Problem-solving in evolving environments is a core element of every engineering discipline. Engineers identify problems, anticipate variables, and develop solutions through rigorous engineering design processes. Accurate and relevant data are essential through every step of this process.
You may wonder how statistics can best be used in engineering projects. The answer lies in applied statistics or the use of statistics to solve functional problems. Extensive knowledge of applied statistical principles and methods adds clarity to decision-making on engineering projects.
Applied Statistics Methods
Engineers around the world use some variation of a design process involving the steps Ask, Research, Imagine, Plan, Create, Test, and Improve. This method is difficult to follow without a systemic approach to collecting, modeling, and analyzing data. Applied statistics help you gain valuable insights and develop real-world solutions.
The first step in applying statistics to engineering challenges is selecting the appropriate data collection method. Engineers often collect samples of larger processes or systems while researching projects. There are three frequently used methods for data collection in engineering:
- Retrospective studies of previously recorded data
- Observing portions of processes or systems for short periods of time
- Designing experiments with randomization for cause-and-effect data
Engineering teams analyze collected data through the use of modeling and simulation. Mechanistic models use robust data and existing knowledge of physical processes to make predictions. The empirical modeling process turns observations into useful insights when there is insufficient data or unknown variables. Probability modeling determines the likelihood of random events within defined sample spaces.
Engineering Uses of Applied Statistics
The usefulness of applied statistics isn’t restricted to a single subset of engineering. In fact, you may be using some form of applied statistics in your daily work without being a statistician. Additional education in statistical analysis, like a graduate degree, makes the following applications possible.
Chemical Engineering
Chemical engineers use applied statistics when designing manufacturing processes for household and industrial products. They need to factor in product safety, budget, and labor availability for effective production methods. Modeling and simulation tools make it possible to improve upon existing processes and create entirely new production lines.
Applied statistics is also helpful when chemical engineers develop and test new products. Engineers focused on research and development deploy statistics when testing chemical interactions. Observational studies on production lines evaluate potential issues with product quality and safety.
Civil Engineering
The long-term structural integrity of bridges, public facilities, and sewer systems depends on effective civil engineering. Engineers in this discipline use retrospective and observational studies to determine the potential use of public assets. Statistics and extensive knowledge of engineering principles turn designs into new structures.
Civil engineers also use applied statistics in the day-to-day operations of local governments. Data collection on water quality, wastewater volume, and traffic stops can be turned into predictions of future trends. Periodic inspections of public properties determine if systems and structures comply with building codes.
Electrical Engineering
Probability models can be used by electrical engineers when anticipating random variables for remote or long-distance systems. For example, voice and data transmissions are dependent on constant functionality by two or more devices along with connectivity infrastructure. A probability model can determine the potential outcomes for a system based on its specifications and usage.
The measurement of an electrical system’s signal-to-noise ratio (SNR) is also helped by a statistical approach. SNR determines the amounts of desired signal voltage and unwanted noise voltage within an electrical system. Electrical engineers can identify and mitigate potential noise sources for improved system performance.
- Learn more about Statistics in Electrical Engineering
Computer Engineering
Statistical analyses are invaluable when computer engineers design new computers, network devices, and other hardware. Engineers analyze performance data from past hardware versions to identify what hasn’t worked and what is possible within specifications. Data collection and modeling also determine the best method for manufacturing hardware.
Computer engineers need to simulate hardware performance before they reach end users. Estimating the mean time between failures (MTBF) for components contributes to informed decision-making on methods for prolonging product lifespans. MTBF, failure rates, and other metrics are possible through applied statistics methods.
- Learn more about Statistics for Software Engineering
Mechanical Engineering
Mechanical engineers are tasked with continuously improving products to keep up with consumer demands. Prototyping of smart appliances, high-performance engines, and Internet of Things devices involves extensive modeling. Applied statistics are useful in assessing component quality and design improvements over previous models.
Manufacturers also ask mechanical engineers to solve equipment failures or performance issues. Empirical modeling may identify obvious issues with a recalled refrigerator or a failed wind turbine. It is also possible to categorize product failures as unique or widespread using probability models.
Master the Skill of Applied Statistics at Michigan Tech Online
Michigan Tech’s Master of Science in Applied Statistics is a 100% online option for those without a previous statistics degree. Applicants need bachelor’s degrees and excellent grades in previous math coursework for admission. There is a 10-week bridge course in linear algebra available for admitted students prior to their first graduate course.
Students learn advanced statistical skills from experienced faculty members during seven-week courses. The 10-course curriculum includes the use of current statistics and analytics software. Engineers learn to apply statistical methods to their work through courses like:
- Computational Statistics
- Predictive Modeling
- Probability and Statistical Inference I and II
- Statistical Methods
This innovative degree is all the more valuable due to Michigan Tech’s stellar reputation. U.S. News & World Report placed Michigan Tech No. 151 in its ranking of National Universities. The school placed among the leading universities in the following categories:
- No. 72 in Top Public Schools
- No. 100 in Best Colleges for Veterans
- No. 170 in Best Value Schools