Data analysis

At Outtadesk, we provide tailored data analysis solutions for researchers in Crop Sciences, Agronomy, Environmental Sciences, and Geosciences. ​We offer three types of data analysis services, each including:

Two complimentary online meetings: the first scheduled before starting the service to understand your data and research goals, and the second tailored to your needs to discuss results and interpretations. The second meeting must be scheduled within 3 business days after delivery.

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To further support your research, we offer optional add-ons:

Six-month unlimited reviews to refine analyses and ensure your results are publication-ready.

Extra 1-hour online meetings for deeper discussions or troubleshooting.

 

With Outtadesk, you receive professional, reliable analysis and the guidance you need to interpret your results confidently.

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We offer three types of services: Data Analysis review, Essential Data Analysis and Advanced Data Analysis. All designed to help you extract clear, actionable insights from your data while saving time and effort:

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Data Analysis review

The Data Analysis review is a rapid yet thorough evaluation of the statistical methods you have applied in your research. This service is ideal for researchers who have already conducted their analysis but want professional confirmation of their approach. We will:

• Review your methods for correctness, appropriateness, and alignment with research objectives.

• Provide feedback on potential improvements or alternative approaches.

• Certificate confirming that your methods have been reviewed by a professional.

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​Essential Data Analysis ​

Our Essential Data Analysis package covers the core statistical tools every project needs, including exploratory data analysis, descriptive statistics, normality tests, analysis of variance, post hoc tests, and non-parametric tests. ​

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Advanced Data Analysis​

Our Advanced Data Analysis package is designed for complex research questions that require deeper statistical modeling. It includes techniques such as Principal Component Analysis (PCA), Factor Analysis, Canonical Correlation Analysis, Discriminant Analysis, linear and non-linear regression, and multivariate multiple regression.​

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