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Statistics is a section of
mathematics tangled with the variety, allotment, analysis, and explanation of
numerical assurance. Statistics is the submission of data (Business
Dictionary). There are compelling
number true samples where statistics are worn. For example, you and a friend are at a basketball game,
and he proposes you a bet that neither team will bump a home run in that game.
Should you take the bet?(Statistics/Introduction/What is Statistics)

There are mainly two types of statistics, descriptive statistics and
inferential statistics. Descriptive statistics accord data that portray
the information in some way. It administers a short synopsis decision of
information. Data can be encapsulated numerically or graphically as you want.
For example, suppose a clothing shop sells pants, t-shirt and shocks. If 100
cloths are sold, and 40 out of the 100 were t-shirt, then one description of
the data on the clothssold would be that 40% were t-shirt. (Descriptive
& Inferential Statistics: Definition, Differences & Examples)

In inferential statistics we benefit
capricious illustration of data which are appropriated from a population to portray
and provoke conjectures regarding the number. For example, if you want to identify
the average height of all the women in a town with a population of so many
million residents. It will not be practical to get the height of each woman. In
this case inferential statistics arrives into play. Inferential statistics are scarce
when it is not appropriate or possible to review each member of a complete
population. (Descriptive & Inferential
Statistics: Definition, Differences & Examples)

A variable will be at whatever attribute,
figure, or quantity that can be consistent or counted. A variable might furthermore be
called a data component.
Gender, business income, birth rate, expenditure, class grades, hair color are
examples of variables.(What are Variables?)

Given below is the flow chart of types of variables:

 

 

Numeric
variables have amount that portrays a perceptible quantity as a
number, like ‘how many’ or ‘how much’. That’s why numeric
variables are quantitative variables. Categorical variables includes amount that portrays ‘quality’
or ‘distinctive’ of a data unit, like ‘what kind or ‘which class’.
Therefore, categorical variables are qualitative variables and add to
be characterized by a non-numeric value. A continuous
variable is a numeric variable. The conclusion can obtain any value among
a persuaded set of real figures. The value given to a
conclusion for a continuous variable can comprise values as tiny as the
apparatus of analysis allows. A continuous variable includes altitude, moment,
era, and heat. A discrete variable is a numeric variable. A conclusion
can obtain a value based on a tally from a set of specific whole
values. A discrete variable cannot take the value of a fraction of
one value and the next closest value. Examples of discrete variables
include the number of registered cars, number of business locations, and
number of children in a family, all of which measured as whole units (i.e.
1, 2, 3 cars).An ordinal variable is a categorical variable.
Conclusions can take a value that can be reasonably arranged or graded. Examples
of ordinal categorical variables include academic grades (i.e. A, B, C),
clothing size (i.e. small, medium, large, extra-large). A nominal
variable is a categorical variable. Conclusions can take a value that
is not able to be coordinated in a cogent sequence. Examples
of nominal categorical variables include sex, business type, eye color,
religion and brand (What are
Variables?)

                                  
Frequency table and its associated terms

 

Frequency table is a system which exhibit crude information in the
manifestation which one can undoubtedly see those data held in the crude
information.

Frequency distributions are of two types:

1.      Discrete frequency distribution: The transform for
get ready this kind from claiming circulation is extremely
straightforward. The development of a discrete frequency circulation
from the provided for crude information may be carried eventually by
perusing the consumption of the system for count marks. In the initially
section of the frequency table we compose every last bit could reasonably be
expected qualities of the variable starting with the least of the most
noteworthy

2.      Continuous or grouped frequency distribution: whereas
constructing a grouped frequency distribution table, at first data are
collected and differentiated into groups which are called classes. Normally, we
employ 5 to 20 classes

Example of a frequency table and
relative frequency table

Performance

Frequency

Relative Frequency

Relative Frequency
Percentage

Early

25

25/100=0.25

25%

On- time

64

64/100=0.64

64%

Late

9

9/100=0.09

9%

Lost

2

2/100=0.02

2%

Total

100

1

100%

 

                                               
Chart and Graph

 

 Chart is a set of steep bars whose areas are
commensurate to the frequencies. In the histogram, variable is consistently
taken on the horizontal axis and frequencies on the vertical axis. The graphs
are used to admit the attribute of discrete and continuous data. Two frequency
distributions can be compared by the shapes and patterns. (Frequency
Distribution)

There are two types of data and they are described as below:

Quantitative data: Quantitative data is numerical. Such as the number of cars,
grades, number of students. This can be diagrammed. If you poll or part, you
are assembling quantitative data. The quantitative data can be of two types:
discrete and continuous data.

Qualitative
data: Qualitative
are descriptive data which cannot be counted like, the types of a dogs, perfume
smell, design of a shirt etc

                                                    
Measure of Location

 

Mean, Median and Mode are the three mainly used measure of location.

 

1.     
Mean:
A mean is synonymous with the average. 
This may be the best measure for symmetrical distributions. Mean is
impacted by every last bit information and most reliable.

2.     
Median:
the median is the worth in the centre of a set of data. Median does not represent
amazing scores. It may be not algebraically characterized.

3.      
Mode:  the mode is the most frequent value, number
or category in a set of data. One way to remember this definition is that mode
sounds like most.

                                                 
Measure of Dispersion

 

The measure of dispersion follows the measures of central tendency so
the common measures of dispersion are standard deviation and variance.

Dispersion is the degree of variation in the data. For example, the age
of instructors {48, 49, 50, 51, 52}

Range is the difference between the maximum and minimum observations.
For example, the minimum age of an instructor was 29 and maximum age was 73.

 

Standard deviation is the square root of the variance. The variance is
in square units so the standard deviation is in the same units as x

 

 

 

                                      Displaying and exploring
data

 

A dot plot bands the data in as little arena
as possible and classify of an individual conclusion is not lost. To evolve a
dot plot, each observation is simply shown as a dot along a horizontal number
line revealing the possible values of the data.

Stem and leaf: One technique that is used to array
quantitative clues in a concise form is the stem and leaf array. It is a
statistical technique to present a set of data. Each numerical value is cleft
into two parts. The dominant digit becomes the stem and the hunting digit the
leaf. The stems are located along the vertical axis and the leaf values are
stacked against each other along the horizontal axis.

Box plot: it is a graphical array, planted on quartiles,
that helps us picture a set of data. To construct a box plot, we need five
statistics;

1.     
The
minimum value

2.     
The
first quartile (Q1)

3.     
The
median

4.     
The
third quartile (Q3) and

5.     
The
maximum value

Skewness: Another
attribute of a set of data is the shape. There are four shapes commonly
observed;

1.     
Symmetric

2.      
Positively
skewed

3.      
Negatively
skewed

4.      
Bimodal

The coefficient of skewness can range from -3 to +3. A
value near -3, reveal negative skewness, a value such as 1.63 reveal moderate
positive skewness and a value of 0, which will occur when the mean and median
are equal, reveals the circulation is symmetrical and that there is no skewness
present.

PEARSON’S COEFFICIENT OF SKEWNESS, sk=   3(x?- Median) /
s

SOFTWARE
COEFFICIENT OF SKEWNESS, sk = n / (n-1) (n-2) x [? (x-x?/s)3]

 

 

Describing
relationship between two variables:

When
we review the connection between two variables we refer to the data as bivariate.
One graphical approach we use to show the connection between variables is
called scatter diagram. To stalemate a scatter diagram we need two variables.
We scale one variable along the horizontal axis of a graph and the other
variable along the vertical axis

Contingency Tables: A
contingency table is a cross-tabulation that concurrently compiles two
variables of interest. For examples:

1.     
Students at a university are classified
by gender and class rank.

2.     
A
product is classified as acceptable or unacceptable and by the shift(day,
afternoon, or night) on which it is manufactured.(McGraw/Hill, 2015)

                                                        
Probability

Probability is analogous to
percentage. Probability is a section of mathematics that deals with calculating
the likelihood of a given event’s occurrence, which is assert as a number
between 1 and 0 (TechTarget).
When the probability is 0, that is an absurd event and if the probability is 1,
that is a sure event. For example, when child will born, if the probability of
girl child is 0.6 then the probability of boy child will be 0.4 because total
should be 1.

 

Probability of an event = the number of ways event A can
occur

                                          The total number of possible
outcomes

For example:

Here, we have to find the
probability of either X and Y

P(X) =0.07

P(Y) =0.02

P(X or Y) =?

As we know,

The probability of either X and Y
occurring is = P(X) +P(Y)

                                                                         
=0.07+0.02

                                                                        
 =0.09

The probability of neither X and Y
occurring is= 1-P

                                     
                                     =
1-0.09

                                           
                             
 =0.01

 

 

 

 

                   The
company that I have researched is manufacturing company of noodles. You can’t
manage what you can’t measure. This company has been using statistics for the
following functions:

 

·        
To forecast the production, whether
there is a stable demand and uncertain demand.

·        
To know the risk that is associated
within the operations and financial costs.

·        
To calculate the given information to
show the statistical outcome.

Statistics can
help in increasing not only the quality of products but also the quantity that
are being manufactured. Statistics can also help support the quality in the
areas of the benefits of the business process, those mechanical and building
processes. For the likelihood for similarity as considerably dependent upon
date, and real time feedback, quality can be expanded almost instantly.