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Stats 12

  1. 1. LESSON – 1 STATISTICS FOR MANAGEMENT Session – 1 Duration: 1 hr Meaning of Statistics The term statistics mean that the numerical statement as well as statistical methodology. When it is used in the sense of statistical data it refers to quantitative aspects of things and is a numerical description. Example: Income of family, production of automobile industry, sales of cars etc. These quantities are numerical. But there are some quantities, which are not in themselves numerical but can be made so by counting. The sex of a baby is not a number, but by counting the number of boys, we can associate a numerical description to sex of all newborn babies, for an example, when saying that 60% of all live-born babies are boy. This information then, comes within the realm of statistics. Definition The word statistics can be used is two senses, viz, singular and plural. In narrow sense and plural sense, statistics denotes some numerical data (statistical data). In a wide and singular sense statistics refers to the statistical methods. Therefore, these have been grouped under two heads – ‘Statistics as a data” and “Statistics as a methods”. Statistics as a Data Some definitions of statistics as a data are a) Statistics are numerical statement of facts in any department of enquiring placed in relation to each other. - Powley b) By statistics we mean quantities data affected to a marked extent by multiple of causes. - Yule and Kendall c) By statistics we mean aggregates of facts affected to a marked extent by multiplicity of causes, numerically expressed, enumerated or estimated according to reasonable standard of accuracy, collected in a systematic manner for pre- determined purpose and placed in relation to each other. - H. Secrist This definition is more comprehensive and exhaustive. It shows light on characteristics of statistics and covers different aspects. 1
  2. 2. Some characteristics the statistics should possess by H. Secrist can be listed as follows.  Statistics are aggregate of facts  Statistics are affected to a marked extent by multiplicity of causes.  Statistics are numerically expressed  Statistics should be enumerated / estimated  Statistics should be collected with reasonable standard of accuracy  Statistics should be placed is relation to each other. Statistics as a method Definition a) “Statistics may be called to science of counting” - A.L. Bowley b) “Statistics is the science of estimates and probabilities”. - Boddington c) Dr. Croxton and Cowden have given a clear and concise definition. “Statistics may be defined as the collection, presentation, analysis and interpretation of numerical data”. According to Croxton and Cowden there are 4 stages. a) Collection of Data A structure of statistical investigation is based on a systematic collection of data. The data is classified into two groups i) Internal data and ii) External data Internal data are obtained from internal records related to operations of business organisation such as production, source of income and expenditure, inventory, purchases and accounts. The external data are collected and purchased by external agencies. The external data could be either primary data or secondary data. The primary data are collected for first time and original, while secondary data are collected by published by some agencies. b) Organisations of data The collected data is a large mass of figures that needs to be organised. The collected data must be edited to rectify for any omissions, irrelevant answers, and wrong computations. The edited data must be classified and tabulated to suit further analysis. 2
  3. 3. c) Presentation of data The large data that are collected cannot be understand and analysis easily and quickly. Therefore, collected data needs to be presented in tabular or graphic form. This systematic order and graphical presentation helps for further analysis. d) Analysis of data The analysis requires establishing the relationship between one or more variables. Analysis of data includes condensation, abstracting, summarization, conclusion etc. With the help of statistical tools and techniques like measures of dispersion central tendency, correlation, variance analysis etc analysis can be done. e) Interpretation of data The interpretation requires deep insight of the subject. Interpretation involves drawing the valid conclusions on the bases of the analysis of data. This work requires good experience and skill. This process is very important as conclusions of results are done based on interpretation. We can define statistics as per Seligman as follows. “Statistics is a science which deals with the method and of collecting, classifying, presenting, comparing and interpreting the numerical data collected to throw light on enquiry”. Importance of statistics In today’s context statistics is indispensable. As the use of statistics is extended to various field of experiments to draw valid conclusions, it is found increased importance and usage. The number of research investigations in the field of economics and commerce are largely statistical. Further, the importance and statistics in various fields are listed as below. a) State Affairs: In state affairs, statistics is useful in following ways 1. To collect the information and study the economic condition of people in the states. 2. To asses the resources available in states. 3. To help state to take decision on accepting or rejecting its policy based on statistics. 4. To provide information and analysis on various factors of state like wealth, crimes, agriculture experts, education etc. b) Economics: In economics, statistics is useful in following ways 1. Helps in formulation of economic laws and policies 2. Helps in studying economic problems 3. Helps in compiling the national income accounts. 4. Helps in economic planning. 3
  4. 4. c) Business 1. Helps to take decisions on location and size 2. Helps to study demand and supply 3. Helps in forecasting and planning 4. Helps controlling the quality of the product or process 5. Helps in making marketing decisions 6. Helps for production, planning and inventory management. 7. Helps in business risk analysis 8. Helps in resource long-term requirements, in estimating consumer’s preference and helps in business research. d) Education: Statistics is necessary to formulate the polices regarding start of new courses, consideration of facilities available for proposed courses. e) Accounts and Audits: 1. Helps to study the correlation between profits and dividends enable to know trend of future profits. 2. In auditing sampling techniques are followed. Functions of statistics Some important functions of statistics are as follows 1. To collect and present facts in a systematic manner. 2. Helps in formulation and testing of hypothesis. 3. Helps in facilitating the comparison of data. 4. Helps in predicting future trends. 5. Helps to find the relationship between variable. 6. Simplifies the mass of complex data. 7. Help to formulate polices. 8. Helps Government to take decisions. Limitations of statistics 1. Does not study qualitative phenomenon. 2. Does not deal with individual items. 3. Statistical results are true only on an average. 4. Statistical data should be uniform and homogeneous. 5. A statistical result depends on the accuracy of data. 6. Statistical conclusions are not universally true. 7. Statistical results can be interpreted only if person has sound knowledge of statistics. 4
  5. 5. Distrust of Statistics Distrust of statistics is due to lack of knowledge and limitations of its uses, but not due to statistical sciences. Distrust of statistics is due to following reasons. a) Figures are manipulated or incomplete. b) Quoting figures without their context. c) Inconsistent definitions. d) Selection of non-representative statistical units. e) Inappropriate comparison f) Wrong inference drawn. g) Errors in data collection. Statistical Data Statistical investigation is a long and comprehensive process and requires systematic collection of data in large size. The validity and accuracy of the conclusion or results of the study depends upon how well the data were gathered. The quality of data will greatly influence the conclusions of the study and hence importance is to be given to the data collection process. Statistical data may be classified as Primary Data and Secondary Data based on the sources of data collection. ♦ Primary data Primary data are those which are collected for the first time by the investigator / researchers and are thus original in character. Thus, data collected by investigator may be for the specific purpose / study at hand. Primary data are usually in the shape of raw materials to which statistical methods are applied for the purpose of analysis and interpretation. ♦ Secondary data Secondary have been already collected for the purpose other than the problem at hand. These data are those which have already been collected by some other persons and which have passed through the statistical analysis at least once. Secondary data are usually in the shape of finished products since they have been already treated statistically in one or the other form. After statistical treatment the primary data lose their original shape and becomes secondary data. Secondary data of one organisation become the primary data of other organisation who first collect and publish them. 5
  6. 6. Primary Vs Secondary Data  Researcher originates primary data for specific purpose / study at hand while secondary data have already been collected for purpose other than research work at hand.  Primary data collection requires considerably more time, relatively expensive. While the secondary data are easily accessible, inexpensive and quickly obtained. Table – A compression of Primary and Secondary Data Primary data Secondary data Collection purpose For the problem at hand For other problems Collection process Very involved Rapid and easy Collection cost High Relatively low Collection time Long Short Suitability Its suitability is positive It may or may not suit the object of survey Originality It is original It is not original Precautions No extra precautions It should be used with required to use the data extra case Limitations of secondary data a) Since secondary data is collected for ‘some other purpose, its usefulness to current problem may be limited in several important ways, including relevancies and accuracy. b) The objectives, nature and methods used to collect secondary data may not be appropriate to present situation. c) The secondary data may not be accurate, or they may not be completely current or dependable. Criteria for evaluating secondary data Before using the secondary data it is important to evaluate them on following factors a) Specification and methodology used to collect the data b) Error and accuracy of data. c) The currency d) The objective – The purpose for which data were collected e) The nature – content of data f) The dependability 6
  7. 7. Sources of data Primary source – The methods of collecting primary data. When data is neither internally available nor exists as a secondary source, then the primary sources of data would be approximate. The various method of collection of primary data are as follows a) Direct personal investigation - Interview - Observation b) Indirect or oral investigation c) Information from local agents and correspondents d) Mailed questionnaires and schedules e) Through enumerations Secondary source – The methods of collecting secondary data i) Published Statistics a) Official publications of Central Government Ex: Central Statistical Organisation (CSO) – Ministry of planning - National Sample Survey Organisation (NSSO) - Office of the Registrar General and Census Committee – GOI - Director of Statistics and Economics – Ministry of Agriculture - Labour Bureau – Ministry of Labour etc. ii) Publications of Semi-government organisation Ex: - The institute of foreign trade, New Delhi - The institute of economic growth, New Delhi. iii) Publication of research institutes Ex: - Indian Statistical Institute - Indian Agriculture Statistical Institute - NCRET Publications - Indian Standards Institute etc. iv) Publication of Business and Financial Institutions Ex: - Trade Association Publications like Sugar factory, Textile mill, Indian chamber of Industry and Commerce. - Stock exchange reports, Co-operative society reports etc. 7
  8. 8. v) News papers and periodicals Ex: - The Financial Express, Eastern Economics, Economic Times, Indian Finance, etc. vi) Reports of various committees and commissions Ex: - Kothari commission report on education - Pay commission reports - Land perform committee reports etc. vii) Unpublished statistics - Internal and administrative data like Periodical Loss, Profit, Sales, Production Rate, Balance Sheet, Labour Turnover, Budges, etc. 8
  9. 9. LESSON – 1 STATISTICS FOR MANAGEMENT Session – 2 Duration: 1 hr Classification and Tabulation The data collected for the purpose of a statistical inquiry some times consists of a few fairly simple figures, which can be easily understood without any special treatment. But more often there is an overwhelming mass of raw data without any structure. Thus, unwieldy, unorganised and shapeless mass of collected is not capable of being rapidly or easily associated or interpreted. Unorganised data are not fit for further analysis and interpretation. In order to make the data simple and easily understandable the first task is not condense and simplify them in such a way that irrelevant data are removed and their significant features are stand out prominently. The procedure adopted for this purpose is known as method of classification and tabulation. Classification helps proper tabulation. “Classified and arranged facts speak themselves; unarranged, unorganised they are dead as mutton”. - Prof. J.R. Hicks ♦ Meaning of Classification Classification is a process of arranging things or data in groups or classes according to their resemblances and affinities and gives expressions to the unity of attributes that may subsit among a diversity of individuals. ♦ Definition of Classification Classification is the process of arranging data into sequences and groups according to their common characteristics or separating them into different but related parts. - Secrist The process of grouping large number of individual facts and observations on the basis of similarity among the items is called classification. - Stockton & Clark Characteristics of classification a) Classification performs homogeneous grouping of data b) It brings out points of similarity and dissimilarities. c) The classification may be either real or imaginary d) Classification is flexible to accommodate adjustments 9
  10. 10. Objectives / purposes of classifications i) To simplify and condense the large data ii) To present the facts to easily in understandable form iii) To allow comparisons iv) To help to draw valid inferences v) To relate the variables among the data vi) To help further analysis vii) To eliminate unwanted data viii)To prepare tabulation Guiding principles (rules) of classifications Following are the general guiding principles for good classifications a) Exhaustive: Classification should be exhaustive. Each and every item in data must belong to one of class. Introduction of residual class (i.e. either, miscellaneous etc.) should be avoided. b) Mutually exclusive: Each item should be placed at only one class c) Suitability: The classification should confirm to object of inquiry. d) Stability: Only one principle must be maintained throughout the classification and analysis. e) Homogeneity: The items included in each class must be homogeneous. f) Flexibility: A good classification should be flexible enough to accommodate new situation or changed situations. Modes / Types of Classification Modes / Types of classification refers to the class categories into which the data could be sorted out and tabulated. These categories depend on the nature of data and purpose for which data is being sought. Important types of classification a) Geographical (i.e. on the basis of area or region wise) b) Chronological (On the basis of Temporal / Historical, i.e. with respect to time) c) Qualitative (on the basis of character / attributes) d) Numerical, quantitative (on the basis of magnitude) 10
  11. 11. a) Geographical Classification In geographical classification, the classification is based on the geographical regions. Ex: Sales of the company (In Million Rupees) (region – wise) Region Sales North 285 South 300 East 185 West 235 b) Chronological Classification If the statistical data are classified according to the time of its occurrence, the type of classification is called chronological classification. Sales reported by a departmental store Sales Month (Rs.) in lakhs January 22 February 26 March 32 April 25 May 27 June 30 c) Qualitative Classification In qualitative classifications, the data are classified according to the presence or absence of attributes in given units. Thus, the classification is based on some quality characteristics / attributes. Ex: Sex, Literacy, Education, Class grade etc. Further, it may be classified as a) Simple classification b) Manifold classification i) Simple classification: If the classification is done into only two classes then classification is known as simple classification. Ex: a) Population in to Male / Female b) Population into Educated / Uneducated ii) Manifold classification: In this classification, the classification is based on more than one attribute at a time. 11
  12. 12. Ex: Population Smokers Non-smokers Literate Illiterate Literate Illiterate Male Female Male Female Male Female Male Female d) Quantitative Classification: In Quantitative classification, the classification is based on quantitative measurements of some characteristics, such as age, marks, income, production, sales etc. The quantitative phenomenon under study is known as variable and hence this classification is also called as classification by variable. Ex: For a 50 marks test, Marks obtained by students as classified as follows Marks No. of students 0 – 10 5 10 – 20 7 20 – 30 10 30 – 40 25 40 – 50 3 Total Students = 50 In this classification marks obtained by students is variable and number of students in each class represents the frequency. Tabulation Meaning and Definition of Tabulation Tabulation may be defined, as systematic arrangement of data is column and rows. It is designed to simplify presentation of data for the purpose of analysis and statistical inferences. 12
  13. 13. Major Objectives of Tabulation 1. To simplify the complex data 2. To facilitate comparison 3. To economise the space 4. To draw valid inference / conclusions 5. To help for further analysis Differences between Classification and Tabulation 1. First data are classified and presented in tables; classification is the basis for tabulation. 2. Tabulation is a mechanical function of classification because is tabulation classified data are placed in row and columns. 3. Classification is a process of statistical analysis while tabulation is a process of presenting data is suitable structure. Classification of tables Classification is done based on 1. Coverage (Simple and complex table) 2. Objective / purpose (General purpose / Reference table / Special table or summary table) 3. Nature of inquiry (primary and derived table). Ex: a) Simple table: Data are classified based on only one characteristic Distribution of marks Class Marks No. of students 30 – 40 20 40 – 50 20 50 – 60 10 Total 50 13
  14. 14. b) Two-way table: Classification is based on two characteristics No. of students Class Marks Boys Girls Total 30 – 40 10 10 20 40 – 50 15 5 20 50 – 60 3 7 10 Total 28 22 50 Frequency Distribution Frequency distribution is a table used to organize the data. The left column (called classes or groups) includes numerical intervals on a variable under study. The right column contains the list of frequencies, or number of occurrences of each class/group. Intervals are normally of equal size covering the sample observations range. It is simply a table in which the gathered data are grouped into classes and the number of occurrences, which fall in each class, is recorded. ♦ Definition A frequency distribution is a statistical table which shows the set of all distinct values of the variable arranged in order of magnitude, either individually or in groups with their corresponding frequencies. - Croxton and Cowden A frequency distribution can be classified as a) Series of individual observation b) Discrete frequency distribution c) Continuous frequency distribution a) Series of individual observation Series of individual observation is a series where the items are listed one after the each observation. For statistical calculations, these observation could be arranged is either ascending or descending order. This is called as array. 14
  15. 15. Ex: Marks obtained Roll No. in statistics paper 1 83 2 80 3 75 4 92 5 65 The above data list is a raw data. The presentation of data in above form doesn’t reveal any information. If the data is arranged in ascending / descending in the order of their magnitude, which gives better presentation then, it is called arraying of data. Discrete (ungrouped) Frequency Distribution If the data series are presented in such away that indicating its exact measurement of units, then it is called as discrete frequency distribution. Discrete variable is one where the variants differ from each other by definite amounts. Ex: Assume that a survey has been made to know number of post-graduates in 10 families at random; the resulted raw data could be as follows. 0, 1, 3, 1, 0, 2, 2, 2, 2, 4 This data can be classified into an ungrouped frequency distribution. The number of post-graduates becomes variable (x) for which we can list the frequency of occurrence (f) in a tabular from as follows; Number of post Frequency graduates (x) (f) 0 2 1 2 2 4 3 1 4 1 The above example shows a discrete frequency distribution, where the variable has discrete numerical values. 15
  16. 16. Continuous frequency distribution (grouped frequency distribution) Continuous data series is one where the measurements are only approximations and are expressed in class intervals within certain limits. In continuous frequency distribution the class interval theoretically continuous from the starting of the frequency distribution till the end without break. According to Boddington ‘the variable which can take very intermediate value between the smallest and largest value in the distribution is a continuous frequency distribution. Ex: Marks obtained by 20 students in students’ exam for 50 marks are as given below – convert the data into continuous frequency distribution form. 18 23 28 29 44 28 48 33 32 43 24 29 32 39 49 42 27 33 28 29 By grouping the marks into class interval of 10 following frequency distribution tables can be formed. Marks No. of students 0-5 0 5 – 10 0 10 – 15 0 15 – 20 1 20 – 25 2 25 – 30 7 30 – 35 4 35 – 40 1 40 – 45 3 45 – 50 2 16
  17. 17. LESSON – 1 STATISTICS FOR MANAGEMENT Session – 3 Duration: 1 hr Technical terms used in formulation frequency distribution a) Class limits: The class limits are the smallest and largest values in the class. Ex: 0 – 10, in this class, the lowest value is zero and highest value is 10. the two boundaries of the class are called upper and lower limits of the class. Class limit is also called as class boundaries. b) Class intervals The difference between upper and lower limit of class is known as class interval. Ex: In the class 0 – 10, the class interval is (10 – 0) = 10. The formula to find class interval is gives on below L−S i= R L = Largest value S = Smallest value R = the no. of classes Ex: If the mark of 60 students in a class varies between 40 and 100 and if we want to form 6 classes, the class interval would be 100 − 40 60 I= (L-S ) / K = = = 10 L = 100 6 6 S = 40 K=6 Therefore, class intervals would be 40 – 50, 50 – 60, 60 – 70, 70 – 80, 80 – 90 and 90 – 100. ♦ Methods of forming class-interval a) Exclusive method (overlapping) In this method, the upper limits of one class-interval are the lower limit of next class. This method makes continuity of data. 17
  18. 18. Ex: Marks No. of students 20 – 30 5 30 – 40 15 40 – 50 25 A student whose mark is between 20 to 29.9 will be included in the 20 – 30 class. Better way of expressing is Marks No. of students 20 to les than 30 5 (More than 20 but les than 30) 30 to les than 40 15 40 to les than 50 25 Total Students 50 b) Inclusive method (non-overlaping) Ex: Marks No. of students 20 – 29 5 30 – 39 15 40 – 49 25 A student whose mark is 29 is included in 20 – 29 class interval and a student whose mark in 39 is included in 30 – 39 class interval. ♦ Class Frequency The number of observations falling within class-interval is called its class frequency. 18
  19. 19. Ex: The class frequency 90 – 100 is 5, represents that there are 5 students scored between 90 and 100. If we add all the frequencies of individual classes, the total frequency represents total number of items studied. ♦ Magnitude of class interval The magnitude of class interval depends on range and number of classes. The range is the difference between the highest and smallest values is the data series. A class interval is generally in the multiples of 5, 10, 15 and 20. Sturges formula to find number of classes is given below K = 1 + 3.322 log N. K = No. of class log N = Logarithm of total no. of observations Ex: If total number of observations are 100, then number of classes could be K = 1 + 3.322 log 100 K = 1 + 3.322 x 2 K = 1 + 6.644 K = 7.644 = 8 (Rounded off) NOTE: Under this formula number of class can’t be less than 4 and not greater than 20. ♦ Class mid point or class marks The mid value or central value of the class interval is called mid point. (lower limit of class + upper limit of class) Mid point of a class = 2 ♦ Sturges formula to find size of class interval Range Size of class interval (h) = 1 + 3.322 log N Ex: In a 5 group of worker, highest wage is Rs. 250 and lowest wage is 100 per day. Find the size of interval. Range 250 − 100 h= = = 55.57 ≅ 56 1 + 3.322 log N 1 + 3.322 log 50 Constructing a frequency distribution The following guidelines may be considered for the construction of frequency distribution. 19
  20. 20. a) The classes should be clearly defined and each observation must belong to one and to only one class interval. Interval classes must be inclusive and non- overlapping. b) The number of classes should be neither too large nor too small. Too small classes result greater interval width with loss of accuracy. Too many class interval result is complexity. c) All intervals should be of the same width. This is preferred for easy computations. Range The width of interval = Number of classes d) Open end classes should be avoided since creates difficulty in analysis and interpretation. e) Intervals would be continuous throughout the distribution. This is important for continuous distribution. f) The lower limits of the class intervals should be simple multiples of the interval. Ex: A simple of 30 persons weight of a particular class students are as follows. Construct a frequency distribution for the given data. 62 58 58 52 48 53 54 63 69 63 57 56 46 48 53 56 57 59 58 53 52 56 57 52 52 53 54 58 61 63 ♦ Steps of construction Step 1 Find the range of data (H) Highest value = 70 (L) Lowest value = 46 Range = H – L = 69 – 46 = 23 Step 2 Find the number of class intervals. Sturges formula K = 1 + 3.322 log N. K = 1 + 3.222 log 30 K = 5.90 Say K = 6 ∴ No. of classes = 6 Step 3 Width of class interval Range 23 Width of class interval = = = 3.883 ≅ 4 Number of classes 6 20
  21. 21. Step 4 Conclusions all frequencies belong to each class interval and assign this total frequency to corresponding class intervals as follows. Class interval Tally bars Frequency 46 – 50 ||| 3 50 – 54 |||| ||| 8 54 – 58 |||| ||| 8 58 – 62 |||| | 6 62 – 66 |||| 4 66 – 70 | 1 Cumulative frequency distribution Cumulative frequency distribution indicating directly the number of units that lie above or below the specified values of the class intervals. When the interest of the investigator is on number of cases below the specified value, then the specified value represents the upper limit of the class interval. It is known as ‘less than’ cumulative frequency distribution. When the interest is lies in finding the number of cases above specified value then this value is taken as lower limit of the specified class interval. Then, it is known as ‘more than’ cumulative frequency distribution. The cumulative frequency simply means that summing up the consecutive frequency. Ex: ‘Less than’ Marks No. of students cumulative frequency 0 – 10 5 5 10 – 20 3 8 20 – 30 10 18 30 – 40 20 38 40 – 50 12 50 In the above ‘less than’ cumulative frequency distribution, there are 5 students less than 10, 3 less than 20 and 10 less than 30 and so on. Similarly, following table shows ‘greater than’ cumulative frequency distribution. 21
  22. 22. Ex: ‘Less than’ Marks No. of students cumulative frequency 0 – 10 5 50 10 – 20 3 45 20 – 30 10 42 30 – 40 20 32 40 – 50 12 12 In the above ‘greater than’ cumulative frequency distribution, 50 students are scored more than 0, 45 more than 10, 42 more than 20 and so on. Diagrammatic and Graphic Representation The data collected can be presented graphically or pictorially to be easy understanding and for quick interpretation. Diagrams and graphs give visual indications of magnitudes, groupings, trends and patterns in the data. These parameter can be more simply presented in the graphical manner. The diagrams and graphs help for comparison of the variables. Diagrammatic presentation A diagram is a visual form for presentation of statistical data. The diagram refers various types of devices such as bars, circles, maps, pictorials and cartograms etc. Importance of Diagrams 1. They are simple, attractive and easy understandable 2. They give quick information 3. It helps to compare the variables 4. Diagrams are more suitable to illustrate discrete data 5. It will have more stable effect in the reader’s mind. Limitations of diagrams 1. Diagrams shows approximate value 2. Diagrams are not suitable for further analysis 3. Some diagrams are limited to experts (multidimensional) 4. Details cannot be provided fully 5. It is useful only for comparison 22
  23. 23. General Rules for drawing the diagrams i) Each diagram should have suitable title indicating the theme with which diagram is intended at the top or bottom. ii) The size of diagram should emphasize the important characteristics of data. iii) Approximate proposition should be maintained for length and breadth of diagram. iv) A proper / suitable scale to be adopted for diagram v) Selection of approximate diagram is important and wrong selection may mislead the reader. vi) Source of data should be mentioned at bottom. vii) Diagram should be simple and attractive viii)Diagram should be effective than complex. Some important types of diagrams a) One dimensional diagrams (line and bar) b) Two-dimensional diagram (rectangle, square, circle) c) Three-dimensional diagram (cube, sphere, cylinder etc.) d) Pictogram e) Cartogram a) One dimensional diagrams (line and bar) In one-dimensional diagrams, the length of the bars or lines is taken into account. Widths of the bars are not considered. Bar diagrams are classified mainly as follows. i) Line diagram ii) Bar diagram - Vertical bar diagram - Horizontal bar diagram - Multiple (compound) bar diagram - Sub-divided (component) bar diagram - Percentage subdivided bar diagram i) Line diagram This is simplest type of one-dimensional diagram. On the basis of size of the figures, heights of the bar / lines are drawn. The distances between bars are kept uniform. The limitation of this diagram are it is not attractive cannot provide more than one information. 23
  24. 24. Ex: Draw the line diagram for the following data Year 2001 2002 2003 2004 2005 2006 No. of students passed in first 5 7 12 5 13 15 class with distinction 16 (15) 14 No. of students passed in FCD (13) (12) 12 10 8 (7) 6 (5) (5) 4 2001 2002 2003 2004 2005 2006 Year Indication of diagram: Highest FCD is at 2006 and lowest FCD are at 2001 and 2004. b) Simple bars diagram A simple bar diagram can be drawn using horizontal or vertical bar. In business and economics, it is very a common diagram. Vertical bar diagram The annual expresses of maintaining the car of various types are given below. Draw the vertical bar diagram. The annual expenses of maintaining includes (fuel + maintenance + repair + assistance + insurance). Type of the car Expense in Rs. / Year Maruthi Udyog 47533 Hyundai 59230 Tata Motors 63270 Source: 2005 TNS TCS Study Published at: Vijaya Karnataka, dated: 03.08.2006 24
  25. 25. 70000 65000 63270 59230 60000 55000 50000 47533 45000 40000 35000 30000 Maruthi Udyog Hyundai Tata Motors Source: 2005 TNS TCS Study Published at: Vijaya Karnataka, dated: 03.08.2006 Indicating of diagram a) Annual expenses of Maruthi Udyog brand car is comparatively less with other brands depicted b) High annual expenses of Tata motors brand can be seen from diagram. ♦ Horizontal bar diagram World biggest top 10 steel makers are data are given below. Draw horizontal bar diagram. US RIVA Thyssen- Tangshan Steel Arcelo Nippo BAO POSCO JFE Stee NUCOR krupp maker r Mittal n Steel l Prodn. in 110 32 31 30 24 20 18 18 17 16 million tonnes 25
  26. 26. Tangshan 16 Thyssen-krupp 17 Top - 10 Steel Makers RIVA 18 NUCOR 18 US Steel 20 BAO Steel 24 JFE 30 POSCO 31 Nippon 32 Arcelor Mittal 110 0 20 40 60 80 100 120 Production of Steel (Million Tonnes) Source: ISSB Published by India Today ♦ Compound bar diagram (Multiple bar diagram) Multiple bar diagrams are used to provide more information than simple bar diagram. Multiple bar diagram provides more than one phenomenon and highly useful for direct comparison. The bars are drawn side-by-side and different columns, shades hatches can be used for indicating each variable used. Ex: Draw the bar diagram for the following data. Resale value of the cars (Rs. 000) is as follows. Year (Model) Santro Zen Wagonr 2003 208 252 248 2004 240 278 274 2005 261 296 302 26
  27. 27. 350 296 302 300 278 274 261 252 240 248 250 Value in Rs. 208 200 150 100 50 0 1 2 3 Model of Car Santro Zen Wagnor Source: True value used car purchase data Published by: Vijaya Karnataka, dated: 03.08.2006 Ex: Represent following in suitable diagram Class A B C Male 1000 1500 1500 Female 500 800 1000 Total 1500 2300 2500 2500 2300 2500 Population (in Nos.) 1500 800 1000 2000 1500 500 1000 1500 1500 1000 500 0 1 2 3 Class Male Female 27
  28. 28. Ex: Draw the suitable diagram for following data Mode of Investment in 2004 in Rs. Investment in 2005 in Rs. investment Investment %age Investment %age NSC 25000 43.10 30000 45.45 MIS 15000 25.86 10000 15.15 Mutual Fund 15000 25.86 25000 37.87 LIC 3000 5.17 1000 1.52 Total 58000 100 66000 100 110 100 5.17 1.52 90 80 25.86 37.87 % of Investment 70 60 25.86 15.15 50 40 30 43.10 45.45 20 10 0 2004 2005 Year Two-dimensional diagram In two-dimensional diagram both breadth and length of the diagram (i.e. area of the diagram) are considered as area of diagram represents the data. The important two-dimensional diagrams are a) Rectangular diagram b) Square diagram a) Rectangular diagram Rectangular diagrams are used to depict two or more variables. This diagram helps for direct comparison. The area of rectangular are kept in proportion to the values. It may be of two types. i) Percentage sub-divided rectangular diagram ii) Sub-divided rectangular diagram 28
  29. 29. In former case, width of the rectangular are proportional to the values, the various components of the values are converted into percentages and rectangles are divided according to them. While later case is used to show some related phenomenon like cost per unit, quality of production etc. Ex: Draw the rectangle diagram for following data Expenditure in Rs. Item Expenditure Family A Family B Provisional stores 1000 2000 Education 250 500 Electricity 300 700 House Rent 1500 2800 Vehicle Fuel 500 1000 Total 3500 7000 Total expenditure will be taken as 100 and the expenditure on individual items are expressed in percentage. The widths of two rectangles are in proportion to the total expenses of the two families i.e. 3500: 7000 or 1: 2. The heights of rectangles are according to percentage of expenses. Monthly expenditure Item Family A (Rs. 3500) Family B(Rs. 7000) Expenditure Rs. %age Rs. %age Provisional stores 1000 28.57 2000 28.57 Education 250 7.14 500 7.14 Electricity 300 8.57 700 10 House Rent 1500 42.85 2800 40 Vehicle Fuel 500 12.85 1000 14.28 Total 3500 100 7000 100 29
  30. 30. Provisonal Stores Education Electricity House Rent Vehicle Fuel 100 80 % of Expenditure 60 40 20 0 A B Family b) Square diagram To draw square diagrams, the square root is taken of the values of the various items to be shown. A suitable scale may be used to depict the diagram. Ratios are to be maintained to draw squares. Ex: Draw the square diagram for following data 4900 2500 1600 Solution: Square root for each item in found out as 70, 50 and 40 and is divided by 10; thus we get 7, 5 and 4. 6000 5000 4900 4000 3000 2500 2000 1600 1000 5 7 4 0 1 2 3 30
  31. 31. Pie diagram Pie diagram helps us to show the portioning of a total into its component parts. It is used to show classes or groups of data in proportion to whole data set. The entire pie represents all the data, while each slice represents a different class or group within the whole. Following illustration shows construction of pie diagram. Draw the pie diagram for following data Revenue collections for the year 2005-2006 by government in Rs. (crore)s for petroleum products are as follows. Draw the pie diagram. Customs 9600 Excise 49300 Corporate Tax and dividend 18900 States taking 48800 Total 126600 Solution: Item / Source Value in Angle of circle %ge crores 9600 Customs 9600 x 360 = 27.30 o 7.58 126600 49300 Excise 49300 x 360 = 140.20 o 39.00 126600 18900 Corporate Tax and Dividend 18900 x 360 = 53.70 o 14.92 126600 48800 State’s taking 48800 x 360 = 138.80 o 38.50 126600 Total 126600 360o 100 31
  32. 32. 7.58 Customs 38.5 Excise 39 Corporate Tax and Dividend State’s taking 14.92 Source: India Today 19 June, 2006 Choice or selection of diagram There are many methods to depict statistical data through diagram. No angle diagram is suited for all purposes. The choice / selection of diagram to suit given set of data requires skill, knowledge and experience. Primarily, the choice depends upon the nature of data and purpose of presentation, to which it is meant. The nature of data will help in taking a decision as to one-dimensional or two-dimensional or three- dimensional diagram. It is also required to know the audience for whom the diagram is depicted. The following points are to be kept in mind for the choice of diagram. 1. To common man, who has less knowledge in statistics cartogram and pictograms are suited. 2. To present the components apart from magnitude of values, sub-divided bar diagram can be used. 3. When a large number of components are to be shows, pie diagram is suitable. Graphic presentation A graphic presentation is a visual form of presentation graphs are drawn on a special type of paper known are graph paper. Common graphic representations are a) Histogram b) Frequency polygon c) Cumulative frequency curve (ogive) 32
  33. 33. Advantages of graphic presentation 1. It provides attractive and impressive view 2. Simplifies complexity of data 3. Helps for direct comparison 4. It helps for further statistical analysis 5. It is simplest method of presentation of data 6. It shows trend and pattern of data Difference between graph and diagram Diagram Graph 1. Ordinary paper can be used 1. Graph paper is required 2. It is attractive and easily 2. Needs some effect to understand understandable 3. It is appropriate and effective to 3. It creates problem measure more variable 4. It can’t be used for further analysis 4. Can be used for further analysis 5. It gives comparison 5. It shows relationship between variables 6. Data are represented by bars, 6. Points and lines are used to represent rectangles data Frequency Histogram In this type of representation the given data are plotted in the form of series of rectangles. Class intervals are marked along the x-axis and the frequencies are along the y-axis according to suitable scale. Unlike the bar chart, which is one-dimensional, a histogram is two-dimensional in which the length and width are both important. A histogram is constructed from a frequency distribution of grouped data, where the height of rectangle is proportional to respective frequency and width represents the class interval. Each rectangle is joined with other and the blank space between the rectangles would mean that the category is empty and there are no values in that class interval. Ex: Construct a histogram for following data. Marks obtained (x) No. of students (f) Mid point 15 – 25 5 20 25 – 35 3 30 35 – 45 7 40 45 – 55 5 50 55 – 65 3 60 65 – 75 7 70 Total 30 33
  34. 34. For convenience sake, we will present the frequency distribution along with mid-point of each class interval, where the mid-point is simply the average of value of lower and upper boundary of each class interval. 7 Frequency (No. of students) 6 5 4 3 2 1 0 15 25 35 45 55 65 75 Class Interval (Marks) Frequency polygon A frequency polygon is a line chart of frequency distribution in which either the values of discrete variables or the mid-point of class intervals are plotted against the frequency and those plotted points are joined together by straight lines. Since, the frequencies do not start at zero or end at zero, this diagram as such would not touch horizontal axis. However, since the area under entire curve is the same as that of a histogram which is 100%. The curve must be ‘enclosed’, so that starting mid-point is jointed with ‘fictitious’ preceding mid-point whose value is zero. So that the beginning of curve touches the horizontal axis and the last mid-point is joined with a ‘fictitious’ succeeding mid-point, whose value is also zero, so that the curve will end at horizontal axis. This enclosed diagram is known as ‘frequency polygon’. Ex: For following data construct frequency polygon. Marks (CI) No. of frequencies (f) Mid-point 15 – 25 5 20 25 – 35 3 30 35 – 45 7 40 45 – 55 5 50 55 – 65 3 60 65 – 75 7 70 34
  35. 35. 10 8 A Frequency polygon 6 Frequency 4 2 0 0 10 20 30 40 50 60 70 80 90 100 Mid point (x) Cumulative frequency curve (ogive) ogives are the graphic representations of a cumulative frequency distribution. These ogives are classified as ‘less than’ and ‘more than ogives’. In case of ‘less than’, cumulative frequencies are plotted against upper boundaries of their respective class intervals. In case of ‘grater than’ cumulative frequencies are plotted against upper boundaries of their respective class intervals. These ogives are used for comparison purposes. Several ogves can be compared on same grid with different colour for easier visualisation and differentiation. Ex: Marks No. of Cum. Freq. Cum. Freq. Mid-point (CI) frequencies (f) Less than More than 15 – 25 5 20 5 30 25 – 35 3 30 8 25 35 – 45 7 40 15 22 45 – 55 5 50 20 15 55 – 65 3 60 23 10 65 – 75 7 70 30 7 35
  36. 36. Less than give diagram Less than Cumulative Frequency 30 'Less than' ogive 25 20 15 10 5 20 30 40 50 60 70 Upper Boundary (CI) Less than give diagram 35 30 'More than' ogive More than Ogive 25 20 15 10 10 20 30 40 50 60 70 Lower Boundary (CI) 36