LCBB5000: SPSS and Excel in Data Analysis for Business Intelligence

This report provides a comparative analysis of SPSS and Excel for data analysis in a business intelligence context. The first part focuses on the importance of Excel, detailing its functions like IF, Pivot Tables, LOOKUP, and Conditional Formatting for data preprocessing, analysis, and visualization, along with an analysis of sales decline in a given dataset. The second part uses SPSS to perform clustering analysis on customer data, examining rice consumption and gender distribution, and calculating mean and median ages. It also discusses popular data mining methods such as classification, clustering, prediction, and pattern tracking. The report concludes with a critical evaluation of the merits and demerits of using SPSS over Excel, highlighting SPSS's advanced statistical testing capabilities and limitations in handling large datasets, while acknowledging Excel's user-friendliness and cost-effectiveness.

Report SPSS

Contents
INTRODUCTION...........................................................................................................................3
MAIN BODY..................................................................................................................................3
Part 1............................................................................................................................................3
Part 2............................................................................................................................................6
CONCLUSION..............................................................................................................................17
REFERENCES................................................................................................................................1

INTRODUCTION
Data analysis is a useful term for each type of business entity for better decision-making. In
order to take corrective decision, it is essential to apply suitable technique of data analysis by
help of excel or any other tool (Alpar and Schulz, 2016). The report is based on two parts in
which first part contains detailed information about data of a superstore and this data set is
analysed through different kinds of functions of excel like IF, PIVOT TABLES etc. On the other
hands, in second part of report data of a clinic is included which is analysed through SPSS
functions. In the further section, different kinds of data mining methods are analysed and critical
evaluation of importance of SPSS over excel is done.
MAIN BODY
Part 1
Importance of excel: In modern time period, there is a wide range of excel functions which are
used by business entities and individuals to present and analyse the data in a manner which can
be used for better decision-making. Below some key importance of excel is explained with
rationale of processing of data, analysis of data and presentation of data:
Pre-processing the data-The term pre-processing of data can be understood as presenting the data
in a form which can be understandable of everyone (Wazurkar, Bhadoria and Bajpai, 2017). In
other words, pre-processing of data is a way of transforming raw data in an easy way. In this
aspect, excel plays a key role because by help of excel complexity of data set is removed and
data is presented in a manner by which users can read and can find out a suitable outcome. For
instance, in the excel filter function can be used for preprocessing of data set and such function
helps in sorting the data in set in a small form.
Analysing the data- This is also another important function of excel which is used to analyse the
data set. The term data analysis can be defined as a process of assessing the data set by help of
different kinds of techniques like mean, mode, median etc. This process can be completed
successfully by help of excel functions and tools. There are various kinds of formulas like if user
need to compute mean of a huge data set than it can be accomplished by applying =MEAN
formula in excel sheet. This way of data analysis reduce the effort and time of analyst as well as

there is a surety that produced outcomes are correct and can be used for decision making in an
effective manner.
Visualising the data- It is essential to present the data in a way which is attractive and
understandable. In order to do so graphs and tables play a key role which are offered under excel
sheet. As we can see that in the excel, users have variety of options of charts like bar chart, pie
chart, column chart and many more (Martínez-Rojas, Marín and Vila, 2016). In order to produce
such chart there is no complex process, users just need to select the data for which they want
chart and have to select type of chart. This process becomes too easier and helpful for many
companies and businesses to prepare an attractive way to present the data set. Apart from these
charts, there is another option by which data can be presented effectively like tables under which
users can fill the colour to make presentation more attractive.
So these are some key importance of excel for data set. Apart from this, in practical manner there
are different kinds of functions which can be used for above mentioned roles. Below description
of each function is done:
IF function- The IF feature is one of the most basic tasks in Excel and it helps you to make
rational distinctions between such a quality and what you anticipate (Nagar, Atriwal and Tayal,
2016). So an IF claim can have two outcomes. If your correlation is valid, the first outcome is the
double if your correlation is incorrect.
In Excel, a four - part period clause is included in the IF feature or IF argument. It is easy to see
in this case what revenue and benefit sums are decreased or even what period the buyer's charge
is, as well as how the IF feature is implemented point by point.
 Cut/paste the taxable date, revenue and benefit again on separate pages for the first time.
 Reorganize old data into quality assurance claim's main framework.
 Use the type 'IF Element.' Shape a code of sort = if (virus type: B2>C3),
 If the recipient satisfies the specifications, continue adding a cell key to be checked as
well.
 Semi-colon Productive: Pick "Rise"
 Delete the multipack; and press Continue if the requirement is not met.
If a feature stresses the value of B3, the function of B1 would mean that the criteria may be much
more critical than B3 if the output of B3 is better than those of segment B3. As this alternative

can be seen, consumers will get the layout of cell B4 as the press the Enter key. Seeing the effect,
move the deal from D4 to remote D8400.
If people should know which H Lookup and V backup are going to be in a process, they won't be
upset. A significant competence is that the customers like it or not. They will explore this in
detail as they interact to small numbers.
PIVOT table- A pivot table is a stats table that summarises a more detailed table's results. This
description could contain sums, estimates, or other figures that are grouped together in a
significant manner by the pivot table (Shollo and Galliers, 2016). In data analysis, pivot tables
are a procedure. In order to summarise, arrange, restructure, group, count, complete or average
data contained in a table, a Pivot Table is used. This makes it easy for us to turn rows through
rows upon rows into lines. It enables any area (column) to be clustered and detailed calculations
to be used.
LOOKUP function- The LOOKUP feature is graded under the Lookup and Comparison
functions in Excel. Whether in a one-row and one array, the method contains a rough fit search
and compares the result value from some other yet another or one-column scope. HLOOKUP
and VLOOKUP it is the more sophisticated variants of the LOOKUP feature. The big distinction
between the VLOOKUP and LOOKUP functions is that the VLOOKUP is restricted to
horizontal lookups only and bridge of the LOOKUP section ensures that both horizontal lookups
and vertical updates can be carried out.
Conditional formatting- Under the contingent type, it is necessary to classify all related entities.
There may also be other examples of a contingent prototype. This also refers to understanding of
meanings and acknowledgement of replication. Eventually Decide (CF) is a process that involves
coding an item or a row series and modifying the context of the measuring performance or
equation to the obligation tax. For example, whether the cell's vision is greater than 100. If the
performance of the cell matches the format specifications, the model they select is applied to the
cell (Khedr, Kholeif and Saad, 2017). The experienced people of the device can be used when
network security does not meet the format specifications. MS Excel ensures rigorous device
protection for sheets, making it easier for workers to track their performance. The three MS

Excel files were put inside an outstanding database using essential visual applications. In MS
Spread, they file the general details as well as keep the ordered data because that space is saved.
Everybody needs to be careful with their records, and MS Excel is very better at fixing this
dilemma. Yet none is aware of repairing it or destroying it.
Decline in sales of given data set:
The related figures state that the company had the average pay and the lowest investment in
January, too. In 2012, but at the other side, double the highest income was reported. The
statistics indicate that profits and profits differ significantly; the group experienced substantial
losses from 2009 to 2010, with the exception of 2012. True revenues improved over 2009, while
in 2009 there was no decline in following years.
Part 2
2.1 Specific example of clustering technique.
How many customers eat rice?

The above table and graph shows that out of 100 customers, 60% customers eat rice and 40% do
not eat rice.
How many customers are Male and Female?

The above table and chart shows that out of 100 customers, 50% customers are male and 50%
are female.
Mean, Median of the ages in the data-

Mean, Median of the customers who eat rice-

Clustering analysis of given data set:
Two step cluster-

K means cluster-