Statistical Analysis and Forecasting
Utilize Data for Forecasting and Decision-Making
Make Better Decisions with Quantitative Insights
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.
What is Statistical Analysis and Forecasting?
Range of Techniques Across Many Industries
- Demand forecasting
- Load planning
- Pricing
- Sales forecasting
- Portfolio allocation
- Strategic planning
- Six Sigma and quality control, and much more
- Profit Projections
Predict the Future from the Past
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.
Visual Communication
Graphs and charts help to visualize variables and results from statistical methods and are an invaluable resource for effectively communicating outcomes.
How Does Statistical Analysis and Forecasting Work?
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.
An ANOVA statistical analysis comparing the means of different data sets.
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:
- Descriptive Statistics – These summarize a data set and visualize it with charts and graphs.
- Statistical Inference – These analyses examine a sample of the entire data set to test a hypothesis and draw conclusions about the entire population.
- Regression – These are techniques which model the relationship between a dependent variable and one or more independent variables in order to make predictions.
- Forecasting – These are analyses applying statistics to historical data to project what could happen in the future.
Statistical Analysis and Forecasting Software from Palisade
Palisade’s StatTools software brings robust statistical analysis and forecasting power to Excel. With StatTools, any Excel user can apply any of over three dozen statistical and forecasting analyses to data directly in their spreadsheet. All StatTools functions behave exactly as native Excel functions do, so there is no learning curve. Furthermore, StatTools is designed to work closely with Palisade’s NeuralTools product for neural networks prediction, making for a combined data analysis powerhouse unavailable anywhere else.