SolverStat Home

• Regression Statistics
  Analysis of Variance: model, residual, lack of fit, pure error and total SS, d.f., MS,
  R, LCI and UCI for R, R², R²adjusted,
  Regression Coefficients Statistic: standard error, calculated z or t ,critical z or t, LCI, UCI, Passed Probability, CV%,
  F or chi² Joint Confidence Coefficients Statistic,
  Variance-Covariance Table and Hessian Variance-Covariance Table for Parameters
  Coefficients Correlation Table;
  Linssen's Modification of Beale's Measure of NonLinearity,
• Observation Statistics (CI based on t or z Statistic)
  Residuals Standard Error;
  Mean LCI and UCI,
  Prediction Standard Error,
  Prediction LCI and UCI,
  Leverage,
  Standard Residuals,
  PRESS Residuals,
  Studentized Residuals,
  Studentized Deleted Residuals,
  Cook's Distance,
  DFFITS,
  COVRATIO,
  DFBETAS,
  Z Expected Under Normality for Residuals;
• New Observation Statistics (CI based on t or z Statistic)
  Standard Error,
  Mean LCI and UCI,
  Prediction Standard Error,
  Prediction LCI and UCI,
  Passed Probability for Sample Mean,
  Passed Probability for Single Observation;
• Residual Statistics
  Durbin Watson Statistics for Autocorrelation of Residuals  (Bounds value, Beta Aproximation probability, a + bDu Aproximation probability),
  Box Pierce Ljung Q' Test for Autocorrelation Of High Order,
  Autoregressive Order Q'Residuals Runs Test,
  Shapiro-Wilk Statistic for Normality of Residuals,
  Lilliefors Statistic for Normality of Residual,
  Jarque-Bera Statistic for Normality of Residuals,
  Anderson-Darling Statistic for Normality of Residuals,
  D'Agostino Omnibus Statistic ,D'A. Kurtosis D'A.Skewness,
  Modified Levene Test for Constant Variance (2 groups) and Multiple Levene Test for Constant Variance (3 groups),
  Breush-Pagan Test for Homoscedasticity of Residuals,
  Koenkar-Evans NR² Test for Homoscedasticity of Residuals;
• Collinearity Diagnostics
  Regressors Correlation Matrix,
  Condition Number of the Regressors Correlation Matrix,
  VIF and Tolerance;
• Model selection
  PRESS-Predictive Error Sum of Squares,
  MEP-Mean Error of Prediction,
  R² prediction,
  SDEC-Standard Deviation of Error of Calculations,
  SDEP-Standard Deviation of Error of Predictions,
  AIC-Akaike Information Criteria,
  AICc-Adjusted Akaike Information Criteria,
  HQC-Hannan Quinn Information Criteria,
  SBC-Schwarz Baynesian Information Criteria,
  F Test for Models Comparison,
  Extra Sum F Test for Significance of Extra Parameters,
  Models Comparison using Information Criterion,
  Log Likelihood Tests for Nested Models,

• Parameters Statistics
  Individual Asymptotic Sigma,
  Simultaneus Asymptotic LCI and UCI,
  Simultaneus Asymmetric LCI and UCI.

• Asymptotic Statistic
  Individual Asymptotic Standard Deviation
  Individual Asymptotic LCI and UCI,
  Simultaneus Asymptotic LCI and UCI;
• Distribution Statistics
  mean of parameters distribution,
  variance,
  standard deviation,
  Sample LCI and UCI,
  standard error of mean,
  t Value,
  Probability Level,
  mean LCI and UCI,
  Median,
  left percentile standard deviation,
  right percentile standard deviation,
  percentile LCI and UCI,
  1, 2.5, 5, 10 , 90, 95, 97.5, 99 percentile,
  maximum,
  minimum,
  Shapiro-Wilk Statistic for Normality of Residuals,
  Lilliefors Statistic for Normality of Residuals,
  Jarque-Bera Statistic for Normality of Residuals,
  Anderson-Darling Statistic for Normality of Residuals,
  D'Agostino Omnibus Statistic ,D'A. Kurtosis D'A.Skewness,
  Histogram Data;
• Tracks
  distribution of selected simulated observations, calculated responses, new observations.

• Asymptotic Statistic
  Individual Asymptotic Standard Deviation
  Individual Asymptotic LCI and UCI,
  Simultaneus Asymptotic LCI and UCI;
• Distribution Statistics
  bias,
  mean of parameters distribution,
  variance,
  standard deviation,
  Sample LCI and UCI,
  standard error of mean,
  t Value,
  Probability Level,
  mean LCI and UCI,
  Median,
  left percentile standard deviation,
  right percentile standard deviation,
  percentile LCI and UCI,
  1, 2.5, 5, 10 , 90, 95, 97.5, 99 percentile,
  maximum,
  minimum,
  Shapiro-Wilk Statistic for Normality of Residuals,
  Lilliefors Statistic for Normality of Residuals,
  Jarque-Bera Statistic for Normality of Residuals,
  Anderson-Darling Statistic for Normality of Residuals,
  D'Agostino Omnibus Statistic ,D'A. Kurtosis D'A.Skewness,
  Histogram Data;

• Tracks
  distribution of selected simulated observations, calculated responses, new observations.

• Logistic Regression Statistics
  Log Likelihood of Fitted Model and Intercept-Only Model,
  G and Deviance,
  Pseudo R², Cox & Snell R², Nagelkerke R²,
  AIC and BIC,
  Models Comparison using Information Criterion,
  Hosmer-Lemeshow Goodness of Fit Test,
  Contingency Table for the Hosmer-Lemeshow Test,
  Hosmer-Lemeshow Classification Table,
  Area under the ROC Curve,
  Association Measure Table (pairs, % concordant, % discordant, % tied, Somers' D, Goodman-Kruskal Gamma, Kendall's Tau-a , Concordance index c),
  Parameters Statistic and Wald Test,
  Variance-Covariance and Correlation Table for Parameters,
  Observation Diagnostic.

• Poisson Regression Statistics
  Log Likelihood of Fitted Model and Intercept-Only Model, Maximum, Deviance, Dispersion Phi ,
  Pearson R², Adjusted Pseudo R^2 
  AIC and BIC,
  Models Comparison using Information Criterion,
  Parameters Statistic and Wald Test,
  Variance-Covariance and Correlation Table for Parameters,
  Observation Diagnostic.

• PCA Statistic
  Descriptive Statistics: mean, variance, sd,
  Correlation and Covariance Matrix,
  Eigenvalues with percent, cumulative percent and the IND of Malinowsky,
  Loadings (Eigenvectors),
  Correlation of Principal Components with Original Variables,
  Principal Component Scores from Correlation Matrix,
  Residuals analysis: Hotelling T² and Hawkins HT²,
  Plots: Loadings, Scores and JK-Biplot.

• Univariate Statistic
  Mean,
  Variance,
  SD,
  Sample LCI and UCI,
  SEM,
  t Value,
  Mean LCI and UCI,
  SD LCI  and UCI,
  SEM LCI and UCI,
  Median,
  Percentile SD,
  Percentile LCI  and UCI ,
  Maximum and Minimum,
  Normality Test Probability: Shapiro-Wilk, Lilliefors, Anderson-Darling, Jarque-Bera,
  D'Agostino Kurtosis, Skewness, Omnibus,
  Skewness, Kurtosis,
  Fisher Skewness g1 Kurtosis g2,
  Histogram Data.

• June 28, 2003 Release
  Fixed a bug in the computation of Lack of Fit.
• February 05, 2004 Release
  Fixed a bug in the use of Weights<1.
• December 27, 2008 Release
  Released version 2.0 as open source.
  Added new functionality and corrected minor bugs.
• March 18, 2009 Release
  Fixed an istallation problem.
• September 20, 2009 Release
  - The VBA function Val() was replaced with the function Cdblx() to solve some problem with the use of comma as decimal separator.
  - Fixed a problem with the output of min and max in the descriptive statistic routine and added a entry "Range".
  - Fixed a problem with the excel function "Application.ChiInv(df,p)" for df>1000; in that cases, now SolverStat uses the function df*FInv(df,1E+09).
• February 24, 2010 Release
  - Fixed some problems with decimal separator introduced in previous release.
  - Improved the computation speed of regression statistics (in some cases from minutes to seconds).
  - Introduced the Association Measure Table for logistic regression.
  - SolverStat now can configure the Excel Solver.
  - New guided procedure for logistic regression.
  - Fixed a problem with FormulaLocal in Bootstrap procedure.
• August 12, 2010 Release
  - Fixed some minor bugs.
  - Added the Poisson regression procedure.
  - First release of The Manual.
• September 10, 2010 Release
  - Revised outliers procedure and added the Grubb's test.
  - New release of The Manual (Proof).
• August 19, 2019 Release
  - Conway-Maxwell-Poisson Regression.
  - Monte Carlo Worksheet Recording (mcmon).
  - Minor bug fixes and improvements.