# Analysing Results

## Analysing Results

### Part 1: Understanding Calibration Curves:

- Be capable of plotting and interpreting
**calibration curves**. - Recognise that these curves establish the relationship between instrument response and analyte concentration.
- Understand the usage of calibration curves to determine the concentration of analyte in unknown samples.

### Part 2: Error Analysis:

- Be able to identify and understand different types of errors such as
**random and systematic errors**. - Be comfortable with calculating
**absolute and relative errors**. - Learn to use
**standard deviation**as a measure of spread of results around the mean. - Understand the meaning of
**confidence intervals**and their interpretation.

### Part 3: Spectral Data Analysis:

- Be familiar with analysing and interpreting
**infrared (IR) and ultraviolet-visible (UV-Vis) spectra**. - Understand the principles of
**nuclear magnetic resonance (NMR) spectroscopy**and how to analyse NMR spectra. - Know how to apply these skills to identify unknown compounds.

### Part 4: Chromatographic Data Analysis:

- Understand the principle of
**retention time**in chromatography and its use in identifying compounds. - Familiarise yourself with the construction and interpretation of
**chromatograms**. - Know how to use
**peak area**or**peak height**to determine concentrations.

### Part 5: Stoichiometric Calculations:

- Be capable of performing
**stoichiometric calculations**based on balanced chemical equations. - Be comfortable with
**limiting reagent**concepts and calculations. - Understand the meaning of
**theoretical yield**,**actual yield**and**percentage yield**. - Recognise the importance of these parameters in evaluating the efficiency of chemical reactions.

### Part 6: Evaluating Techniques and Methods:

- Understand how to evaluate a technique or method based on accuracy, precision, reproducibility, and repeatability.
- Know how to improve a method or technique by identifying potential sources of error or inaccuracy.
- Understand the importance of
**critical thinking**in experimental design and data analysis.