A statistical model is a model that embodies a set of statistical assumptions concerning the generation of sample data. It represents, often in considerably idealized form, the data-generating process. The goal of statistical modeling is to summarizes a test’s results in such a way that evaluators can observe data patterns, draw conclusions, and ultimately answer the questions that prompted the test.
Benefit of Services
Without statistical modeling, evaluators are left, at best, with “eye-ball” tests or, at worst, gut-feelings of whether one system performed better than another. Rigorous statistical analysis is less subject to bias, as it involves objectively quantifying and summarizing the data. This results in defensible conclusions that can better inform decision making.
- Regression Analysis
- Quality Control
- Machine Learning
What Each Includes?
Regression analysis is a set of statistical methods used for the estimation of relationships between a dependent variable and one or more independent variables. It can be utilized to assess the strength of the relationship between variables and for modeling the future relationship between them.
How many types of regression techniques do we have?
- Linear Regression
- Logistic Regression
- Polynomial Regression
- Stepwise Regression
- Ridge Regression
- Lasso Regression
The quality control tools and techniques that we use are:
- Cause and Effect Diagrams.
- Control Charts.
- Pareto Chart and Pareto Analysis.
- Run Charts.
- Scatter Diagrams.
- Statistical Sampling
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
Some Machine Learning Methods:
- Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events.
- In contrast, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled.
- Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of data.
- Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards.
Forecasting is a technique that uses historical data as inputs to make informed estimates that are predictive in determining the direction of future trends.
Businesses utilize forecasting to determine how to allocate their budgets or plan for anticipated expenses for an upcoming period of time. This is typically based on the projected demand for the goods and services offered.
- Qualitative methods: These types of forecasting methods are based on judgments, opinions, intuition, emotions, or personal experiences and are subjective in nature. They do not rely on any rigorous mathematical computations.
- Quantitative methods: These types of forecasting methods are based on mathematical (quantitative) models, and are objective in nature. They rely heavily on mathematical computations.