This document provides an overview of managerial statistics and key statistical concepts. It discusses the definition and utility of statistics, as well as characteristics like studying aggregate facts affected by multiple causes. It also outlines how statistics are used in business functions like marketing, production, and finance. Additionally, it defines important statistical terms like population, sample, parameter, and discrete and continuous data. Finally, it discusses primary and secondary data sources and various methods for collecting primary data through interviews, questionnaires, and enumerators.

1. managerial statistics: an overview

2. “ there are three kinds of lies: lies, damned lies and statistics .”…… Benjamin Disraeli

3. Statistics: Statistics is a art and science pertaining to the collection, analysis, interpretation or explanation, and presentation of data.

4. Utility of statistics: To understand the reason for variation of a data and its causes to take better decision about a phenomenon that produced it.

5. Characteristics of statistics: It deals with aggregate of facts. These are affected to a marked extent by multiplicity of causes. These are numerically expressed. These are enumerated or estimated according to reasonable standard of accuracy. These are collected in a systematic manner. These are collected for a pre-determined purpose. These are placed in relation to each other.

6. Statistics in business management: Marketing Production Finance Personnel

7. Concept of sample and population: Population is the total group of respondents that the researcher wants to study. Populations are too costly and time consuming to study in entirety. Sample is a section of the population that we are actually going to survey. It is important to have a sample that will represent our entire population in order to minimize biases. E.g.: You want to know how American citizens feel about the war in Iraq. Your population: The United States. Your sample: 500 citizens selected randomly from each state.

8. Concept of statistics & parameter: Characteristic of a sample is called statistics and characteristic of a population, which is inferred on the basis of the sample statistics is called parameter .

9. Types of statistical methods: Descriptive Descriptive statistics includes statistical method involving the collection, presentation & characterization of a set of data in order to describe it’s various features. They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data. Inferential With inferential statistics, we are trying to reach conclusions that extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer from the sample data what the population parameter might be.

10. Limitations of statistics: It does not study qualitative phenomenon. It does not study individual fact or observation. It can be misused. Statistical results are true only on an average.

11. Data: Data are collection of any number of related observations. A collection of data is called a data set , and a single observation data point .

12. Qualitative & quantitative data: Qualitative Deals with descriptions. Data can be observed but not measured. E.g. color, texture, smell, beauty etc. Quantitative Deals with numbers. Data which can be measured. E.g. length, height, area, volume, speed etc.

13. Concept on discrete & continuous data: A type of data is discrete if there are only a finite number of values possible or if there is a space on the number line between each 2 possible values. Ex. A 5 question quiz is given in a Math class. The number of correct answers on a student's quiz is an example of discrete data. The number of correct answers would have to be one of the following : 0, 1, 2, 3, 4, or 5. Continuous data can take up any numerical value. This is a type of data that is usually associated with some sort of physical measurement. Ex. The height of trees at a nursery is an example of continuous data. Is it possible for a tree to be 76.2" tall? Sure. How about 76.29"? Yes. How about 76.2914563782"? Yes.

14. Sources of data: Primary source : source that itself collects the data. Secondary data : source that uses available data which were collected by some other agency.

15. Primary Data: Data never gathered before or first hand data. Advantage: find data you need to suit your purpose Disadvantage: usually more costly and time consuming than collecting secondary data

16. Types of Primary Data: Demographic/Socioeconomic Age, Sex, Income, Marital Status, Occupation Psychological/Lifestyle Activities, Interests, Personality Traits Attitudes/Opinions Preferences, Views, Feelings, Inclinations Awareness/Knowledge Facts about product, features, price, uses Intentions Planned or Anticipated Behavior Motivations Why People Buy (Needs, Wants, Wishes, Ideal-Self) Behavior Purchase, Use, Timing, Traffic Flow

17. Methods of collecting primary data: Direct personal interview Indirect oral interviews Information from correspondents Mailed questionnaire method Schedules sent through enumerators

18. Direct personal interview: There is face to face contact of the surveyor with the respondent. Merits: Respondent feels encouraged to answer. Information obtained is more correct as the interviewer can clear up the doubts pertaining to certain questions & can even cross-examine. Sensitive questions can be cleverly dealt after analyzing the psychology of the respondent. Language of communication could be adjusted if required. Demerits: Costly Chances of personal prejudice & bias is higher compared to other methods. Interviewer needs to be an expert in his area Time consuming Suitability: where intensive study of a limited field is desired

19. Indirect oral interview: Here the interviewer contacts third party capable of supplying necessary information. It is generally adopted in those cases where the information to be obtained is of complex nature and the informants are reluctant to respond if approached directly. E.g. information regarding addiction to drug, alcohol, visiting prostitutes etc.

20. Information from correspondent: Here the investigator appoints local agents or correspondents in different place to collect information. Newspaper agencies generally adopt this method. However the information may be affected by the personal prejudice and bias of the correspondent.

21. Mail questionnaire method: Here a list of questions pertaining to the survey is prepared and sent to the various informants by post. Merits: Most suitable when field of investigation is vast and informants are spread over wide geographical area. Relatively cost effective. Respondents can fill it in her own preferred time & may consult others if required. Demerits: Respondent needs to be literate, can’t be used for illiterates. Involves uncertainty about response. Information may be wrong & may be difficult to verify the accuracy.

22. Schedules sent through enumerators: Schedules ( is the name usually applied to a set of questions which are asked and filled in a face-to-face situation with another person ) are sent through interviewers or enumerators to get replies of the questions in the schedule from the respondents. Merit: Illiterate can participate as questionnaire is filled by enumerators. Rate of response is high as respondents are contacted personally. Demerits: Involves high cost Time consuming Training and expertise of enumerator is required

23. Secondary data: Secondary data is data collected by someone other than the user. Common sources of secondary data for social science include censuses, surveys, and organizational records.

24. Advantages and Disadvantages of secondary data: Advantages: Resource implications – usually easier to gather than primary data Cost & time effective Deficiencies & gaps can be identified easily and rectification measures could be take to overcome them. Quality and permanence of data – eg. government surveys Disadvantages: Suitability Cost and access – may still be difficult in spite of resource advantages Validity of some secondary data (eg. Internet sources)

25. Constructing the Questionnaire: Select the correct types of questions: open ended – harder to score but get “richer” information closed ended, dichotomous – offer two either/or responses (true/false; yes/no; for/against multiple choice – select one or more than one scaled response – gather range of “values” (strongly disagree, somewhat disagree, neutral, somewhat agree, strongly agree

26. Important characteristics of good questionnaires: Plan a user-friendly format. Gather demographic data – age, gender, etc., when necessary. Guarantee anonymity ( without name ). Ensure ease of tabulation. Ask well-phrased and unambiguous questions that can be answered. Develop for completeness – get all the data. Pilot test the instrument.

27. Sample Questionnaire:

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