“All knowledge that the world has ever received comes
from the mind; the infinite library of the universe is in our own mind”
Business leaders are
yearning for deeper knowledge and insights on all aspects of their business and
they know that the information they need is available within all the data
flowing through the company IT systems. The growth of social
conversations and content across the many channels is providing business
leaders with a never ending stream of incoming data. The challenge is to
turn all that data into insights and then develop strategies/actions based on
those insights. That is where Business Analytics play a crucial role.
Learning’s from Business Analytics Lecture
‘Business Analytics’ AKA ‘Business
Intelligence’ allows users to examine and manipulate data to drive
positive business actions armed with advanced analytics insights, business
users can make well-informed, fact-based decisions to support their
organizations’ tactical and strategic goals.
The underlying
objective: “Discover new and
meaningful patterns in data”
But the
amount of data is doubling every year. Information from devices, machines and
social data, creates an entirely new set of challenges and reinforces the fact
that data will continue to grow exponentially for the foreseeable future. There
arises need of advanced tools and techniques. SPSS is one of them.
SPSS
“SPSS –A Social Science Statistical Package” is
a computer program used for:
SPSS is
very similar to Microsoft Excel in Layout. There is a menu and a tool bar
option at the top of every window and some of its functions are just like an
Excel.
Getting Familiar with SPSS
Launching SPSS: Launch SPSS uses a start menu or a
desktop shortcut. SPSS starts and the opening screen resembles a spread sheet.
A dialogue box will open which will allow opening an existing data or creating
a new data.
Variables and Cases: SPSS
uses data organised in rows and columns. Rows are cases and columns are
variables. Variables are categorised as:
Category Variables: These variables are limited in number
with fixed number of possible values. For Example: Gender- There are only 2
fixed values i.e. Male and Female.
Continuous Variables: Continuous Variables can further be
classified into :
- Continuous : Age (Years, Months and Days) e.g. John is 22 years 2 month 10 days old
- Discrete: These numbers have fixed values. E.g. Number of brothers and sisters.
The users of the SPSS have two views:
1. Data View: Contain data in
form of number or texts.
2. Variable
View: In this the user gets
information about variables included in the data set. The columns provide information about the various
characteristics of variables. There are ten columns in total. Each column has a
different significance for variables. Columns include:
· Name : Name of the
variable
· Type : There are 8
options which include Numeric, Comma, Dot, Scientific Notation, Date, Dollar, Custom Currency and String.
· Width : Helps to decide
number of characters to display
· Decimal: Helps to control
number of characters after the decimal place.
· Labels: Helps to give
complete description of a variable.
· Values:Helps to provide a key for what the numbers of a numeric variable may represent. (E.g. 1= Male, 2= Female )
· Missing: Helps to indicate
missing values in the variable. These missing values are not used for analysis.
· Columns: Helps to indicate
total number of columns a variable value may have.
· Align: Help to align data to
right or left.
· Measure: Helps to indicate the level of measurment of a variable. There are three forms :Nominal, Ordinal and Scale
Level of Measurement of Variable
Defining of data is of no use without performing
operations and analysing data. There are three type of analysis based
on type of data:
1. Uni-variate: Simplest form of analysing a single
variable data. Data can be represented in form of pie chart.
2. Bi-variate: It includes analysis of two variables simultaneously
through cross tabulation or pie charts.
3. Multivariate: It includes analysis of more than 2
variables through scatter diagram etc.
In more advanced
analysis, data can be transformed from one form to the other through TRANSFORM tab. For e.g. Age from
continuous discrete form can be transformed to groups.
Data can be analysed though ANALYSE tab. Under
ANALYSE tab there is a descriptive option. Descriptive Statistics in SPSS include:
·
Frequencies: They are primarily used
for discrete data (e.g., nominal and ordinal data).It tells the number of times
the variable has occurred. E.g. To know
the shopping frequency of a person. Data
can be showed in terms of per cent and can be combined and analysed through
cumulative per cent.
· Cross Tabulation: They are primarily
used to tabulate two or more data variables and to assess the relationship
between them. Cross Tabulation generates bi-variant relation. If continuous
variables are there, they are first re-categorized into category variables and
then analysis is done.
e.g. Age can be 19,20,21,22,23,24,25,26,27,28,29
and 30 years. The task is to find out how many people marry before the age of
25 years. Depicting this data in a cross tabulated form can be very tedious.
Thus it can be re-categorised in to age groups 19-25 and after 25.
Before Cross
Tabulation, Hypothesis and Null Hypothesis need to be form.
Hypothesis:
H1: Unemployment affects the
children going to school
Null
Hypothesis: H0: There is no relation
between Unemployment and children going to school.
After developing Hypothesis and
performing Cross Tabulation, to assess whether relationship is there between
two variables or not, Test of Independence
i.e. Chi-square test is performed which comes under testing of Hypothesis.
Now according to the Chi-Square test, if the significant
value is lower than .05, there is a significant difference between ratios. So null
hypothesis would be rejected.
Author
Akhil Aggarwal
Roll No: 14126 | SIBM (2011-13) Batch
(Team G)
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