Statistics is a section of

mathematics tangled with the assortment, allotment, analysis, and clarification

of numerical certainty. Statistics is the application 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 depict

the information in some way. It administers a shortsynopsis judgment 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 know

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 acomplete

population. (Descriptive & Inferential

Statistics: Definition, Differences & Examples)

A variable will be at whatever attribute,

figure, or quantity that can beconsistent or counted. A variable might furthermore be

called a datacomponent.

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 a ‘quality’

or ‘distinctive’ of a data unit, like ‘what type’ or ‘which class’.Therefore,

categorical variables are qualitative variables and contribute to be

characterized by a non-numeric value.A continuous variable is a

numeric variable. The conclusion can take any value between a convinced

set of real numbers. The value given to a conclusion for a

continuous variable can include values as small as the apparatus of

analysis allows. A continuous variable includes height, time, age, and

temperature.A discrete variable is a numeric variable. Aconclusion

can take a value based on a count from a set of definite 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 utilization 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: While

constructing a grouped frequency distribution table, at first data are

collected and differentiated into groups which are called classes. Normally, we

use 5 to 20 classes

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 compile 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

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.