Data analysis simplified.
AcaStat is an inexpensive and easy-to-use data analysis tool. Easily create data files or import data from spreadsheets or delimited text files. Run crosstabulations, descriptive statistics, correlation, and common significance tests without learning programming code or complex interface commands.
The Mac version may be purchased through this site or from the Mac App Store.
Requires Windows 7 SP1 or later — Mac OS X 10.9.5 or later.
AcaStat for Windows screenshots
- AcaStat Spec Sheet
- Compare to Clarity and StatCalc
- User Manual
- Intuitive data analysis interface
- Drag and drop variable selection
- Running record of analyses
- Data file capacity ~200,000 observations
- 18 statistical procedures for spreadsheet data
- 18 statistical procedures for summary data
- Format, compute and recode variables
- Create mean centered variables
- Split data file module for reducing number of variables
- Create, save, export delimited or AcaStat data files
- Open saved data files
- Paste or drag and drop spreadsheet data
- Import delimited text files
- Random sampling module
- Weight procedure (frequencies and descriptives)
- Charts module
- Filter (exclude) observations
- Merge data files by records and variables
- Print and save output
- Convert output to tab delimited for pasting into spreadsheets (aligns spreadsheet columns with output)
- Drag and drop data and output files into AcaStat
- Example data files with variable formatting
- Statistics glossary module
- Quick Start and User Manuals
- On-line How To Guide
Data Module Statistics
- Frequencies tables - chi-square goodness of fit
- List observations - up to ten variables
- Descriptives - count, sum, minimum, maximum, mean, median, range, population and sample variance, population and sample deviation, skewness, standard error, coefficient of variation, 95% mean confidence interval, one-sample t-test
- Explore descriptive statistics - by subgroups
- Crosstabulations - cell count, row %, col %, total %, chi-square,Yate's correction, Fisher's, odds ratio, relative risk, Cramer's V, Pearson C, lambda, kappa, McNemar's Test
- Independent and Paired T-test of Means - homogeneity of variance
- Wilcoxon Signed-Rank
- Mann-Whitney U
- One-way ANOVA - Bonferroni post hoc, eta, eta squared, Levene Test for variance homogeneity
- Kruskal-Wallis Test
- Bivariate Correlation - Pearson and Spearman rho correlation and matrices
- Point-Biserial Correlation
- Simple and multiple regression (OLS) - summary statistics, output residuals and predictive values
- Simple and multiple logistic regression - summary statistics, output predictive values
- Diagnostic accuracy - area under the curve, sensitivity, specificity, positive and negative predictive values, likelihood ratios, kappa
- Appraisal Analysis – average price and appraisal value, ratios, price-related differential (PRD), coefficient of dispersion (COD), coefficient of variation (CV)
- Repeated Random Sampling – set sample size and number of iterations to create a data file of means
Summary Statistics Module
- Weighted Mean
- Confidence Intervals (Mean/Proportion)
- Goodness of Fit
- Chi-Square Test of Independence (6x6 table)
- Z-Test of Proportions (One/Two Sample)
- T-Test of Means (One/Two Sample)
- One-Way ANOVA (6 group means)
- Z, T, F, Chi-Sq Distributions (estimate p-values from critical values)
- Epidemic Curve
- Disaggregate Counts (creates a data file from summary statistics for use in the charts procedure)
Quickly summarize data.
Drag and drop variables and AcaStat automatically runs the selected statistical procedure. Key summary statistics are displayed in the Explore tab for quick review. Build regression models by incrementally adding variables to the model. Copy and paste contents into a spreadsheet or word processor to assist in report writing and editing.
Visualize summary data.
Automatically produce charts for five statistical procedures. A horizontal bar chart for frequencies, a histogram for descriptives, a scattergram for correlation, a histogram of residuals for OLS regression, and a ROC curve for diagnostic accuracy. Additional statistics are recorded in the Output Log.
Enhance your analyses.
The Options Panel displays variable list boxes, analysis options, and control settings. Select multiple variables for analysis, remove variables from analysis, set controls, and choose options such as saving regression residuals or changing output tables.
Create and edit data files.
Create and edit data files with hundreds of variables. The Format module is displayed in the Data tab to the left of the data spreadsheet. Set missing values and format up to 12 value labels. Use the Recode module to create new variables from ranges of values or compute/transform new variables.
Conventional statistical tables.
The Output Log displays the results of the analyses in a text window for editing, printing, and saving as a text file. Each time a statistical procedure is run, the results are automatically sent to the Output Log. Convert output to tab delimited format for pasting into spreadsheet software.
Visualize data patterns.
The Charts module produces graphic displays of data. The chart can be saved, printed, and copied by using the toolbar buttons or menu pull-down. The chart can also be formatted to adjust background, titles, and decimals. A control variable can be used to chart subgroups of data.
Compare Summary Statistics
Analyze counts, proportions and means.
The Summary Statistics module includes 16 procedures from StatCalc for analyzing summary data. Compare summary statistics, create confidence intervals, create Epi Curves, and verify hand calculations for common analytical methods taught in statistics courses.
Learn how to use statistical procedures.
AcaStat Help includes a Help index, Statistics Glossary, and two instructional modules. AcaStat Overview provides an annotated summary of key features. An on line How To Guide provides instruction on using the Options Panel variable boxes to run each statistical procedure. The instruction explains the purpose of the procedure, displays an example of the output, provides an example of the data format, and shows how to set up the analysis list boxes.
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