BUSINESS STATISTICS. MBA, Pokhara University. Bijay Lal Pradhan, Ph.D.

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1 BUSINESS STATISTICS MBA, Pokhara University Bijay Lal Pradhan, Ph.D.

2 WHY BUSINESS STATISTICS Most successful Manager and Decision makers understand the information and know how to use it effectively

3 COURSE CONTENT Introduction and Data Collection Summarization of Data Grouping and Displaying Data Numerical Descriptive Measures Basic Probability: Concepts and Applications. Probability Distributions Sampling Distribution and Estimation Hypothesis Testing Chi-Square Test and Analysis of Variance Correlation and Regression Analysis

4 BOOK Basic Books Levine, D. M., Krehbiel, T. C., Berenson, M. L., and Viswanathan, P. K., Business Statistics (Fourth Edition), New Delhi: Pearson Education. Levin, R. I. and Rubin, D. S., Statistics for Management (Seventh Edition), New Delhi: Prentice Hall. References Siegel, A. F., Practical Business Statistics (Fourth Edition), New York: Andrew F, Irwin. Anderson, D. R., Sweeney, D.J. and Williams, T. A., Statistics for Business and Economics (Eighth Edition), New Delhi: Thomson.

5 INTRODUCTION AND DATA COLLECTION Definition of statistics, Application in Business and Economics, Descriptive and Inferential Statistics, Types of Data (Categorical and Numerical), Classification of data (Crosssectional, Time series, Pooled), Sources of Data (Primary and Secondary), Census and Sampling, Parameter and Statistics, Data Collection Technique, Questionnaire Construction

6 DEFINITION OF STATISTICS In plural sense, the word statistics refer to numerical facts and figures collected in a systematic manner with a definite purpose in any field of study. In this sense, statistics are also aggregates of facts which are expressed in numerical form. For example, Statistics on industrial production, statistics or population growth of a country in different years etc. In singular sense, it refers to the science comprising methods which are used in collection, analysis, interpretation and presentation of numerical data. These methods are used to draw conclusion about the population parameter.

7 APPLICATION IN BUSINESS AND ECONOMICS Accounting: Sample Audit, different tools Finance: price/earning ratio, dividend yield (comparison with average or to other companies) Marketing: AC Nielsen (World s largest Chain Market Researcher ) Production: Statistical Quality Control, Forecast, Aggregate production planning. Economics: Forecast for future economy. Price Index, Unemployment rate, capacity utilization.

8 BRANCH OF STATISTICS (1) Descriptive Statistics: In descriptive statistics, it deals with collection of data, its presentation in various forms, such as tables, graphs and diagrams and findings averages and other measures which would describe the data. For Example: Industrial statistics, population statistics, trade statistics etc Such as businessman make to use descriptive statistics in presenting their annual reports, final accounts, bank statements.

9 BRANCH OF STATISTICS (2) Inferential Statistics: In inferential statistics, it deals with techniques used for analysis of data, making the estimates and drawing conclusions from limited information taken on sample basis and testing the reliability of the estimates. For Example: Suppose we want to have an idea about the percentage of illiterates in our country. We take a sample from the population and find the proportion of illiterates in the sample. This sample proportion with the help of probability enables us to make some inferences about the population proportion. This study belongs to inferential statistics

10 Descriptive & Inferential Statistics Statistics Descriptive Inferential Estimation Hypothesis Testing Tabular Graphical Point Interval Parametric Non-Parametric The methods of inferential statistics are applicable when results are obtained from a random. Uncertainty always remains while generalizing results from a sample to a population. The degree of uncertainty is measured in terms of probability in inferential statistics.

11 DATA AND ITS TYPE A characteristic or measurement that may different from one entity to another or place to place or time to time is called Data, which is able to distinguish among them. For eg. The measurement for height, weight, income, expenditure, demand etc. Data are collected for an investigation or research depending on the nature of the problem, they may relate to individuals, families, houses, village, business etc. The collected data are known as observations. Observations may be measured out of the 4 type of physical measurement.

12 DATA AND ITS TYPE The distinguishing of the observations from one outcome to other is called categorization. In other word categorization is a partition or a sub partition of total possible outcomes into different distinct groups or elements. The data refereeing to a single time point or a single space point ( or any single factor of the variable/attribute is a cross section data.)

13 DATA AND ITS TYPES The data, which are collected according to time variation (year, month, week, day, hours, minutes etc) (time series) The data, which are collected according to place, area, region etc (geographical / spatial) The data, which are placed to compare two or more variables, which is used to find out relationship between two or more variables and used for estimating one variable using known value of another variable. (Ordered data)

14 SOURCES OF PRIMARY AND SECONDARY DATA Primary Data collected by investigator from personal experimental studies for a specific research First hand data Collected when secondary data are unavailable and inappropriate Secondary The data (published or unpublished form which has collected by others for their purpose) can be utilized for study of another investigator, such data is said to be secondary data.

15 SOURCES OF PRIMARY AND SECONDARY DATA Source of Primary data Questionnaire survey (post, internet) Interview (personal/telephone) Focus group discussion Community forums and public hearing Observation Case studies Diaries Key informants interview

16 SOURCES OF PRIMARY AND SECONDARY DATA Source of Secondary data Usual public sources Nepal census of Household and Population, agriculture, business, vital statistics etc Governmental organization-national and district level use for development of society (office of ministry, municipality, district development office etc) Opinion and poll taken by others Health and microbial survey done by others INGO s, NGO s, UN publication Unusual sources: Easily accessible The yellow pages, Newspapers, Bulletin Board, Films, Post cards, old prints, Topographical maps etc

17 SOURCES OF PRIMARY AND SECONDARY DATA Problems in collecting primary data Timeframe, budgetary Transportation Non response error Biasness of enumerator Lack of expertise in construction of questionnaire and collection of data Problems in collecting secondary data Definition of terms and units If two set data comparison may make confusion Data may not be exact form of requirement Reliability and suitability

18 DATA COLLECTION TECHNIQUES Method of data collection Primary sources Secondary Sources Observation interviewing Questionnaire Documents Participa nts Non Participants Structure Non Structure Mailed Question naire Collectiv e Question naire Govt. Publication Earlier Research Census Personal Records Client Histories Service records

19 OBSERVATION Participant observations: researcher participates in the activities (as a member) with or without their knowledge that they are being observed. (involve as a prisoner to study the behavior & life of prisoners. Non-participant observation: do not get involve in activities but remains a passive observer. (function carried out by nurse observed)

20 STRUCTURE INTERVIEW Pre-determined set of questions /Interview schedule Face to face Telephone Other electronic media

21 UNSTRUCTURED INTERVIEW In-depth interview Focus group interview Narratives/ oral histories

22 QUESTIONNAIRE DESIGN Main instrument in survey Foundation of questionnaire is question It must translate research objective in to specific question Answer to such question provide data for hypothesis testing It must motivate the respondent so that necessary information is obtained

23 THE MAJOR CONSIDERATION Content Structure (type) Format Sequence

24 CONTENT Factual Background Environment Habits likes Opinion Attitude Behaviour Idea inclination

25 TYPES OF QUESTIONS Closed end questions Open end questions Contingency questions

26 FORMAT OF QUESTION Rating question Strongly agree, Agree, Disagree, strongly disagree, No opinion Matrix question Large set of rating questions, has same response categories Semantic differential Bio polar rating Good Bad Ranking question Placing objects according to relative order

27 ORDER OF QUESTION Random order Logical progression The Funnel Sequence Successive questions have narrower scope Is used when the topic itself motivate the respondent to give answer. The Inverted Funnel Sequence Narrower questions are followed by broader ones It is used when the topic of survey does not strongly motivate the respondent to communicate

28 PITFALL IN QUESTIONNAIRE CONSTRUCTION Wording of question (simple and everyday language) Response set (similar pattern questions) Leading questions Unemployment is increasing, is not it? Threatening questions (embarrassing) Presumption questions How many cigarettes do you smoke in a day? Double barreled questions How often and how much time do you spend in your visit? Does you organization have special recruitment policy for minorities and women?

29 SOME MORE INFORMATION Cover letter Should motivate to share the required information, include objectives and relevance of the study Instructions Clear understanding of the questions and way of giving answer

30 QUESTIONNAIRE Through post Through Enumerator Online Survey You can use googledocs (free of cost) or monkeysurvey different online survey tools

31 THE SAMPLING PROCESS POPULATION INFERENCE SAMPLE

32 REGARDING THE SAMPLE POPULATION (N) IS THE SAMPLE SAMPLE (n) REPRESENTATIVE?

33 REGARDING THE INFERENCE POPULATION (N) INFERENCE IS THE SAMPLE (n) INFERENCE GENERALIZABLE?

34 Sampling and its significance in research Sampling consists of obtaining information from only a part of a large group or population; and it indicates about the whole population. The objective of sampling is thus to secure a sample which will represent the population and reproduce the important characteristics of the population under the study as closely as possible. 34

35 The value calculated from a defined population, such mean (µ), standard deviation (σ), standard error of mean (s.e.) is called a parameter. It is a constant value because it covers all the members of the population. A value calculated from a sample is called statistic such as mean, Standard deviation and proportion. 35

36 CENSUS VS SAMPLING?? The data is the basic units in statistical analysis and inference; is either collected by experimentation or by sampling methods. Method of collection of statistical data by complete enumeration of the population is census. If the data collected by a certain group or part of population is called sampling enquiry. The principal advantages of sampling as compared to complete enumeration of the population are: Reduced cost Save time and speed up Greater scope and improved accuracy 36

37 SOME TERMINOLOGY USED IN SAMPLING a. Universe/Population: It is the set of object under study. In a census survey, all the universe or population is studied while in a sample survey an appropriate number of units called samples is selected and studied; the generalization is made for the universe or population from which the samples are drawn. b. Finite population: The number of items or the units under the study is known. c. Infinite population: The number of units of the items is unknown. d. Element: Each and every unit of population or universe is called element. An element constitutes one case for analysis. 37

38 SOME TERMINOLOGY USED IN SAMPLING e. Sampling unit: The smallest unit of population to be sampled is called sampling unit and on which observations can be made. f. Sample and sample size: An element or sampling unit from which information is collected is called a sample. A sample should be optimum, effective, representative, reliable and flexible. The term sample size refers to the number of items to be selected from the universe to constitute a sample. This is the number of respondents or units in the population included in a sample for studying the population. g. Sampling Frame or source list A list of all the units of population from which a sample is selected is called sampling frame. 38

39 SOME TERMINOLOGY USED IN SAMPLING h.parameter A coefficient or value for the population that corresponds to particular statistic from a sample is called parameter. A parameter is characteristic of population. For instance, mean, standard deviation, etc. i. Statistic It is characteristics of a sample and is hence computed from the actual data. j. Respondent A sampling unit from which information is collected is called respondent. k. Non- respondent Those respondents who were included in the sample but failed to respond because they refused, could not reach, or some other responses. 39

40 Define Population Specify the Sampling frame Specify the sampling unit The sampling method Selection of sampling method Determine the sample size Specify the sampling plan Select the sample 40

41 TYPES OF SAMPLING Random sampling 1. Simple random sampling a. Lottery method b. Use of random number 2. Stratified sampling Non Random sampling 1. Judgmental sampling 2. Convenient sampling 3. Quota sampling 4. Snow ball sampling 5. Purposive sampling 3. Cluster sampling 4. Systematic sampling 5. Multistage sampling 41

42 SIMPLE RANDOM SAMPLING Lottery Method Use of Random Numbers 42

43 Stratified Random sampling Each class is said to be strata If the population is heterogeneous then srs may not give representative data within class homogeneous; between class heterogeneous 43

44 Cluster sampling Each class is said to be cluster If the population is heterogeneous then srs may not give representative data within class heterogeneous; between class homogeneous 44

45 Systematic Sampling Sampling of households 45

46 A study of attitude of Nepalese people towards the Family Planning 5 developmental Region of Nepal 1st 2 nd 3 rd 4 th 5th 1st 4th

47 Multistage Sampling Development Region Different Anchal

48 Similarly Some districts can be taken as sample from the selected anchal Likewise VDC, Municipality ward number and house no can be taken as sample In this way there is Nepal Development region zone District VDC Municipality Ward no 48

49 Non random sampling Judgmental sampling: choice of sample items depends exclusively on the judgment of the investigator Convenience sampling: A sample obtained from readily available lists Quota sampling: In quotas are setup according to some specified characteristics and sample will be taken according to specified quota. Sampling will be depend upon the field representative

50 Snow ball method Assumption of this method is that if small ball is let roll from the top of snow-peak, it gathers substantial amount of snow and looks like a big ball when it arrives at the bottom of snow hill.

51 SAMPLING ERRORS It is the error of representativeness It is the difference between total population value and the sampling value The degree to which sample characteristics approximate the characteristics of total population. Sapling error = Statistics Parameter SSSSSSSSSSSSSSSS eeeeeeeeee αα 1 SSSSSSSSSSSS ssssssss Parameter 20 years Statistic 19 years Statistic 21 years Statistic 24 years