Data set B – Wine Quality – Jan Version

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Data set B – Wine Quality – Jan Version

Wine is a product where its quality adds value to the finished product. Wine quality is typically assessed using sensory evaluations such as the aroma, taste and mouthfeel. As sensory evaluations may be subjective, studies have been conducted using chemical analysis methods to increase objectivity of evaluations (e.g., Schober et al., 2022).

According to Hu et al. (2016), volatile acidity, free sulfur dioxide and alcohol are the most important variables in determining wine quality. A wine manufacturer wishes to explore the claim made Hu et al. (2016) to set new guidance on pricing of their wines. From table 1, we have the following variables:

· fixed acidity

· volatile acidity

· citric acid

· residual sugar

· chlorides

· free sulfur dioxide

· total sulfur dioxide

· density

· pH

· sulphates

· alcohol

· quality = 0 (very bad) to 10 (very excellent)

Using the data collected in table 1, the wine manufacturer wants you to investigate the following.

1. Build three simple linear regression models to predict the quality of wine from the variables suggested by Hu et al. (2016).

2. Of the three variables suggested by Hu et al. (2016), which has the most impact on quality of wine (if there is any difference)?

3. Is there a significant difference of the quality of red wine compared with white wine?

You need to present your answer in the form of a report.

Reference list:

Hu, G.; Xi, T.; Mohammed, F.; Miao, H. (2016) Classification of Wine Quality with Imbalanced Data. In Proceedings of the 2016 IEEE International Conference on Industrial Technology (ICIT), Taipei, Taiwan, 14–17

Schober, D.; Gilmore, A.; Chen, L.; Zincker, J.; Gonzalez, A. (2022). Determination of Cabernet Sauvignon Wine Quality Parameters in Chile by Absorbance-Transmission and Fluorescence Excitation Emission Matrix (A-TEEM) Spectroscopy. Food Chem., 392, 133101.

Task Brief:

This is the assessment brief for the statistical analysis report, which is worth 50% of your overall grade for this course.

Upon successful completion of this assessment, you will be able to:

· LO1: Critique original research data sets relevant to their field of study selecting appropriate statistical methods

· LO2: Discuss the relevance, validity, and reliability of statistical methods in the context of experimental design

· LO3: Evaluate and interpret scientific information and data, both qualitative and quantitative, relevant to applications of their subject area

This assessment will require you to analyse and evaluate data and present the data, conclusions, and any future recommendations in the form of a report.

There is no single or best solution for this assessment, and you are to develop your own strategy in tackling the report.

An exemplar will be provided for to help you with formatting.

Any calculations must only use the methods and techniques described to you on this course (Theme 1-8).

Microsoft Excel must be used for the statistical analysis you will perform below. Use of other software is not permitted.

The report should include the following elements.

1. Introduction

In this section of the report, you should identify the purpose of research or experiment. You should make sure to explain why this report should be read and the questions you wished to be answered.

2. Experimental Design

In this section, you should identify and state explicitly:

· the factors

· treatment

· Any lurking variables

Using the data you have chosen, you should use an appropriate random sampling method to choose a sample of size 50.

From the data provided and sample selected, you must highlight any:

· possible errors in measurement

· outliers

You must describe their effect on your conclusions.

You should select and justify what statistical methods you will use to identify any statistical links between the variables in the data, and to link this with the questions you wish to answer.

3. Results

Here you present your main findings from your statistical calculations and graphs. This section should only include numerical and graphical outputs without an analysis or discussion of what these outputs might mean.

Any calculations and graphs used in this report must follow the methods and techniques described to you on this course (Theme 1-8).

· Make sure to only include main results that help answer the questions you wish to find answers to.

· Stay away from reporting results that do not help you answer your question.

· Makes sure any descriptive statistics and numerical calculations are presented in clearly labelled tables.

· Appropriate charts and graphical summaries should be used to show patterns in the data or links between the variables.

4. Discussion and Findings

In this section, you shall provide your analysis and define your results in context of the scenario you have chosen. You should make clear what the answer to your research question is and any future recommendations.

You should consider the following to help with your analysis:

· Use appropriate descriptive statistics to compare treatments

· Analyse and interpret the numerical calculations, commenting on how the collection method of the data and the quality of the data effects the validity of the results.