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 |
0.080 | 0.780 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.675 |
Model: | OLS | Adj. R-squared: | 0.624 |
Method: | Least Squares | F-statistic: | 13.17 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.95e-05 |
Time: | 05:02:59 | Log-Likelihood: | -100.17 |
No. Observations: | 23 | AIC: | 208.3 |
Df Residuals: | 19 | BIC: | 212.9 |
Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 182.8264 | 167.804 | 1.090 | 0.290 | -168.392 534.045 |
C(dose)[T.1] | -195.0758 | 208.285 | -0.937 | 0.361 | -631.022 240.871 |
expression | -11.5374 | 15.043 | -0.767 | 0.453 | -43.023 19.948 |
expression:C(dose)[T.1] | 23.1243 | 19.193 | 1.205 | 0.243 | -17.046 63.295 |
Omnibus: | 0.321 | Durbin-Watson: | 1.919 |
Prob(Omnibus): | 0.852 | Jarque-Bera (JB): | 0.484 |
Skew: | -0.185 | Prob(JB): | 0.785 |
Kurtosis: | 2.393 | Cond. No. | 714. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.650 |
Model: | OLS | Adj. R-squared: | 0.615 |
Method: | Least Squares | F-statistic: | 18.61 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.72e-05 |
Time: | 05:02:59 | Log-Likelihood: | -101.02 |
No. Observations: | 23 | AIC: | 208.0 |
Df Residuals: | 20 | BIC: | 211.4 |
Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 24.4604 | 105.487 | 0.232 | 0.819 | -195.583 244.503 |
C(dose)[T.1] | 55.4961 | 11.620 | 4.776 | 0.000 | 31.257 79.735 |
expression | 2.6685 | 9.447 | 0.282 | 0.780 | -17.038 22.375 |
Omnibus: | 0.238 | Durbin-Watson: | 1.885 |
Prob(Omnibus): | 0.888 | Jarque-Bera (JB): | 0.432 |
Skew: | 0.091 | Prob(JB): | 0.806 |
Kurtosis: | 2.354 | Cond. No. | 263. |
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: | 05:02:59 | 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: | 0.252 |
Model: | OLS | Adj. R-squared: | 0.216 |
Method: | Least Squares | F-statistic: | 7.067 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0147 |
Time: | 05:02:59 | Log-Likelihood: | -109.77 |
No. Observations: | 23 | AIC: | 223.5 |
Df Residuals: | 21 | BIC: | 225.8 |
Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 370.3547 | 109.506 | 3.382 | 0.003 | 142.624 598.086 |
expression | -27.0085 | 10.160 | -2.658 | 0.015 | -48.137 -5.880 |
Omnibus: | 1.516 | Durbin-Watson: | 2.050 |
Prob(Omnibus): | 0.469 | Jarque-Bera (JB): | 1.345 |
Skew: | 0.522 | Prob(JB): | 0.510 |
Kurtosis: | 2.439 | Cond. No. | 191. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
F-statistic | p-value | df difference |
3.501 | 0.086 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.600 |
Model: | OLS | Adj. R-squared: | 0.491 |
Method: | Least Squares | F-statistic: | 5.509 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0148 |
Time: | 05:02:59 | Log-Likelihood: | -68.421 |
No. Observations: | 15 | AIC: | 144.8 |
Df Residuals: | 11 | BIC: | 147.7 |
Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 123.2128 | 112.225 | 1.098 | 0.296 | -123.793 370.219 |
C(dose)[T.1] | 167.1006 | 142.736 | 1.171 | 0.266 | -147.058 481.260 |
expression | -6.0747 | 12.170 | -0.499 | 0.628 | -32.861 20.711 |
expression:C(dose)[T.1] | -13.5580 | 15.695 | -0.864 | 0.406 | -48.102 20.986 |
Omnibus: | 0.445 | Durbin-Watson: | 1.368 |
Prob(Omnibus): | 0.801 | Jarque-Bera (JB): | 0.526 |
Skew: | -0.112 | Prob(JB): | 0.769 |
Kurtosis: | 2.110 | Cond. No. | 260. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.573 |
Model: | OLS | Adj. R-squared: | 0.502 |
Method: | Least Squares | F-statistic: | 8.061 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00604 |
Time: | 05:02:59 | Log-Likelihood: | -68.913 |
No. Observations: | 15 | AIC: | 143.8 |
Df Residuals: | 12 | BIC: | 145.9 |
Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 198.0731 | 70.549 | 2.808 | 0.016 | 44.360 351.786 |
C(dose)[T.1] | 44.4134 | 14.082 | 3.154 | 0.008 | 13.730 75.096 |
expression | -14.2266 | 7.603 | -1.871 | 0.086 | -30.792 2.339 |
Omnibus: | 1.753 | Durbin-Watson: | 0.955 |
Prob(Omnibus): | 0.416 | Jarque-Bera (JB): | 1.025 |
Skew: | -0.290 | Prob(JB): | 0.599 |
Kurtosis: | 1.858 | Cond. No. | 93.8 |
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: | 05:02:59 | 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: | 0.220 |
Model: | OLS | Adj. R-squared: | 0.160 |
Method: | Least Squares | F-statistic: | 3.658 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0781 |
Time: | 05:02:59 | Log-Likelihood: | -73.441 |
No. Observations: | 15 | AIC: | 150.9 |
Df Residuals: | 13 | BIC: | 152.3 |
Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 260.9496 | 87.929 | 2.968 | 0.011 | 70.991 450.908 |
expression | -18.5792 | 9.715 | -1.912 | 0.078 | -39.567 2.408 |
Omnibus: | 0.204 | Durbin-Watson: | 2.100 |
Prob(Omnibus): | 0.903 | Jarque-Bera (JB): | 0.026 |
Skew: | 0.006 | Prob(JB): | 0.987 |
Kurtosis: | 2.796 | Cond. No. | 89.7 |