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Zometric Statistical Software
Zometric Statistical software is one of the most intuitive and easy to use software in the market. While most other statistical software require deep knowledge of statistics, our software is designed keeping the needs of beginners and business users in mind.
Its ideal for users who want to:
- Quickly and easily analyze their data anywhere, anytime - its browser based!
- Analyze data for lean, six sigma or other continuous improvement projects.
- Analyze day-to-day quality control / assurance data
- Generate customer requested reports like SPC / Control charts / process capability / MSA
Statistical tools and features
Graphical analysis
| Graphical Tools | Application Hint |
|---|---|
| Pie Chart | Compare the proportion(or relative contribution) of data in each category or group. |
| Bar Chart | Compare statistics summary statistics, using bars to represent groups or categories. |
| Pareto Chart | Identify the significant / most frequency categories among many categories. |
| Boxplot | Visual representation of quartiles of data and outliers if any. |
| Density Heatmap | Visual representation of relationship between one or more categorical variables. |
| Scatter Plot 2D | Visual representation of relationship between a pair of continuous variables. |
| Correlation Analysis | Measure strength and direction of association between pairs of variables. |
| Scatter Plot 3D | Visualise relationship between a response variable (Z) and two predictor variables (X and Y) |
| Histogram (Flexi) | View distribution of datasets with continuous data and discrete categories. |
| Histogram Distplot | View distribution of data, and corresponding normal dist curve, categorized by group labels. |
| Out of Spec Estimator | Estimate probability of out of specification assuming normal distribution. |
| Timeline Chart | Graphical timeline showing relationship between the tasks and the milestones. |
| View normal probability | View relationship between cumulitive probabilities and x values for normal distribution |
| Descriptive statistics | Commonly used descriptive statistics |
| Graphical summary | Graphical summary of commonly used statistics |
| Multi-vari chart | Visualise relationship between one or more factors and a response variable |
| Run chart | Look for patterns in process data and test for nonrandom behaviour. |
SPC Control Charts
| SPC Tools | Application Hint |
|---|---|
| Box-Cox transformation | Transform non-normal data for control charts |
| Johnson transformation | When continuous data is collected in subgroups, with typical subgroup size 8 or less |
| Xbar-R chart | When continuous data is collected in subgroups, with typical subgroup size 8 or less |
| Xbar chart | Monitor the mean of your process when you have continuous data in subgroups |
| R Chart | Monitor the variation(range) of your process when you have continuous data in subgroups.Works best with subgroup sizes of 8 or less |
| Xbar-S-Chart | When continuous data is collected in subgroups, with typical subgroup greater than 8 |
| S chart | Monitor the variation(standard deviation) of your process when you have continuous data in subgroups |
| I-MR R/S(Between/Within) chart | Monitor the mean of your process and the variation between and within subgroups when each subgroup is a different part or batch. |
| I-MR Chart | When continuous data is collected as individual samples (subgroups size = 1) |
| Z-MR chart | Monitor the mean and the variation (Moving range) of different parts when relatively few units are made for each parts as in short-run processes |
| P Chart | When proportion of defectives (or binomial events) are tracked |
| Laney P chart | Create a p chart that correct for over dispersion or under dispersion. |
| Laney U chart | Create a u chart that correct for over dispersion or under dispersion. |
| NP Chart | When defectives (or binomial events) on a constant sample size are tracked |
| U Chart | When defects per unit (or Poisson events per opportunity) are tracked |
| C Chart | When defects (or Poisson events) on a constant opportunity are tracked |
Process Capability
| Process Capability Tool | Application Hint |
|---|---|
| Machine Capability (Normal) | Assess and evaluate the performance and effectiveness of a machine or manufacturing process |
| Normal Process Capability | Determine how well a process output meets customer specifications, assuming normal distribution |
| Non Normal Process capability | Determine how well process output meets customer specifications when data don't follow normal distribution |
| Capability Six Pack (Normal) | Statistical measure used to assess the performance and consistency of a process |
| Capability six pack between/within | Assess the stability, capability and normality of a process that has systemic between-subgroup variation, such as batch process. |
| Poisson capability | Determine whether the rate of defects per unit(DPU) meet customer requirements, Use when each item can have more than one defect. |
| Binomial capability | Determine whether the percentage of defective items meet customer requirements, Use when each item is classified into one of the two categories, such as pass or fail. |
Hypothesis Tests
| Hypothesis Testing tools | Application Hint |
|---|---|
| One sample z-test | Determine whether the mean of a population differs significantly from a specified value, when the population standard deviation is known. |
| One sample z test for summarized data | Determine whether the mean of a population differs significantly from a specified value, when the population standard deviation is known. |
| One sample t test | Determine whether the mean of a population differs significantly from a specified value, when the population standard deviation is unknown. |
| One sample t test for summarized data | Determine whether the mean of a population differs significantly from a specified value, when the population standard deviation is unknown. |
| Two sample t test | Determine whether the mean of two samples differs significantly from each other. |
| Two sample t test for summarized data | Determine whether the mean of two samples differs significantly from each other. |
| Paired t test | Determine whether the mean of differences between two paired samples differs significantly from a hypothesized differences. |
| One sample proportion test | Determine whether the population proportion differs from a specified hypothesized proportion. |
| Two sample proportion test | Determine whether the population proportion of two groups differ. |
| One Variance Test | Determine whether the variances of two or more groups or samples are significantly different from each other. |
| Two variance test | Determine whether the variances of two independent samples are significantly different from each other. |
| Normality test | Determine whether a set of continious data (eg: length, weight) follows a normal distribution. |
| Covariance | Calculate the variances of variables and ovariances of each pair. |
| Outlier test | Test for an outlier in a sample using Grubbs test. |
| Chi square goodness of fit | Determine whether the proportion of items in each category is significantly different from the specified proportions. |
| Poisson goodness of fit | Determine whether your data follow a Poisson distribution. Use this test when you count occurrences, such as the number of defects per unit. |
| Bootstrap 1-sample | Explore sampling distribution of a specified statistic of a sample of data, and estimate a confidence interval for the population parameter. |
| Individual distribution identification* | Test for the best fit of your data from among multiple theoritical distributions. *This tool is currently in beta. |
Anova
| Anova Tool | Application Hint |
|---|---|
| One Way ANOVA | Determine whether the population means of two or more groups differ. |
| One way ANOVA(Response data are in a separate column for each factor level) | Determine whether the population means of two or more groups differ. |
| Test of equal variances | Determine whether variances of two or more groups differ. |
| Main effects plot | Examine differences between level means for one or more factors. |
Measurement System Analysis
| MSA Tools | Application Hint |
|---|---|
| Type 1 GRR | Evaluate the effects of bias + repeatability from multiple measurements of one part. Typically done before a gage R&R study. |
| Linearity & Bias | Assess the linear relationship and evaluate systematic errors |
| Crossed GRR | Assess the variation in your measurement system when every operator measures every part in the study in a balanced design. |
| Crossed GRR (AIAG format) | Assess the variation in your measurement system when every operator measures every part in the study in a balanced design. Data capture is in format presented in AIAG manual. |
| Nested GRR | Assess the variation in your measurement system when every operator cannot measures all parts. |
| Attribute Agreement | Assess agreement among raters in assigning attributes or categories to items. |
| Attribute Gauge Study | Evaluate your attribute measurement device such as a go/no-go gage is accurate and consistent using analytic method documented by AIAG MSA manual . |
Regression
| Regression Tool | Application Hint |
|---|---|
| Fitted line model | Model the relationship between categorical or continuous predictors and one response. |
| Fit Regression Model | Model the relationship between categorical or continuous predictors and one response. |
| Stability study | Analyze the stability of a product over time and determine it's shelf life. Establish the relationship between the response variable, time, and an optional batch factor using linear regression. |
| Ordinal logistic regression | Model the relationship between predictors and a response that has three or more outcome that have an order such as low, medium and high. |
| Nominal logistic regression | Model the relationship between predictors and a response that has three or more outcome that donot have an order. |
| Binary logistic regression | Perform logistic regression on binary response. |
| Orthogonal regression | Model the relationship between one response and one predictor when the measurements of both the response and the predictor include random error. |
Design Of Experiments
| DOE Tool | Application Hint |
|---|---|
| Create definitive screening | Create definitive screening experiment to identify significant factors from upto 48 factors. |
| Analyse definitive screening | Analyse results of definitive screening experiment, and identify significant factors from upto 48 factors. |
| Create & analyse factorial DoE | Create and analyse a designed experiment to study the effects of 2 to 15 factors. |
| Response optimizer | Identify input settings that optimizes, minimizes or maximizes one or more responses. |
Sampling
| Sampling Tool | Application Hint |
|---|---|
| AQL Sampling Plans | AQL based attribute sampling plans, as per ANSI/ASQ Z1.4 standard. |
| Variable AQL sampling plans | AQL based Sampling plans for inspection by variables. Ref: ANSI/ASQ Z1.9 & IS 2500-2 standards. |
| Create variable acceptance sampling | Create sampling plan to accept/reject lots based on variable characterstics. |
| Compare Variable acceptance sampling | Compare multiple variable sampling plans. Understand how varying the sample size and the critical distance affects the plan risk |
Non-Parametric
| Non-Parametric Tool | Application Hint |
|---|---|
| Kruskal Wallis | Determine whether the median of 2 or more groups differs when the data for all the groups have simlarly shaped distributions |
Random Data
| Random Data Tool | Application Hint |
|---|---|
| Random normal data | Generate random data with Normal distribution |
| Random binomial data | Generate random data with Binomial distribution |
| Random poisson data | Generate random data with Poisson distribution |
| Random exponential data | Generate random data with Exponential distribution |
| Random lognormal data | Generate random data with Lognormal distribution |
| Random F data | Generate random data with F distribution |
| Random T data | Generate random data with T distribution |
| Random weibull data | Generate random data with Weibull distribution |
Multivariate
| Multivariate Tool | Application Hint |
|---|---|
| Principal components | Create fewer variables (principal components) as linear combinations of of the original variables that explain maximum amount of variation. |
Reliability
| Reliability Tool | Application Hint |
|---|---|
| Demonstration Test Plans | Determine the sample size or the testing time that you need to demonstrate that your reliability exceeds a specific standard |
| Estimation Test Plans | Determine the sample size that you need to estimate reliability parameters |
| Pre Process Warranty Data | convert shipping and warranty return data into a standard reliability data from of failures and suspension. |
| Parametric Growth Curve | Determine whether system failures becoming more frequent,less frequent or remaining constant using power-law process or homogeneous poisson process. |
| Warranty Prediction | Fit a parametric distribution to pre-processed warranty data and predict the number or cost of future warranty claims. |
| Parametric Distribution Analysis- (Right Censoring) | Fit a parametric distribution to failure time data and evaluate the reliability of your product by estimating parametrs for the distribution. You can also evaluate the overall reliability of your system if there are multiple causes of failures. |
System requirements
Zometric Statistical Software is a browser based software. All you need is a modern web browser like Google Chrome and an internet connection. No setup / installation is required.
Licensing Options
Zometric Statistical Software is available in two licensing options:
- Single named user subscription. This is ideal for individuals, or organizations with one user.
- Concurrent login limit subscription. This is a cost effective option for organizations with multiple-users.
Contact us for a free demo and consultation.
Pricing
Zometric Statistical software is meant to be affordable for all. Our concurrent login limit subscriptions allows you to add users up to 5X the number of concurrent login-limits.