ROV BIZSTATS is an applied statistics toolkit that is focused on user friendliness but is still powerful enough to solve most day-to-day statistical problems. As a standalone software, it will also work with the existing data in your spreadsheets, providing detailed reports complete with analytical results and in-depth explanations of the results.

The ROV BizStats software utilizes the following models and methods:

1. AI Machine Learning: Bagging Linear Fit Bootstrap Aggregation (Supervised)
2. AI Machine Learning: Bagging Nonlinear Fit Bootstrap Aggregation (Supervised)
3. AI Machine Learning: Classification Regression Trees CART (Supervised)
4. AI Machine Learning: Classification with Gaussian Mix & K-Means (Unsupervised)
5. AI Machine Learning: Classification with Gaussian SVM (Supervised)
6. AI Machine Learning: Classification with K-Nearest Neighbor (Supervised)
7. AI Machine Learning: Classification with Phylogenetic Tree & Hierarchical Clustering (Unsupervised)
8. AI Machine Learning: Classification with Linear SVM (Supervised)
9. AI Machine Learning: Classification with Polynomial SVM (Supervised)
10. AI Machine Learning: Custom Fit Model (Supervised)
11. AI Machine Learning: Dimension Reduction Factor Analysis (Unsupervised)
12. AI Machine Learning: Dimension Reduction Principal Component Analysis (PCA)
13. AI Machine Learning: Ensemble Common Fit (Supervised)
14. AI Machine Learning: Ensemble Common Fit (Supervised)
15. AI Machine Learning: Ensemble Time-Series (Supervised)
16. AI Machine Learning: Linear Fit Model (Supervised)
17. AI Machine Learning: Logistic Binary Classification (Supervised)
18. AI Machine Learning: Multivariate Discriminant Analysis (Linear) (Supervised)
19. AI Machine Learning: Multivariate Discriminant Analysis (Quadratic) (Supervised)
20. AI Machine Learning: Neural Network (Cosine Hyperbolic Tangent)
21. AI Machine Learning: Neural Network (Hyperbolic Tangent) (Supervised)
22. AI Machine Learning: Neural Network (Linear) (Supervised)
23. AI Machine Learning: Neural Network (Logistic) (Supervised)
24. AI Machine Learning: Normit Probit Binary Classification (Supervised)
25. AI Machine Learning: Random Forest (Supervised)
26. AI Machine Learning: Segmentation Clustering (Unsupervised)
27. ANCOVA (Single Factor Multiple Treatments)
28. ANOVA (MANOVA General Linear Model)
29. ANOVA (Randomized Blocks Multiple Treatments)
30. ANOVA (Single Factor Multiple Treatments)
31. ANOVA (Single Factor Repeated Measures)
32. ANOVA (Two-Way Analysis)
33. ANOVA (Two-Way MANOVA General Linear Model)
34. ARIMA
35. ARIMA Seasonal (SARIMA)
36. Auto ARIMA
37. Auto Econometrics (Detailed)
38. Auto Econometrics (Quick)
39. Autocorrelation and Partial Autocorrelation
40. Autocorrelation Durbin-Watson AR(1) Test
41. Bonferroni Test (Single Variable with Repetition)
42. Bonferroni Test (Two Variables with Repetition)
43. Box–Cox Normal Transformation
44. Box’s Test for Homogeneity of Covariance
45. Charts: 2D Area
46. Charts: 2D Bar
47. Charts: 2D Column
48. Charts: 2D Line
49. Charts: 2D Pareto
50. Charts: 2D Point
51. Charts: 2D Scatter
52. Charts: 3 Variables Bubble
53. Charts: 3D Area
54. Charts: 3D Bar
55. Charts: 3D Column
56. Charts: 3D Line
57. Charts: 3D Pareto
58. Charts: 3D Point
59. Charts: 3D Scatter
60. Charts: Box-Whisker
61. Charts: Fan Chart
62. Charts: Q-Q Normal
63. Coefficient of Variation Homogeneity Test
64. Cointegration Test (Engle-Granger)
65. Combinatorial Fuzzy Logic
66. Control Chart: C
67. Control Chart: NP
68. Control Chart: P
69. Control Chart: R
70. Control Chart: U
71. Control Chart: X
72. Control Chart: XMR
73. Convolution Simulation: Discrete Normal with Lognormal Arithmetic Scale
74. Convolution Simulation Discrete Normal Lognormal Logarithmic Scale
75. Convolution Simulation Poisson Frechet
76. Convolution Simulation Poisson Gumbel Max
77. Convolution Simulation Poisson Lognormal Arithmetic Scale
78. Convolution Simulation Poisson Lognormal Log Scale
79. Convolution Simulation Poisson Normal
80. Convolution Simulation Poisson Pareto
81. Convolution Simulation Poisson Weibull
82. Correlation Matrix (Linear, Nonlinear)
83. Covariance Matrix
84. Cox Regression
85. Cubic Spline
86. Custom Econometric Model
87. Data Analysis: Cross Tabulation
88. Data Analysis: New Values Only
89. Data Analysis: Pivot Table
90. Data Analysis: Subtotal by Category
91. Data Analysis: Unique Values Only
92. Data Descriptive Statistics
93. Deseasonalize
94. Discriminate Analysis (Linear)
95. Discriminate Analysis (Quadratic)
96. Distributional Fitting: ALL: Continuous
97. Distributional Fitting: Continuous (Akaike Information Criterion)
98. Distributional Fitting: Continuous (Anderson–Darling)
99. Distributional Fitting: Continuous (Kolmogorov–Smirnov)
100. Distributional Fitting: Continuous (Kuiper’s Statistic)
101. Distributional Fitting: Continuous (Schwarz/Bayes Criterion)
102. Distributional Fitting: Discrete (Chi-Square)
103. Diversity Index (Shannon, Brillouin, Simpson)
104. Eigenvalues and Eigenvectors
105. Endogeneity Test with Two Stage Least Squares (Durbin-Wu-Hausman)
106. Endogenous Model (Instrumental Variables with Two Stage Least Squares)
107. Error Correction Model (Engle-Granger)
108. Exponential J-Curve
109. Factor Analysis (PCA with Varimax Rotation)
110. Forecast Accuracy (All Goodness of Fit Measures)
111. Forecast Accuracy: Akaike, Bayes, Schwarz, MAD, MSE, RMSE
112. Forecast Accuracy: Diebold–Mariano (Dual Competing Forecasts)
113. Forecast Accuracy: Pesaran–Timmermann (Single Directional Forecast)
114. Generalized Linear Models (Logit with Binary Outcomes)
115. Generalized Linear Models (Logit with Bivariate Outcomes)
116. Generalized Linear Models (Probit with Binary Outcomes)
117. Generalized Linear Models (Probit with Bivariate Outcomes)
118. Generalized Linear Models (Tobit with Censored Data)
119. Granger Causality
120. Grubbs Test for Outliers
121. Heteroskedasticity Test (Breusch–Pagan–Godfrey)
122. Heteroskedasticity Test (Lagrange Multiplier)
123. Heteroskedasticity Test (Wald–Glejser)
124. Heteroskedasticity Test (Wald’s on Individual Variables)
125. Hodrick-Prescott Filter
126. Hotelling T-Square: 1 VAR with Related Measures
127. Hotelling T-Square: 2 VAR Dependent Pair with Related Measures
128. Hotelling T-Square: 2 VAR Indep. Equal Variance with Related Measures
129. Hotelling T-Square: 2 VAR Indep. Unequal Variance with Related Measures
130. Internal Consistency Reliability: Cronbach’s Alpha (Dichotomous Data)
131. Internal Consistency Reliability: Guttman’s Lambda and Split Half Model
132. Inter-rater Reliability: Cohen’s Kappa
133. Inter-rater Reliability: Inter-Class Correlation (ICC)
134. Inter-rater Reliability: Kendall’s W (No Ties)
135. Inter-rater Reliability: Kendall’s W (with Ties)
136. Inter-rater Reliability: Kuder Richardson
137. Kendall’s Tau Correlation (No Ties)
138. Kendall’s Tau Correlation (with Ties)
139. Linear Interpolation
140. Logistic S-Curve
141. Mahalanobis Distance
142. Markov Chain
143. Markov Chain Transition Risk Matrix
144. Multiple Poisson Regression (Population and Frequency)
145. Multiple Regression (Deming Regression with Known Variance)
146. Multiple Regression (Linear)
147. Multiple Regression (Nonlinear)
148. Multiple Regression (Ordinal Logistic Regression)
149. Multiple Regression (Through Origin)
150. Multiple Regression (Two-Variable Functional Form Tests)
151. Multiple Ridge Regression (Low Variance, High Bias, High VIF)
152. Multiple Weighted Regression (Fixing Heteroskedasticity)
153. Nominal Data Contingency Analysis (McNemar’s Marginal Homogeneity)
154. Nonparametric: Chi-Square GOF for Normality (Grouped Data)
155. Nonparametric: Chi-Square Independence
156. Nonparametric: Chi-Square Population Variance
157. Nonparametric: Cochran’s Q (Binary Repeated Measures)
158. Nonparametric: D’Agostino–Pearson Normality Test
159. Nonparametric: Friedman’s Test
160. Nonparametric: Kruskal–Wallis Test
161. Nonparametric: Lilliefors Test for Normality
162. Nonparametric: Mann–Whitney Test (Two Var)
163. Nonparametric: Mood’s Multivariate Median Test
164. Nonparametric: Runs Test for Randomness
165. Nonparametric: Shapiro–Wilk–Royston Normality Test
166. Nonparametric: Wilcoxon Signed-Rank Test (One Var)
167. Nonparametric: Wilcoxon Signed-Rank Test (Two Var)
168. Parametric: One Variable (T) Mean
169. Parametric: One Variable (Z) Mean
170. Parametric: One Variable (Z) Proportion
171. Parametric: Power Curve for T-Test
172. Parametric: Two Variable (F) Variances
173. Parametric: Two Variable (T) Dependent Mean
174. Parametric: Two Variable (T) Independent Equal Variances
175. Parametric: Two Variable (T) Independent Unequal Variances
176. Parametric: Two Variable (Z) Independent Means
177. Parametric: Two Variable (Z) Independent Proportions
178. Partial Correlations (Using Correlation Matrix)
179. Partial Correlations (Using Raw Data)
180. Principal Component Analysis
181. Process Capability (CPK, PPK)
182. Quick Statistic: Absolute Values (ABS)
183. Quick Statistic: Average (AVG)
184. Quick Statistic: Count
185. Quick Statistic: Difference
186. Quick Statistic: LAG
187. Quick Statistic: Lead
188. Quick Statistic: LN
189. Quick Statistic: LOG
190. Quick Statistic: Max
191. Quick Statistic: Median
192. Quick Statistic: Min
193. Quick Statistic: Mode
194. Quick Statistic: Power
195. Quick Statistic: Rank Ascending
196. Quick Statistic: Rank Descending
197. Quick Statistic: Relative LN Returns
198. Quick Statistic: Relative Returns
199. Quick Statistic: Semi-Standard Deviation (Lower)
200. Quick Statistic: Semi-Standard Deviation (Upper)
201. Quick Statistic: Standard Deviation Population
202. Quick Statistic: Standard Deviation Sample
203. Quick Statistic: Sum
204. Quick Statistic: Variance (Population)
205. Quick Statistic: Variance (Sample)
206. ROC Curves, AUC, and Classification Tables
207. Seasonality
208. Segmentation Clustering
209. Skew and Kurtosis: Shapiro–Wilk and D’Agostino–Pearson
210. Specifications Cubed Test (Ramsey’s RESET)
211. Specifications Squared Test (Ramsey’s RESET)
212. Stationarity: Augmented Dickey-Fuller
213. Stationarity: Dickey-Fuller (Constant and Trend)
214. Stationarity: Dickey-Fuller (Constant No Trend)
215. Stationarity: Dickey-Fuller (No Constant No Trend)
216. Stepwise Regression (Backward)
217. Stepwise Regression (Correlation)
218. Stepwise Regression (Forward)
219. Stepwise Regression (Forward-Backward)
220. Stochastic Process (Exponential Brownian Motion)
221. Stochastic Process (Geometric Brownian Motion)
222. Stochastic Process (Jump Diffusion)
223. Stochastic Process (Mean Reversion)
224. Stochastic Process (Mean Reverting and Jump Diffusion)
225. Structural Break (Chow Structural Stability Test)
226. Survival and Hazard Tables (Kaplan–Meier)
227. Time-Series Analysis (Auto)
228. Time-Series Analysis (Double Exponential Smoothing)
229. Time-Series Analysis (Double Moving Average Lag)
230. Time-Series Analysis (Double Moving Average)
231. Time-Series Analysis (Holt–Winters Additive)
232. Time-Series Analysis (Holt–Winters Multiplicative)
233. Time-Series Analysis (Seasonal Additive)
234. Time-Series Analysis (Seasonal Multiplicative)
235. Time-Series Analysis (Single Exponential Smoothing)
236. Time-Series Analysis (Single Moving Average)
237. Trend Line (Difference Detrended)
238. Trend Line (Exponential Detrended)
239. Trend Line (Exponential)
240. Trend Line (Linear Detrended)
241. Trend Line (Linear)
242. Trend Line (Logarithmic Detrended)
243. Trend Line (Logarithmic)
244. Trend Line (Moving Average Detrended)
245. Trend Line (Moving Average)
246. Trend Line (Polynomial Detrended)
247. Trend Line (Polynomial)
248. Trend Line (Power Detrended)
249. Trend Line (Power)
250. Trend Line (Rate Detrended)
251. Trend Line (Static Mean Detrended)
252. Trend Line (Static Median Detrended)
253. Value at Risk (VaR and CVaR)
254. Variances Homogeneity Bartlett’s Test
255. Volatility (EGARCH)
256. Volatility (EGARCH-T)
257. Volatility (GARCH)
258. Volatility (GARCH-M)
259. Volatility (GJR GARCH)
260. Volatility (GJR TGARCH)
261. Volatility (Log Returns)
262. Volatility (TGARCH)
263. Volatility (TGARCH-M)
264. Yield Curve (Bliss)
265. Yield Curve (Nelson–Siegel)