Procedure: Logit / Probit / Weibull
UNISTAT statistical package extends Excel with Logit / Probit / Weibull support.
Sample output
Here we provide annotated example report output from the UNISTAT Excel add-in for data analysis.
Logit / Probit / Weibull
Regression Results
Model selected: Logit
Valid Number of Cases: 16, 0 Omitted
Response Variable: Good
Subject Variable: Total
| Coefficient | Standard Error | Z-Statistic | 1-Tail Probability | Lower 95% | Upper 95% |
| Constant | -1.4604 | 0.0964 | -15.1490 | 0.0000 | -1.6494 | -1.2715 |
| A | 0.6498 | 0.1154 | 5.6298 | 0.0002 | 0.4236 | 0.8760 |
| B | 0.3101 | 0.1222 | 2.5377 | 0.0276 | 0.0706 | 0.5496 |
| C | 0.9806 | 0.1107 | 8.8560 | 0.0000 | 0.7636 | 1.1976 |
| D | 0.4204 | 0.1910 | 2.2011 | 0.0500 | 0.0461 | 0.7946 |
| -2 Log likelihood | 2104.1204 |
| Goodness of Fit: |
| Chi-Square Statistic | 13.6067 |
| Degrees of Freedom | 11 |
| Right-Tail Probability | 0.2555 |
Expected Frequencies
| Row | Observed Responses | Expected Responses | Residuals | Probability |
| 1 | 84.0000 | 89.8673 | -5.8673 | 0.1884 |
| 2 | 75.0000 | 71.0915 | 3.9085 | 0.3078 |
| 3 | 13.0000 | 15.1470 | -2.1470 | 0.2404 |
| 4 | 35.0000 | 35.4771 | -0.4771 | 0.3774 |
| 5 | 67.0000 | 57.3442 | 9.6558 | 0.3823 |
| 6 | 201.0000 | 205.0245 | -4.0245 | 0.5424 |
| 7 | 16.0000 | 14.6455 | 1.3545 | 0.4577 |
| 8 | 102.0000 | 104.4028 | -2.4028 | 0.6178 |
| 9 | 2.0000 | 3.1336 | -1.1336 | 0.2611 |
| 10 | 7.0000 | 5.2474 | 1.7526 | 0.4036 |
| 11 | 4.0000 | 2.2764 | 1.7236 | 0.3252 |
| 12 | 8.0000 | 5.7596 | 2.2404 | 0.4800 |
| 13 | 3.0000 | 5.3365 | -2.3365 | 0.4851 |
| 14 | 27.0000 | 28.9549 | -1.9549 | 0.6434 |
| 15 | 1.0000 | 2.2493 | -1.2493 | 0.5623 |
| 16 | 23.0000 | 22.0422 | 0.9578 | 0.7110 |
Correlation Matrix of Regression Coefficients
| Constant | A | B | C | D |
| Constant | 1.0000 | -0.5095 | -0.1961 | -0.3929 | -0.0716 |
| A | -0.5095 | 1.0000 | -0.1534 | -0.2952 | -0.0430 |
| B | -0.1961 | -0.1534 | 1.0000 | -0.0014 | -0.0810 |
| C | -0.3929 | -0.2952 | -0.0014 | 1.0000 | -0.0569 |
| D | -0.0716 | -0.0430 | -0.0810 | -0.0569 | 1.0000 |
Covariance Matrix of Regression Coefficients
| Constant | A | B | C | D |
| Constant | 0.0093 | -0.0057 | -0.0023 | -0.0042 | -0.0013 |
| A | -0.0057 | 0.0133 | -0.0022 | -0.0038 | -0.0009 |
| B | -0.0023 | -0.0022 | 0.0149 | -0.0000 | -0.0019 |
| C | -0.0042 | -0.0038 | -0.0000 | 0.0123 | -0.0012 |
| D | -0.0013 | -0.0009 | -0.0019 | -0.0012 | 0.0365 |
Logit / Probit / Weibull
Regression Results
Model selected: Probit
Valid Number of Cases: 16, 0 Omitted
Response Variable: Good
Subject Variable: Total
| Coefficient | Standard Error | Z-Statistic | 1-Tail Probability | Lower 95% | Upper 95% |
| Constant | -0.8933 | 0.0561 | -15.9286 | 0.0000 | -1.0032 | -0.7833 |
| A | 0.3963 | 0.0698 | 5.6740 | 0.0001 | 0.2594 | 0.5332 |
| B | 0.1890 | 0.0749 | 2.5238 | 0.0283 | 0.0422 | 0.3359 |
| C | 0.6027 | 0.0675 | 8.9292 | 0.0000 | 0.4704 | 0.7350 |
| D | 0.2584 | 0.1169 | 2.2106 | 0.0492 | 0.0293 | 0.4876 |
| -2 Log likelihood | 2103.5279 |
| Goodness of Fit: |
| Chi-Square Statistic | 12.9949 |
| Degrees of Freedom | 11 |
| Right-Tail Probability | 0.2937 |
Logit / Probit / Weibull
Regression Results
Model selected: Weibull
Valid Number of Cases: 16, 0 Omitted
Response Variable: Good
Subject Variable: Total
| Coefficient | Standard Error | Z-Statistic | 1-Tail Probability | Lower 95% | Upper 95% |
| Constant | -1.5228 | 0.0821 | -18.5421 | 0.0000 | -1.6838 | -1.3619 |
| A | 0.5113 | 0.0937 | 5.4590 | 0.0002 | 0.3277 | 0.6949 |
| B | 0.2301 | 0.0880 | 2.6139 | 0.0241 | 0.0576 | 0.4027 |
| C | 0.7574 | 0.0886 | 8.5528 | 0.0000 | 0.5839 | 0.9310 |
| D | 0.2878 | 0.1286 | 2.2388 | 0.0468 | 0.0358 | 0.5398 |
| -2 Log likelihood | 2106.3834 |
| Goodness of Fit: |
| Chi-Square Statistic | 15.9936 |
| Degrees of Freedom | 11 |
| Right-Tail Probability | 0.1414 |