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Importance of Statistical and Experimental Design

16 Sep

 

Importance of Statistical and Experimental Design

Every operation in a system requires careful planning and every experiment that yields a result requires verification and validation. Statistical and Experimental design by CAMO Software is based around this concept, to assist other companies and research centers in their experimentation, throughout the world. It deals with the design of experiments containing variables, under the full control of technical experts and designers who are responsible for many tasks such as A/B testing, split testing, multivariate data analysis and graphic design.

Role of CAMO software in Experimental Design

CAMO Software and tools related to experimentation are designed with all major types of manufacturing businesses in mind, such as food and beverages, software and hardware, pharmaceuticals, mining, paper and plastic industries. Planned experimentation is a part of chemical formulations, components, elements, materials and physical objects. The company, through its statistical analysis and experimental design, offers multiple data screening and evaluation, product management support, integration of technology into productivity, and assessment of economic returns. The services thus offered are meant for long-term use of requirements involving various elements with varying objectives. The fluctuating nature of expectations demand for caution during the design. For example, if the project undertaken with CAMO Software is based on agriculture and farming, the trials include :

- Assessment of land availability

- Determine the soil heterogeneity and guidance for modification of its characteristics

- Determine the effect of change in soil fertility on the land

- Ensure combined analysis and maintain consistency in the design

- Reduce or eliminate unnecessary previous factors during the experiment

- Integrate farmers’ trials to the experiment wherever necessary

- Determine the size of farming plot with possibility of errors taken into consideration

Important Experimentation Steps

Following are the seven major tips that are crucial in any experimentation in order to find a solution to a given problem:

- Define the problem and ready the variable

- Define objectives and check with the hypothesis

- Test the hypothesis including the stated variable

- Collect the data

- Analyze the data

- Interpret the target result

- Conclude the hypothesis.

CAMO Software engages in statistical methods to ensure proper execution of the above seven steps in the assigned experiment. The steps also touch on crucial areas of data such as samples collection, screening, coding, transformation, variables, parameters and analysis.

Collection, Screening and Analysis of Data in Detail

It’s a common idea that every data collected in an experiment becomes the part of that experiment in question. This is not true because some data do not adequately represent the matter under study. For example, a faulty sampling technique may ruin the whole observation or an incorrect calculation, a faulty measuring apparatus may wrong the data. Efficient data collection and screening involves weeding out such faulty data. The screening procedure approach makes sure that the data is rechecked, which may involve re-examining the collected data or revisiting the site when there is a doubt. A good successful practice means re-computing the extracted values and ensuring consistency throughout the process.

Another professional way to determine the appropriate value of a data is through drawing a relation between the input data and statistically acceptable data. This screening process, also known as spurious observation, depends on statistical distribution theory. It is based on classifying the data where maximum confidence lies. If the outlier falls outside this limit, the suspected data is rejected.

James is passionate about Experimental Design, he works as a data analyst for Camo.

 

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