7COM1079: Data Analysis Report on Wine Importers Business Growth

This report presents a data analysis of Australian Wine Importers (AWI), focusing on its business growth using numerical data like wine taste ratings and price. The study addresses research questions concerning AWI's current status, the impact of statistics on business decisions, and the relationship between taste ratings and wine costs. The methodology involves qualitative analysis, employing descriptive statistics, data visualization (line graphs), correlation, and linear regression using SPSS. Results include descriptive statistics, data visualizations depicting the relationship between tasting scores and wine prices, and correlation analysis of wine points, price, and Z-scores. The discussion interprets these results, highlighting the statistical significance of the findings and the model's ability to explain the wine reviews dataset. The conclusion emphasizes the model's statistical significance in aiding AWI's business decisions, based on wine tasting scores, making it a good fit for analyzing the wine reviews dataset. References to relevant sources are provided.

Team Research & Development

Table of Contents
1. Introduction............................................................................................................................1
2. Background.............................................................................................................................1
2.1 Research Questions.........................................................................................................1
2.2 Peer Evaluation...............................................................................................................1
3. Method.....................................................................................................................................2
4. Result.......................................................................................................................................2
4.1 Project Planning and Management...............................................................................2
4.2 Descriptive Statistics.......................................................................................................4
4.3 Data Visualization...........................................................................................................5
4.4 Correlation.......................................................................................................................7
4.5 Significance......................................................................................................................7
5. Discussion................................................................................................................................9
6. Conclusion.............................................................................................................................10
References.....................................................................................................................................11

1. Introduction
This report present a data analysis of a company named Australian Wine Importers
(AWI), which aims to have business growth. It intends to take correct business decisions to
support it growth of its business. It contains huge data and variables, among which the numerical
data like taste ratings and price of the wine are considered for the research analysis.
2. Background
AWI imports wine to Australia. The cost of wine and the taste of the wine are the
important factors which can help the business to grow. The company has collected the tasting
results, which are collected in the tasting rooms. The genuine ratings are recorded by the trusted
tasters. It is observed that the tasting scores given to each wine are considered important and has
high priority in determining the right decisions which can support AWI’s growth (Zackthoutt,
2017).
2.1 Research Questions
The research questions are listed below:
1) What is the current status of AWI?
2) Can the statistics support to take business decisions?
3) What the taste ratings statistics have impact on cost of the wine?
4) Is the tasting score enough for improving and helping AWI?
5) How such a huge data is examined, classified and evaluated to make the right
business decisions?
6) In the wine industry, what factors lead to systematic growth of the business,
including right business decision making?
7) What is the relation between the tasters score, with the type of wines, producer,
wine’s description, and its price?
8) What will the status of AWI be after new wine price implementation?
2.2 Peer Evaluation
The peer contribution is shown below (Bingham and Fry, 2010).
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3. Method
The qualitative methodology is expected to suit this research, where the wine reviews
dataset is analyzed. The expected approach for this research is the positivism research
philosophy that considers the real observation data. The descriptive statistics can be used for
evaluating the mean and standard mean of the required questions, to get the desired results. The
data visualization can help to view to results in graphical format. The correlation could support
in classification of the respective data used. The significance can be useful to check the statistical
significance of the model. This research demands linear regression, where the SPSS tool is used.
4. Result
4.1 Project Planning and Management
This section recognizes all the artifacts included in the project, and they are presented by
using the work break down structure which is displayed in the following screenshot.
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The sprint back log tables is as follows.
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4.2 Descriptive Statistics
Using SPSS, the descriptive statistics is completed as presented in the following
screenshot.
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Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
points 150930 80 100 87.89 3.222
price 137235 4 2300 33.13 36.323
Valid N (list wise) 137235
The above table signifies the results of the descriptive statistics.
4.3 Data Visualization
The data visualization is used to display the wine reviews data visualization by using the
suitable graphs. The below line graph is used to represent the relationship between the tasting
score and prices. It displays that the tasting score have impact on cost of the wine (Mcgrath,
2018).
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The below line graph is used to represent the tasting score versus country which is used for
improving and helping AWI in each country.
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4.4 Correlation
The wine points, price, Zscore (points), and Zscore (price) correlated helps to measure,
formulate and classify the wine reviews data.
Correlations
points price Zscore(points
)
Zscore(price
)
points Pearson Correlation 1 .460** 1.000** .460**
Sig. (2-tailed) .000 .000 .000
N 150930 137235 150930 137235
price Pearson Correlation .460** 1 .460** 1.000**
Sig. (2-tailed) .000 .000 .000
N 137235 137235 137235 137235
Zscore(points) Pearson Correlation 1.000** .460** 1 .460**
Sig. (2-tailed) .000 .000 .000
N 150930 137235 150930 137235
Zscore(price) Pearson Correlation .460** 1.000** .460** 1
Sig. (2-tailed) .000 .000 .000
N 137235 137235 137235 137235
**. Correlation is significant at the 0.01 level (2-tailed).
4.5 Significance
This section helps to evaluate the statistical significance of the model with the help of
linear regression and SPSS tool.
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The output of linear regression is presented below (HARRELL, 2016).
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5. Discussion
The results obtained are interpreted in this section. Descriptive statistics is completed to
help get the statistical information of the dataset, then the data visualization section shows the
graphical results for showing the relationship that exists between the tasting score and wine
prices, the effect of tasting score on the wine’s price is also displayed graphically. Further, the
data visualization shows the tasting score versus country which is used for improving and
helping AWI in each country (Lee, 2012). Accordingly, to evaluate the wine reviews data
correlation is completed, where the wine points, price, Zscore (points), and Zscore (price) are
correlated. From the results of correlation, the .sig value is observed to be .000, and it represents
the wine points, price, Zscore (points), and Zscore (price) to be statistically significant.
Moreover, it measures the wine data quality based on the reviews, points, and price of the wine.
From this it is evident that these factors can lead AWI to a systematic growth and help in taking
correct business decisions.
The linear regression is completed, which facilitates some tables like the model summary
table. This table assists to conclude the model as either a good or bad fit model. In this context,
the obtained R value shows 0.460, it gives a variability of 46% depending on the wine tasting
scores rated by the tasters. Then, the ANNOVA table’s results show the .sig values as .000. Thus,
the <0.005 is satisfied and so the created model is significant enough to clarify that the wine
tasting scores can help to improve and help AWI to grow and take business decisions. Even, the
coefficient tables contains .sig value and it shows .000 for the tasting score and wine price that
are utilized to justify the data of wine reviews.
6. Conclusion
The analysis proves the results where the created model is statistically significant for
helping AWI to improve its business decisions based on the wine tasting scores. Consequently, it
is good fit model for explaining the wine reviews dataset. The linear regression method is used to
get this result, including descriptive statistics, data visualization, and correlation. Thus, the wine
reviews dataset is analyzed. The qualitative methodology is the base of this research. The output
are shown with the help of data visual to easily understand the results. Therefore, the created
models help to evaluate and validate the wine price and tasting results.
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