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Statistics for Scientists
1 Day Course | Statisics Level 1

This one day introductory course to the fundamental of statistics, relevant to all professionals in the analytical sciences, life sciences and related fields.

• We limit numbers to 20 per course so that each delegate gets the opportunity to ask questions and fully participate in tutorial exercises
• When delivered on-site we can design the course material to suit your specific training needs
• Customisable written assessments are available if required

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This course comprehensively covers the basic concepts and methods of statistics with applications in the field of
analytical sciences. During this course, we will demonstrate methods of exploring, organising, and presenting data,
and introduces the fundamentals of probability.
We will:
- explore the foundations of statistical inference, including the concepts of parameters and estimates
- explore the use of the statistical measures, confidence intervals, and hypothesis tests.

Who is this course for

The aim of the course is to foster an interest in applied statistics and equip scientists and project managers to confidently apply statistical techniques to investigate and improve data quality.

Previous knowledge

Good knowledge of chromatography and statistical analysis is advantageous.

What you will learn

• Quantifying Errors
• Normal (Gaussian) Distribution
• Significance Tests
• Statistical Measures
• Confidence Limits
• Analysis of Variance
 Introduction Analytical problems Errors in quantitative analysis Random and systematic errors Accuracy, repeatability, reproducibility Standard reference materials Statistical Measures Mean and standard deviation Variance and coefficient of variation Significance Tests Null hypothesis, type I and type II errors Comparison of and Comparison of and t-tests F-tests One-sided and two-sided tests Outliers Analysis of variance (ANOVA) Fixed effects and random effects Comparison of several means Normal (Gaussian) Distribution Sampling distributions Confidence limits Significant figures Propagation of errors Regression and Correlation Calibration graphs Correlation coefficient Regression of y on x Errors in the regression line Calculation of a concentration Limit of detection Standard additions Outliers in regression Weighted regression lines

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