Geo Centrism Concept

Geo Centrism Concept

Geo centrism concept is basically combination of ethnocentrism and poly centrism concept of internal business. Geo centrism approach does take into account the local culture and incorporate local requirement in the overall direction or business plans of company.

1.    Combined & Balanced Approach

Geo centrism approach is combined and balance approach of business. It does consider the foreign market as new market with some requirements (Local requirements), but it also values the strategic direction of organization.

2.    Cultural Requirement Considered

Cultural requirement of a market considered for market and other business plans. Organization makes required amendment in overall policies to incorporate local requirements. It means in Geo centrism approach, operational policies can be amended & implemented by local management.

3.    Overall Direction

In this approach overall direction or strategic direction of the organization does not lost, because policies are not totally changed due to local requirement ,rather local requirement are incorporated case to case bases ,and such changes mostly relates to operations, and does not change or effect strategic directions.

4.    Centered & Local Decision Making

In geo centrism approach some decision are made by the central management, and some decision are taken at local level. it means that strategic and major decision are taken by the key management and market and operational decision are made by local management.

Polycentrism Concept

Poly-centrism Concept

Poly centrism approach of doing business internationally is opposite to ethnocentrism approach. In poly centrism approach the local market is given due importance for decision making relating to that market (Local Market). It means each market is independent evaluated for decision making. Characteristics of poly centrism have been explained below;

1.    Value Different Cultures

Poly centrism recognizes the importance of different cultures, and different culture values are incorporated in business strategies and decision making process. In fact new policies are made based on the cultural requirement of local market.

2.    Decentralization

Poly centrism is primarily decentralization concept, and decisions are taken at local level keeping in view the local market conditions. It means local management is empowered to take appropriate decisions under this approach.

3.    Local Research

Local research is conducted for evaluating & understanding the local market & demand.  Such research results are incorporated in business plans of the company. These plans are more realistic in nature.

4.    Total New Market Concept

Overseas market is considered a total new market, and therefore new business plans and policies are required to be framed for new market.

5.    Lack control & Standardization

Poly centrism approach lacks control & standardization within organization. In one organization so may plans and strategies are being implemented. It means it is difficult to set one strategic direction under this approach.

6.    Timely Decisions

Poly centrism concept or strategy of doing business facilitates timely decisions. it means that local management can take all required decision in time, we know that timely decision are critical for cost and damage controls.

Characteristics of Ethnocentrism

Median Definition

Median Concept

Median is a point which divides the ordered data in two equal points. It is fundamental for median calculation that data is in order i.e. data is arranged from lower value to high value. Median can also be defined as point below which 50% order data lies.

Median Formula

Median has two different formulas for two different cases i.e. n/2 integer and non integer.

1)    [{n/2}+1]th observation ( where n/2 is integer

2)    [n+1]/2 th observation  (where n/2 is not integer)

Median & Quin tiles

Median as well as quin-tile may be calculated for large data, typically there are three types of quintiles calculated Q1, Q2, and Q3 which divided the ordered data into 25%, 50% and 75% respectively.

Q1 = [n/4+1] th

Q3 = [3n/4] th observation

Index Number Concept

Index Number Concept

Index number tells about the average change in a variable over the period of time or with respect of time.

Index Number Base

Index number (change) is calculated with respect of a base period. Base has fundamental importance in index number calculation and with the help of base comparison is possible.

Index Number Types

Index number can be classified into two types i.e. simple index number & composite index number. Single index number deals with single variable (calculated for single variable), while composite index number may be calculated for number of variables.

Index Number Construction Steps

Index number construction process involves four steps i.e. selection of commodities and their prices, selection of bases, section of average, and selection of weights.

1.    Selection of commodities

Index number construction first step is selection of commodities which are to be included for calculating index number for whole sale price. There is no hard and fast rule for such selection, however, there is a general principal for such selection i.e. larger is number greater would be the accuracy.

2.    Selection of Base

Index number construction second step is to select a base for index number. There are two methods of selection a base i.e. fixed based & Chain based method. In fixed base method a year is chosen as base which does not change over the life of index number, while in chain base method base changes i.e. immediately preceding year is regarded as base.

3.    Selection of average

Index number construction third step is to choose an appropriate average which includes arithmetic mean, geometric mean and median.

4.    Selection of weights

Index number construction last step is to choose an appropriate weight. Weights are used to identify relative importance of a commodity. There are two types of weights i.e. base year weighting & current year weighting.

Graph Concept

Graph Concept

Graph is used to represent time series (continuous data change over the period of time).  example variation of weather during the day etc.

Graph Advantages

1.    Series Comparison

Graph facilitates comparison of two or more series, for example the score card of a cricket match effectively presents the progress of both team.

2.    Prediction

Graph facilitates future prediction & forecast. Graph basically shows a trend of performance of variable and such trend can be used to predict future value.

Graph Types

Graph can be broadly classified into two types i.e. Time series Graph & Frequency Distribution Graph.

1.    Time Series Graph

Graph in the form of curve which shows the changes in a variable over the period of time is known as time series. Time is shown over the x-axis while the other dependant variable is shown on y-axis.

2.    Frequency Distribution Graph

Frequency distribution graph can be further classified into histogram, frequency polygon, and frequency curve.

i.        Histogram

Graph of adjacent rectangle with marked bases with class boundaries. Area of rectangle shows the frequency, if class interval is same, then width will be same of all rectangles and length will show the frequency position, otherwise width & length both will be changed. Length presents the number of observation (frequency).

ii.        Frequency polygon

Graph constructed with help of class mark and related frequency is known as frequency poly gone. Class marks are shown on x-axis while the frequency is shown on y axis, the connecting the points (class mark & related frequency) will result in frequency polygon.

iii.        Frequency Curve

Graph with small class interval and large observation result in frequency curve and therefore effectively Frequency curve is extension of histogram & frequency polygon.

Types of Frequency Curves

Types of frequency curves include symmetrical curve, moderately skewed (asymmetrical), and extremely skewed (J shape) and U shaped distribution.

1       .    Symmetrical Curve

Curve which has equal distance from the central maximum point is known as symmetrical curve, for example a normal curve.

2       .    Asymmetrical Curve

Curve which both end tail are of different length is known as asymmetrical.

3       .    Extremely Skewed

    Extremely skewed curve where most of frequency fall at one end of the curve. This curve         also known as J curve.

4        .    U Shaped curve

U shaped curve frequency at both end goes to maximum, these types of curves are rarely found.

Diagram Concept

Diagram Concept

Diagrams are used to present the data in pictorial form. Diagram are used , where same type of data is presented in pictorial form over period of time.

Diagrams Advantages

Diagram are used for number of advantages

1.    Attractive

Diagrams are more attractive than table, and therefore widely used for presentation.

2.    User Friendly

Diagrams are easier to understand than figures and therefore have long lasting effect on user.

3.    Comparison

Diagram facilitates the comparison between two periods.

Diagram Disadvantages

1.    Complex Data

Diagram cannot be used for complex data presentation; these are only suitable for limited and simple data.

2.    Additional Job

Diagram is time consuming job because diagram is prepared from the classified data. Therefore first data is collected & classified, and then diagrams are prepared from classified data.

Diagram Types

1.    Linear Diagram

Diagram which has one dimension is known as linear diagrams. Examples of linear Diagrams are simple bars, multiple bars. Bars will have equal width but different length, where length presents the value, while width has no significance.

Single diagram used to describe single characteristics of a variable for example production of cotton over period.  Multiple bars are used to describe two characteristics of variable. for example cotton production & area used.

2.    Areal or Two Dimension Diagram

Diagram which has two dimensions are known as two dimension Diagrams. Rectangles and sub divided rectangles are example of two dimension diagram. Rectangles use both length and width to represent the variable value.

3.    Cubical or Three Dimensional Diagram

Diagram which have more than two dimensions are known as Cubical or three dimension diagram.

4.    Pie-Diagram

Diagram which present data in the form of circles or sector is known as pie gram. Size of sector is proportional to the value.

5.    Pictograph

Diagram which uses small pictures or symbols are known as pictograph

Statistical Population Concept

Statistical Population Concept

Population is set of all possible observation. For example all students in a school is population. Statistical population is not necessarily required to represent a whole population of area; rather it is a group of people which is being studied.

Statistical Population Size

Statistical population size means the number of possible outcome or observation of finite population. For example we studying height of 9^th class student, then population size is would be total number of student in the 9th class.

Statistical Population Parameter

Statistical population mean the numeric value assigned to population. For example the average height of 9^th class student is 5 feet that was calculated from the population of 50 student of 9^th class. Height of 5 feet is parameter (represent population).

Statistical Population Types

Population can be broadly classified as under

1.    Finite Statistical population

Population which represents or consists of a fixed number of values is known as finite population. For example number of student in school, number of student in class, number of flights from Karachi airport.

2.    Infinite Statistical population

Population which size is not exactly known is regarded as infinite population. Infinite population size is so large that no one can exactly tell about size. For example, Particles of sand in a desert, number of point on a line.

3.    Real Statistical Population

Population that represents real values is known real population i.e. age, height, weight, length.

4.    Hypothetical Statistical population

Population may comprise of hypothetical events or observation i.e. possible outcome of coin toss, possible outcome of a cricket match, etc.

Secondary Data Concept

Secondary Data Concept

Secondary data is a data which is not originally collected for a purpose, such data collected by someone else for some purpose. Secondary data has already gone through a statistical process / technique.

Secondary Data Sources

Secondary data sources include Government, Semi Government, Trade union, research & educational institution, professional magazine and news paper.

Secondary Data Advantages

1.    Low Cost

Secondary data first advantage is its low cost. There is very little cost involved in collected such data. For example you need to buy a professional magazine from the market and this is the total cost for your data collection.

2.    Time Saved

Secondary data second advantage is saving of time because data is readily available and therefore can immediately be used for the research or other purposes.

Secondary Data Disadvantages

1.    Not updated

Secondary first disadvantage is its non update ion issue because it is originally collected by the current user.

2.    Difficult to use

Secondary data second disadvantage is difficulty faced by the user for using the data because it may be available in a form and format which may be difficult to manipulate by current user.

Primary Data Concept

Primary Data Concept

Primary Data is a kind of data which is originally collected for a purpose. Such data is then processed by using different statistical technique for decision making, prediction etc.

Primary Data Collection Methods

Primary data Collection methods includes direct interview, indirect interview or investigation, questionnaires,

1.    Direct investigation

Interested person collect the data personally. Data collected under this method is regard to be accurate and relevant to the need of interested person.

2.    Indirect Investigation

Under this method interested person hire some person for data collection. Primary reason to hire other person is difficult to get data. For example male researcher cannot get information about pregnancy related disease.

3.    Questionnaire

Under this method data is collected by designing a questionnaire. This methods cheap and provides exact question. However, this method is effective if questionnaire has been well designed, questions are well structure, and data is properly recorded in the forms.

4.    Enumerator

Under this method questionnaire is filled by the trained enumerator , there are number of advantages for using trained enumerator for form /questionnaire filling i.e. low rate of mistakes, high response, efficiency etc.

Primary Data Advantages

Primary data advantages includes more relevant, update (current data)

1.    Relevance of Data

Primary data is regarded as more relevant to purpose and therefore more useful for decision making and analyses.

2.    Current Data

Primary data main advantage includes its updated form. Therefore primary data is regarded as more reliable for statistical analyses. Secondary data on other side may not be current and therefore not useful.

Descriptive Statistics Concept

Descriptive Statistics Concept

Descriptive statistics means the presentation of data with the help of numbers, tables, graph, and summaries.

Descriptive Statistics Importance

Descriptive statistics converts raw data into information. Such information can be used for different purposes including planning, decision making.Descriptive statistics play important role in data manipulation. Different kind of summaries and averages are calculated for data analyses and decision making.

Descriptive Statistics Uses

Descriptive statics is used for data analyses, decision making, presentations to the management, planning, goal setting, etc.

1.    Data Analyses

Descriptive statistics basic uses include data analyses. In business world data analyses is a fundamental for effective decision making.

2.    Decision Making

Descriptive statics provides necessary tools and information to meet decision making need of management.

3.    Planning

Descriptive statics is used in business planning & budgeting. Descriptive statistics provides necessary information for

future planning including budgeting.