Improve your forecasting and decision-making by applying powerful statistical analysis to your data, directly in your Excel spreadsheet. With predictive modeling and forecasting techniques, you can gain the insight needed to make critical decisions with confidence for cases such as demand forecasting, pricing, portfolio allocation, load planning, sales forecasting, strategic planning, profit projections, and more.

Related pages ->When your organization has access to useful and potentially critical data, easy and effective analysis is a must. Competitive advantage can be gained with better knowledge of the potential future outcomes, group comparisons and by measuring dependency. A deep and accurate statistical and visual understanding of key variables is invaluable.

Statistical analysis and forecasting provide this with quantitative and graphical results to display to your key stakeholders.

Statistical analysis and forecasting provide this with quantitative and graphical results to display to your key stakeholders.

The Monte Carlo method is a computerized mathematical technique that allows people to quantitatively account for risk in forecasting and decision-making. At its core, the Monte Carlo method is a way to use random samples of parameters to explore the behavior of a complex system. A Monte Carlo simulation is used to handle an extensive range of problems in a variety of different fields to understand the impact of risk and uncertainty.

Statistical analysis and forecasting represent a broad range of techniques. It is used in all sectors such as manufacturing & consumer goods, healthcare & pharmaceuticals, finance & banking, insurance & reinsurance, pharmaceuticals, aerospace & defense, energy & utilities, and others.

Historically observed data can be described in terms of useful summary statistics for each variable, as well as the dependency across variables. Predictive modeling and forecasting techniques are applied to the data set, generating a reliable picture of the future to assist your decision making. Methods of statistical inference, hypothesis tests and quality control fulfill more specialized needs.

Graphs and charts help to visualize variables and results from statistical methods and are an invaluable resource for effectively communicating outcomes.

Demand forecasting

Load planning

Pricing

Sales forecasting

Portfolio allocation

Strategic planning

Six Sigma and quality control, and much more

Profit Projections

Monte Carlo simulation furnishes the decision-maker with a range of possible outcomes and the probabilities they will occur for any choice of action. It shows:

the extreme possibilities

the outcomes of going for broke and for the most conservative decision

along with all possible consequences for middle-of-the-road decisions

The technique was first used by scientists working on the atom bomb; it was named for Monte Carlo, the Monaco resort town renowned for its casinos. Since its introduction in World War II, Monte Carlo simulation has been used to model a variety of physical and conceptual systems.

Statistical analysis and forecasting start with a data set. From here you determine which analyses are most relevant to you. Any variable can be described with statistics calculated directly from the data, or shown in graphical summaries such as histograms and box plots. Time series can be forecasted any number of periods into the future using techniques that include trending and seasonality options. Forecasts can be represented graphically, greatly facilitating discussions and decision-making among all stakeholders. Quality control methods can also be applied to time series variables, creating Pareto and X/R charts (among others) to identify the most significant types of defect occurrences as well as the frequency of variation. These types of analyses are important in producing products or services of consistent high quality, such as in Six Sigma work.

When the data set contains related observations of dependent and independent variables, techniques such as regression and cluster analysis become appropriate. Statistical inference work such as ANOVA (analysis of variance) and nonparametric tests on the data form the basis of valid claims of significance. Correlations are calculated between variables to highlight dependencies your organization is exposed to.

When the data set contains related observations of dependent and independent variables, techniques such as regression and cluster analysis become appropriate. Statistical inference work such as ANOVA (analysis of variance) and nonparametric tests on the data form the basis of valid claims of significance. Correlations are calculated between variables to highlight dependencies your organization is exposed to.

With PrecisionTree, you never leave your spreadsheet, allowing you to work in a familiar environment, and get up to speed quickly.

See results in risk profile graphs, 2-way sensitivity, tornado graphs, spider graphs, policy suggestion reports, and strategy-region graphs.

Set up your decision tree in Microsoft Excel exactly as you need it with logic nodes, reference nodes, linked trees, custom utility functions, and influence diagrams.

Types of Statistical Analysis

There are different types of statistical analysis, employing different mathematical techniques to achieve different goals. Some of the more common types include:

Feature | Benefit | Professional Edition | Industrial Edition |
---|---|---|---|

Optimization under uncertainty | Combines Monte Carlo simulation with sophisticated optimization techniques to find optimal solutions to uncertain problems. Used for budgeting, allocation, scheduling, and more. | ||

Efficient Frontier Analysis | Especially useful in financial analysis, Efficient Frontiers determine the optimal return that can be expected from a portfolio at a given level of risk | ||

Ranges for adjustable cells and constraints | Streamlined model setup and editing | ||

Genetic algorithms | Find the best global solution while avoiding getting caught in local, “hill-climbing” solutions | ||

Six solving methods, including GAs and OptQuest | Always have the best method for different types of problems | ||

RISKOptimizer Watcher and Convergence Monitoring | Monitor progress toward best solutions in real time | ||

Overlay of Optimized vs Original Distribution | Compare original output to optimized result to visually see improvements | ||

Original, Best, Last model updating | Instantly see the effects of three solutions on your entire model |

Values are positively skewed, not symmetric like a normal distribution. It is used to represent values that don’t go below zero but have unlimited positive potential. Examples of variables described by lognormal distributions include real estate property values, stock prices, and oil reserves.

Values are positively skewed, not symmetric like a normal distribution. It is used to represent values that don’t go below zero but have unlimited positive potential. Examples of variables described by lognormal distributions include real estate property values, stock prices, and oil reserves.

All values have an equal chance of occurring, and the user simply defines the minimum and maximum because they have no knowledge of which values are more likely than others. Examples of variables that could be uniformly distributed include manufacturing costs or future sales revenues for a new product.

The user defines the minimum, most likely, and maximum values. Values around the most likely are more likely to occur. Variables that could be described by a triangular distribution include past sales history per unit of time and inventory levels.

The user defines the minimum, most likely, and maximum values, just like the triangular distribution. Values around the most likely are more likely to occur. However values between the most likely and extremes are more likely to occur than the triangular; that is, the extremes are not as emphasized. An example of the use of a PERT distribution is to describe the duration of a task in a project management model.

The user defines the minimum, most likely, and maximum values, just like the triangular distribution. Values around the most likely are more likely to occur. However values between the most likely and extremes are more likely to occur than the triangular; that is, the extremes are not as emphasized. An example of the use of a PERT distribution is to describe the duration of a task in a project management model.

Feature | Benefit | Professional Edition | Industrial Edition |
---|---|---|---|

Optimization under uncertainty | Combines Monte Carlo simulation with sophisticated optimization techniques to find optimal solutions to uncertain problems. Used for budgeting, allocation, scheduling, and more. | ||

Efficient Frontier Analysis | Especially useful in financial analysis, Efficient Frontiers determine the optimal return that can be expected from a portfolio at a given level of risk | ||

Ranges for adjustable cells and constraints | Streamlined model setup and editing | ||

Genetic algorithms | Find the best global solution while avoiding getting caught in local, “hill-climbing” solutions | ||

Six solving methods, including GAs and OptQuest | Always have the best method for different types of problems | ||

RISKOptimizer Watcher and Convergence Monitoring | Monitor progress toward best solutions in real time | ||

Overlay of Optimized vs Original Distribution | Compare original output to optimized result to visually see improvements | ||

Original, Best, Last model updating | Instantly see the effects of three solutions on your entire model |

During a Monte Carlo simulation, values are sampled at random from the input probability distributions. Each set of samples is called an iteration, and the resulting outcome from that sample is recorded. Monte Carlo simulation does this hundreds or thousands of times, and the result is a probability distribution of possible outcomes. In this way, Monte Carlo simulation provides a much more comprehensive view of what may happen. It tells you not only what could happen, but how likely it is to happen.

Monte Carlo simulation provides a number of advantages over deterministic, or “single-point estimate” analysis:

An enhancement to Monte Carlo simulation is the use of Latin Hypercube sampling, which samples more accurately from the full range of values within distribution functions and produces results more quickly.

PrecisionTree has a multitude of applications, including:

Better Research, Insights, and Outcomes for All

Whether your organization’s focus is qualitative, quantitative, or mixed methods data analysis, we can help your whole team work better together — collaborating to aggregate, organize, analyze, and present your findings. Lumivero’s enterprise licensing options offer volume pricing for teams and organizations needing nine (9) or more licenses.

Enterprise licenses allow the flexibility to install Lumivero software and solutions on multiple computers (up to the maximum number of licenses that your site has purchased) with a centralized management solution.

Enterprise licenses allow the flexibility to install Lumivero software and solutions on multiple computers (up to the maximum number of licenses that your site has purchased) with a centralized management solution.

Stay up-to-date with free upgrades to the latest releases

Reduce IT costs with one platform deployed across your organization

Reassign licenses to different users as teams evolve

Centralize license and subscription management in one place

Streamline budget allocation, especially for smaller groups and consultancy firms

Enjoy a Dedicated Customer Success Manager and pro-rated rates for new users