.201 | 0.244 | -96.345 357.968 |
| C(dose)[T.1] | 55.9389 | 9.417 | 5.940 | 0.000 | 36.295 75.583 |
| expression | -9.5153 | 13.506 | -0.705 | 0.489 | -37.689 18.658 |
| Omnibus: | 0.659 | Durbin-Watson: | 1.635 |
| Prob(Omnibus): | 0.719 | Jarque-Bera (JB): | 0.647 |
| Skew: | 0.004 | Prob(JB): | 0.724 |
| Kurtosis: | 2.179 | Cond. No. | 210. |
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: | 05:19:35 | 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: | 0.053 |
| Model: | OLS | Adj. R-squared: | 0.008 |
| Method: | Least Squares | F-statistic: | 1.185 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.289 |
| Time: | 05:19:35 | Log-Likelihood: | -112.47 |
| No. Observations: | 23 | AIC: | 228.9 |
| Df Residuals: | 21 | BIC: | 231.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -99.8291 | 165.074 | -0.605 | 0.552 | -443.120 243.462 |
| expression | 21.9460 | 20.159 | 1.089 | 0.289 | -19.977 63.869 |
| Omnibus: | 3.442 | Durbin-Watson: | 2.564 |
| Prob(Omnibus): | 0.179 | Jarque-Bera (JB): | 1.466 |
| Skew: | 0.193 | Prob(JB): | 0.480 |
| Kurtosis: | 1.825 | Cond. No. | 195. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.657 | 0.222 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.538 |
| Model: | OLS | Adj. R-squared: | 0.413 |
| Method: | Least Squares | F-statistic: | 4.277 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0313 |
| Time: | 05:19:35 | Log-Likelihood: | -69.502 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 11 | BIC: | 149.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 332.5976 | 184.117 | 1.806 | 0.098 | -72.640 737.836 |
| C(dose)[T.1] | -165.0412 | 284.712 | -0.580 | 0.574 | -791.688 461.606 |
| expression | -31.5278 | 21.852 | -1.443 | 0.177 | -79.624 16.568 |
| expression:C(dose)[T.1] | 25.3043 | 34.353 | 0.737 | 0.477 | -50.306 100.914 |
| Omnibus: | 3.488 | Durbin-Watson: | 1.241 |
| Prob(Omnibus): | 0.175 | Jarque-Bera (JB): | 1.851 |
| Skew: | -0.857 | Prob(JB): | 0.396 |
| Kurtosis: | 3.157 | Cond. No. | 407. |
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: | 6.388 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0129 |
| Time: | 05:19:35 | Log-Likelihood: | -69.863 |
| 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 | 246.4829 | 139.500 | 1.767 | 0.103 | -57.461 550.427 |
| C(dose)[T.1] | 44.3657 | 15.223 | 2.914 | 0.013 | 11.197 77.535 |
| expression | -21.2890 | 16.537 | -1.287 | 0.222 | -57.319 14.741 |
| Omnibus: | 4.200 | Durbin-Watson: | 1.191 |
| Prob(Omnibus): | 0.122 | Jarque-Bera (JB): | 2.088 |
| Skew: | -0.888 | Prob(JB): | 0.352 |
| Kurtosis: | 3.430 | Cond. No. | 160. |
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: | 05:19:35 | 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: | 0.173 |
| Model: | OLS | Adj. R-squared: | 0.109 |
| Method: | Least Squares | F-statistic: | 2.717 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.123 |
| Time: | 05:19:35 | Log-Likelihood: | -73.877 |
| No. Observations: | 15 | AIC: | 151.8 |
| Df Residuals: | 13 | BIC: | 153.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 368.6141 | 167.059 | 2.206 | 0.046 | 7.706 729.522 |
| expression | -33.1677 | 20.122 | -1.648 | 0.123 | -76.638 10.303 |
| Omnibus: | 1.486 | Durbin-Watson: | 2.112 |
| Prob(Omnibus): | 0.476 | Jarque-Bera (JB): | 1.036 |
| Skew: | 0.377 | Prob(JB): | 0.596 |
| Kurtosis: | 1.956 | Cond. No. | 152. |