# Descriptive much manageable form to be used in

Descriptive
statistics

1-      Definition:

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It is branch of statistics which deals with
quantitative information. It’s also known summary statistics

–
Descriptive
statistics aim actually to summarize or explore data of sample
instead of using these data to make predictions or inferences about population
of data (larger group from which sample is selected).

–
Descriptive
statistics summarizes quantitative data either by numerical measures
such as mean, mode, variance or visually organized forms such as bar charts,
tables, graph, histogram, frequency distribution tables, stem and leaf plot

–
Descriptive
statistics are distinguished from inferential statistics on
following basis. With descriptive statistics there are simple descriptions
about what data is trying to show done by analysis and summarizing these
collection of information. However in inferential statistics data is actually
used in different way where inferences and predictions are done which is beyond
immediate sets of data presented alone.

–
Descriptive
statistics simply present quantities data including numerical
facts and measures in much manageable form to be used in research studies

2-      Types of descriptive statistics (Main types)

There
are two major types of descriptive statistics depending on whether simple
summaries provided by descriptive statistics are whether quantitative (these
include various descriptive measures such as mean variance) or visual (these
include simple-understand graphs and tables)

Ø  Types of descriptive statistics:

1-      Organize Data:

–
Tables

–
Graphs

2-      Summarize Data:

–
Central Tendency.

–
Variation.

Summarizing
Data:

–
Central Tendency
(or Groups’ “Middle Values”)

ü  Mean

ü  Median

ü  Mode

–
Variation (or
Summary of Differences Within Groups)

ü  Range

ü  Interquartile
Range

ü  Variance

ü  Standard
Deviation

Central
tendency

It actually indicates a
typical value which will be considered as one central number best summarizes
entire set of values or measurements (This calculates
a central value of a data set)

Central tendency measures

`1. Mean:

–
Mean=Sum of
values/number of values

–
It is known as
the ((average)).This actually calculated by adding all values in series of
observation divided by their total number

–
It’s the most
common measure of central tendency

–
Mean works best
if distribution of data is very even across range or distributes in
normal-curve shape

2. Median:

–
It’s considered
as middle value which divides set of observations into two equal halves

–
Median formula:
(N+1)/2

–
It’s considered
as better measure of central tendency than mean if your
data are skewed

–
It’s not
influenced by outliers like mean so best works in case of unbalanced
observations.

3. Mode:

–
It’s the value
which occurs with greatest value of frequency in a set of data

–
It’s useful when
differences are rare or when the differences are non-numerical

–
It’s not always
a central value and a set of data can have actually more than one mode

Measures
of Variation

Variation
or Dispersion refers to another value which actually indicates how far the measurements
are from the mean or from that central value:

Ø  Range:

–
It’s the
difference between greatest and lowest value in a set of data

–
Range=X (max)-X
(min)

Ø  Variance:

–
It is the mean
deviation of all values from the mean.

–
It’s calculated
by getting deviation of each value alone then suaring it to finally add all
squared deviations divided by their (total number minus one)

–
It considers all
values in series of observation but being in squared units make it unable to be

Ø  Standard deviation:

–
It is actually
square root of variance

–
Standard
deviation is considered as the “average” degree to which scores
deviate from the mean.

3. Types of variables

In statistics, a
variable has two characteristics:

§  A
variable is an attribute that describes a person, place, thing, or idea.

Variables
can be qualitative (categorical) or quantitative (numeric).

Ø  Qualitative. Qualitative
categories that result in descriptive values or labels. For example the breed
of a dog (e.g., collie, shepherd) the color of a ball (e.g., red, green) would
be examples of qualitative or categorical variables.

Ø  Quantitative. Quantitative variables
are numeric. They represent a measurable quantity and can be presented
numerically. For example, what is the difference between having seven apples
and saying that they are delicious, we can count or measure the seven apples
but we can’t put a number for how delicious they are, saying you have seven
apples because they can be presented numerically is quantitative but saying
they are delicious is not quantitative that’s because you can’t measure or
write them in numbers.

4-importance
of descriptive statics

The importance of
descriptive statics

–
It enables us to
present data in a meaningful way.

–
Allows for a simpler interpretation of the data.
Therefore using descriptive statistics is useful in summarizing a group of data.