Data Analysis: Market Trends & Sales Factors at Midwestern Uni. Cafe

This report evaluates the market and factors affecting sales at the Executive Express café, operated by students at Midwestern University. The analysis uses a dataset of 47 observations and 22 variables, including dates, temperature, sales, items sold, and items wasted, to inform decisions on ordering and maximizing daily sales. The report identifies market penetration, determines the effect of temperature on sales, and analyzes the correlation between daily sales and the day of the week. Statistical methods such as regression analysis, correlation, and means are employed to analyze the data. The findings indicate no direct relationship between daily sales and temperature, but a positive correlation between soda sales and temperature. The report concludes with recommendations for optimizing ordering based on temperature to maximize profit and minimize waste. Desklib provides access to similar reports and solved assignments for students.

Market evaluation and factors affecting sales in Student cafe:
Data Analysis conducted Student
Student name
University name
March 14, 2018
Abstract
The data set is from Executive Express café which is run and operated by students at
Midwestern University. It contains 47 observations and data 22 variables that are dates,
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temperature, and sales, item sold and item wasted. The data was used to make the decision on
what to order and when to order to maximize daily sales and thus making more profit. The café
have been experiencing low sales and a high number of items wasted. The market penetration
that is which is most selling products and least selling products.
Introduction
The student's café has been in operation selling products to the student. Its main aim is to
optimize the number of daily sales and reduce the number of wastes to ensure maximum
utilization of products ordered and resources available increasing daily turnover. The main
objective of every business is to make a profit, increase it market and reduce operational cost.
The aim of this analysis is to determine the effect of temperature on sales and analyze the market
penetration of each commodity sold in the school. Temperature has been associated with the
number of sales each day. During cold seasons people tend to hanger around the café for a long
time leading to more sales. This may not be so to the number of sodas sold and temperature.
Also, there is need to identify if there is any correlation between daily sale and day of the week.
Every business has challenges and the main question is how to optimize profits. Therefore
Executive Express would like to make use of historical data to come up with the best strategy to
maximize profit and minimize losses.
Methodology
In order to perform this analysis one source was used to obtain the data, thus the data was
obtained from secondary sources. The data was obtained from http://ww2.amstat.org. The main
problem in data collection is that the data obtained was seasonal and may be affected by
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seasonality effects. The study made use of Excel software for analysis and data cleaning. The
data was quantitative in nature; inferential statistics and descriptive statistics were used in the
analysis. That is regression analysis, correlation and means (Neuman, 2014).
Descriptive statistics
4.59338061465721 1.4
13
6
6
1.89.5
4.79858156028369
30.3016548
463357
20.5446808
510638
Mean sale
Bread Sand Sold
Wraps Sold
Wraps Sold
Muffins Sold
Cookies Sold
Fruit Cup Sold
Chips
Juices
Sodas
Coffees
The best selling commodities are sodas and coffees with mean sales of 30.3 and 20.5
respectively, bread sand and fruit cup are the least selling products. Students buy more snacks
and soft drinks from school café.
Bread Sand Waste
Wraps Waste
Muffins Waste
Cookies Waste
Fruit Cup Waste
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6
Mean product wasted
Bread sand and wraps are the most wasted products in the café. There is a lot of waste in the café
which mainly consist of baked products. The management should optimize sales by reducing
number of baked products order daily.
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Results
The first step is to plot to scatter plot of temperature against sales and temperature against soda
sales.
0 10 20 30 40 50 60
0
50
100
150
200
250
300
Scatter plot of sales against
temperature
There exist no linear relationship between sales and temperature. Temperature has no linear
association with the sales output. However, in some seasons this does not apply showing that
although there is a positive association between sales and temperature there may be other factors
affecting sales which are not captured by scatter plot.
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0 10 20 30 40 50 60
0
10
20
30
40
50
60
Plot of temperature against daily
sales of soda
The scatter plot soda sales and temperature a weak positive linear relationship between two
variables. The correlation coefficient (r) between temperature and soda sales is 0.53; this is a
weak positive linear relationship. As temperature increases soda sales also increases and vice
versa. This was captured on scatter plot. The correlation coefficient between temperature and
daily sales is -0.21 which is a weak negative relationship. The plot indicated no linear association
between the two variables.
ANOVA
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df SS MS F
Significan
ce F
Regressio
n 1 587.46 587.46 0.28 0.60
Residual 45
94485.7
2
2099.6
8
Total 46
95073.1
8
Table 1: Regression output of sales (y variable) against temperature (x variable)
The value of R-square is 0.01 which means only 1% of sales output is explained by daily
temperature. Thus the model is not a good fit. The p-value is 0.60 which greater than the level of
significance 0.05 thus we conclude that daily temperature cannot be used to predict daily sales.
ANOVA
df SS MS F
Significanc
e F
Regressio
n 1
2691.5
5
2691.5
5 17.60 0.0001
Residual 45
6883.7
3 152.97
Total 46
9575.2
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Table 2: Regression output of soda sales (y variable) against temperature (x variable)
The p-value is 0.0001 at alpha equals 0.05, the p-value is less than 0.05 and therefore we
conclude that temperature is significances in predicting the number of soda sales. Thus the
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relationship between soda sales and temperature is statistical significance. The higher
temperature in a day results in a higher the soda sales in that day and vice versa.
0 10 20 30 40 50 60
0
50
100
150
200
250
300
Time Series Plot of Total sales
The number of sales is affected by daily seasonality and shocks; there is sudden rise and decrease
in number of sales in the café. When the seasonal effects is eliminated using exponential plotting
it is evident that the sales shows a constant trend and an increase in number of sales is expected.
Conclusion
From analysis and results above we conclude that there is no relationship between daily sales
and daily temperature. There is no statistical significance between the association of sales and
temperature. Sales are affected by other factors, not temperatures such as cost and demand. Thus
we expect same sales in any particular day if other factors are held constant. There is a positive
linear association between soda sales and daily temperature. As temperature increases also soda
sales increases and as temperature decreases soda sales also increases. From linear regression
analysis, there is statistical significance between soda sales and daily temperature. This can be
generalized to other soft drinks sold in the café such as juice and fruit cup. The management
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should order more juice, fruit cup and soda on a hot day and few in cold seasons. This will lead
to higher sales and less waste thus maximizing profit and minimizing loss.
References
Neuman, W. (2014). Social Research Methods: Qualitative and Quantitative Approaches, 7th
Edition. Pearson Education Limited: UK.
Journal of Statistics Education, 1993-2015 data archive,
http://ww2.amstat.org/publications/jse/jse_2001/jse_data_archive.html, Obtained 14 March
2018.
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