Business
Analytics:
Statistical package for
social sciences (SPSS) is a widely used program for data entry, statistical analysis, to create tables and graphs. It
can perform analysis and handles a large amount of data. One can find two files
in the SPSS tool, namely Data file, a spread sheet like system for defining,
entering, editing, and displaying data whose extension of the saved file will
be in “.sav” format and Output file,
which displays output and errors whose extension of the saved file will be in “.spv” format.
In the
initial class we have got familiar with the SPSS software by entering a sample data
problem and analyzing the same. While entering the data, we had known how to
use the data view tab, variable view tab and description of the data in the
variable view like, Name, Type, Width, Decimals, Label, Values, Missing, Columns,
Align, and Measure.
The name
in the first character of the variable name must be alphabetic, unique, and
have to be less than 64 characters.
The
type enables to specify the type of variable. The two basic types of variables
that we use are numeric and string.
In the next class we
came to know about the ‘Classification of techniques into level-1 and level-2
depending on the number of variables we use. We also came to know about the
different types of variables such as, ‘Continuous or Quantitative and Discrete
or Qualitative variables.
After knowing how to
use or operate the software, we worked on the pre-loaded data file namely, ‘GSS93
subset.sav. With the help of data available, we analyzed on a null hypothesis that,
“There is no relation between number of siblings and the age when married”
What exactly is degrees of freedom?
The mathematical answer is a single
phrase, "The rank of a quadratic form." It is one thing to say that
degrees of freedom is an index and to describe how to calculate it for certain
situations, but none of these pieces of information tells what degrees of
freedom means.
A data set contains a number of observations, n’. They constitute n’ individual pieces of
information. These pieces of information can be used either to estimate
parameters or variability. In general, each item being estimated costs one
degree of freedom. The remaining degrees of freedom are used to estimate
variability. All we have to do is count properly.
A single sample: There are n observations. There's one parameter (the mean) that needs
to be estimated. That leaves n-1 degrees
of freedom for estimating variability.
Two samples: There are n1+n2 observations. There are two
means to be estimated. That leaves n1+n2-2 degrees
of freedom for estimating variability.
One-way ANOVA with g groups: There are n1+..+ng observations. There
are g means to be
estimated. That leaves n1+..+ng-g degrees
of freedom for estimating variability. This accounts for the denominator
degrees of freedom for the F statistic.
The primary null hypothesis being tested by one-way ANOVA is that
the g population
means are equal. The null hypothesis is that there is a single mean. The
alternative hypothesis is that there are g individual means. Therefore, there are g-1--that is g (H1) minus 1 (H0)--degrees of
freedom for testing the null hypothesis. This accounts for the numerator
degrees of freedom for the F ratio.
There is another way of viewing the numerator degrees of freedom
for the F ratio. The null hypothesis says there is no variability in the g population means. There
are g sample means.
Therefore, there are g-1 degrees
of freedom for assessing variability among the g means.
By:
Telikepalli V Santosh Phani
Group C
Roll No: 14116,
Group C
Roll No: 14116,
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