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  • Whether it is a manufacturing unit, or a service organization, the resources have to be utilized to its maximum in an efficient manner.
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  • 7 7 Marketing problems may be difficulty-related or opportunity-related. For both, the prerequisite of defining the problem is to identify and diagnose it. Conduct situation analysis. It provides the basic motivation and momentum for further research.
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  • 14 11 Hypotheses: A hypothesis is a tentative statement that proposes a possible explanation to some phenomenon or event. Usually, a hypothesis is based on some previous observation such as noticing that in November many trees undergo color changes in their leaves and the average daily temperatures are dropping.
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  • 16497 mgt 252

    1. 2. <ul><li>Quantitative Technique is a scientific approach to managerial decision-making. </li></ul><ul><li>The successful use of Quantitative Technique for management would help the organization in solving complex problems on time, with greater accuracy and in the most economical way. </li></ul>
    2. 3. <ul><li>Suppose you watch a light flashing every 2 seconds, and another light flashing every 3 seconds, how would you calculate when the two lights would flash together? </li></ul>
    4. 5. STATISTICAL TECHNIQUES <ul><li>Methods of collecting Data </li></ul><ul><li>Classification and tabulation of collected data </li></ul><ul><li>Probability theory and sampling analysis. </li></ul><ul><li>Correlation and Regression Analysis </li></ul><ul><li>Index Numbers </li></ul><ul><li>Time Series Analysis </li></ul><ul><li>Ratio Analysis </li></ul>
    5. 6. OPERATION RESEARCH (OR PROGRAMMING) TECHNIQUES <ul><li>Linear Programming </li></ul><ul><li>Decision Theory </li></ul><ul><li>Theory of Games </li></ul><ul><li>Queuing Theory </li></ul>
    6. 7. QT IN BUSINESS AND MANAGEMENT <ul><li>MANAGEMENT </li></ul><ul><li>i) Marketing : </li></ul><ul><li> Selection of product mix, Sales resources allocation, analysis market research information, Sales forecasting </li></ul><ul><li>ii) Production </li></ul><ul><li> Production planning, control and analysis </li></ul><ul><li> Evaluation of machine performance </li></ul><ul><li> Quality control requirements </li></ul><ul><li> Inventory control measures </li></ul>02/01/12
    7. 8. QT IN BUSINESS AND MANAGEMENT <ul><li>iii) Finance, Accounting and Investment : </li></ul><ul><li> financial forecast, budget preparation, Cash flow analysis, Capital budgeting, Dividend and Portfolio management, Financial planning </li></ul><ul><li>iv) Personnel </li></ul><ul><li> labour turnover rate </li></ul><ul><li> employment trends </li></ul><ul><li> performance appraisal </li></ul><ul><li> wage rates and incentive plans </li></ul>02/01/12
    8. 9. <ul><li>ECONOMICS </li></ul><ul><li> measure of GNP. </li></ul><ul><li> determination of business cycle. </li></ul><ul><li> comparison of market prices etc. </li></ul><ul><li> analysis of population </li></ul><ul><li> formulation of appropriate economic policies </li></ul><ul><li>RESEARCH AND DEVELOPMENT </li></ul><ul><li> development of new product lines </li></ul><ul><li> optimum use of resources </li></ul><ul><li> evaluation of existing products </li></ul>02/01/12 QT IN BUSINESS AND MANAGEMENT
    9. 10. Understanding Research ! <ul><li>Research </li></ul><ul><ul><li>Literally, research (re-search) means “search again”. </li></ul></ul><ul><ul><li>Organized analysis of any subject based on “borrowed” materials, with suitable acknowledgement. </li></ul></ul><ul><ul><li>A systematic, careful inquiry or examination to discover new information about something, or establish new relationships between things, and to expand and verify existing knowledge for some specified purposes. </li></ul></ul>
    10. 11. Objectives of Research <ul><li>To find out answers to questions by applying systematic and scientific techniques. </li></ul><ul><li>To obtain familiarity of any phenomenon. </li></ul><ul><li>To determine association between variables. </li></ul><ul><li>To determine characteristics of an individual or group of activities and frequency of occurrence. </li></ul>
    11. 12. Features of a Good Research <ul><li>Objectivity </li></ul><ul><li>Control </li></ul><ul><li>Generalization </li></ul><ul><li>Free from personal bias </li></ul><ul><li>Systematic (well planned research design) </li></ul><ul><li>Reproducible </li></ul><ul><li>Revealing of limitations </li></ul><ul><li>Application of ethical standards </li></ul>
    12. 13. Ten Steps in the Marketing Research Process <ul><li>1. Define the Problem </li></ul><ul><li>2. Establish Research Objective </li></ul><ul><li>3. Determine Research Design </li></ul><ul><li>4. Identify Information Needs and Sources </li></ul><ul><li>5. Determine Methods of Data Collection </li></ul><ul><li>6. Design Instrument for Data Collection </li></ul><ul><li>7. Determine Sample Plan and Sample Size </li></ul><ul><li>8. Collect Data </li></ul><ul><li>9. Analyze Data </li></ul><ul><li>10. Prepare and Present Final Report </li></ul>
    13. 14. Step 1: Define the research problem I <ul><li>The very first, and the most important step in research: </li></ul><ul><ul><li>“ A problem well-defined is half solved” </li></ul></ul><ul><ul><li>Nature of the problem determines the type of study to conduct. </li></ul></ul><ul><li>A research problem must be accurately and precisely defined, otherwise the task of designing a good research difficult. </li></ul>
    14. 15. Step 1: Define the research problem II <ul><li>Get the right answer to the question: </li></ul><ul><ul><li>“ What exactly does the firm want (or need) to know?” </li></ul></ul><ul><li>The basic question to address is: </li></ul><ul><ul><li>“ How to know that there is a problem?” </li></ul></ul><ul><li>Problems may become apparent from: </li></ul><ul><ul><li>deviation from the business plan, company records and reports, customer complaints and grievances, conversations with company employees, and observation of inappropriate behavior or conditions in the firm; </li></ul></ul><ul><ul><li>the success of the firm’s competitors, and published materials reporting issues such as, changes in market or environmental trends, new government regulations, anticipated changes in the economy, etc.) </li></ul></ul>
    15. 16. Step 2: Establish Research Objectives <ul><li>“ If you do not know what you are looking for, you won’t find it” </li></ul><ul><li>Research objectives are related to and determined by the problem definition. In establishing research objectives, the researcher must answer the following questions: </li></ul><ul><ul><li>i) What specific information should the project provide? </li></ul></ul><ul><ul><li>ii) If more than one type of information will be developed from </li></ul></ul><ul><ul><li>the study, which is the most important? and finally, </li></ul></ul><ul><ul><li>iii) What are the priorities? </li></ul></ul><ul><li>When specifying research objectives, development of hypotheses, might be very helpful. </li></ul><ul><li>When achieved, objectives provide the necessary information to solve the problem. </li></ul>
    16. 17. Step 3: Research Design <ul><li>3. Research Design step involves the development of a research plan for carrying out the study. </li></ul><ul><ul><li>There are a number of alternative research designs. The choice will largely depend on the research purpose. </li></ul></ul>
    17. 18. Step 4: Specify the information required. Step 5: Design the method of collecting the needed information. <ul><li>4. After defining the problem the researcher must determine what kind of information will best meet the research objectives. </li></ul><ul><ul><li>Secondary information </li></ul></ul><ul><ul><li>Primary information </li></ul></ul><ul><li>5. Marketing research information may be collected in many ways: </li></ul><ul><ul><li>via mail, telephone, fax, Internet, or personal interview. </li></ul></ul><ul><ul><li>using consumer panels, consisting of individuals. </li></ul></ul>
    18. 19. Step 6: Design the questionnaire. <ul><li>A primary responsibilities of a marketing researcher is to design the data collection instrument or questionnaire in a manner so that it is easily understood by the respondent and administered to them. </li></ul>
    19. 20. Step 7: Decide on the sampling design. Step 8: Manage and implement the data collection. <ul><li>The researcher must determine the criteria that would enable a respondent to take part in a study. </li></ul><ul><ul><li>The sampling design must result in the proper sample of respondents being selected. Different sampling designs are available to researchers. </li></ul></ul><ul><li>The researcher must properly manage and oversee the data collection process. </li></ul><ul><ul><li>If interview method is used, the researcher must train interviewers and develop procedures for controlling the quality of the interviewing. </li></ul></ul>
    20. 21. Step 9:Analyze and interpret the results. Step 10: Communicate the findings and implications. <ul><li>The ‘raw’ research data needs to be edited, tabulated and analyzed to find the results and to interpret them. </li></ul><ul><ul><li>the method used may be manual or computer based. </li></ul></ul><ul><ul><li>The analysis plan follows from the research objective of the study. </li></ul></ul><ul><ul><li>Association and relationships of variables are identified and discussed in the light of the specific marketing problem. </li></ul></ul><ul><li>The researcher has to submit a written report and often make an oral presentation to management or the client. </li></ul><ul><ul><li>In conducting all the marketing research activities; the marketing researchers must adhere to ethical standards. </li></ul></ul>
    21. 22. The Correlation Correlation measures the strength of a relationship between two variables.
    22. 23. Correlation determines whether values of one variable are related to another.
    23. 24. Independent and Dependent Variables <ul><li>Independent variable: is a variable that can be controlled or manipulated. </li></ul><ul><li>Dependent variable: is a variable that cannot be controlled or manipulated. Its values are predicted from the independent variable. </li></ul>
    24. 25. Example <ul><li>Independent variable in this example is the number of hours studied. </li></ul><ul><li>The grade the student receives is a dependent variable. </li></ul><ul><li>The grade student receives depend upon the number of hours he or she will study. </li></ul><ul><li>Are these two variables related? </li></ul>Student Hours studied % Grade A 6 82 B 2 63 C 1 57 D 5 88 E 3 68 F 2 75
    25. 26. Correlation Coefficient <ul><li>The correlation coefficient computed from the sample data measures the strength and direction of a relationship between two variables. </li></ul><ul><li>The range of the correlation coefficient is. </li></ul><ul><li>- 1 to + 1 and is identified by r. </li></ul>
    26. 27. Positive and Negative Correlations <ul><li>A positive relationship exists when both variables increase or decrease at the same time. (Weight and height). </li></ul><ul><li>A negative relationship exist when one variable increases and the other variable decreases or vice versa. (Strength and age). </li></ul>
    27. 28. The Correlation Positive Correlation 0 < R < 1 No Correlation R = 0 Negative Correlation -1 < R < 0
    28. 29. Range of correlation coefficient <ul><li>In case of exact positive linear relationship the value of r is +1. </li></ul><ul><li>In case of a strong positive linear relationship, the value of r will be close to + 1. </li></ul>
    29. 30. Range of correlation coefficient <ul><li>In case of exact negative linear relationship the value of r is –1. </li></ul><ul><li>In case of a strong negative linear relationship, the value of r will be close to – 1. </li></ul>
    30. 31. Range of correlation coefficient <ul><li>In case of a weak relationship the value of r will be close to 0. </li></ul>
    31. 32. Computational Formula for Correlation <ul><li>By substituting and rearranging, you obtain a substantial (and not very transparent) formula for </li></ul>
    32. 33. Cigarettes ( X ) Lung Capacity ( Y ) 0 45 5 42 10 33 15 31 20 29
    33. 34. Computing a correlation Cigarettes ( X ) XY Lung Capacity ( Y ) 0 0 0 2025 45 5 25 210 1764 42 10 100 330 1089 33 15 225 465 961 31 20 400 580 841 29 50 750 1585 6680 180
    34. 35. Computing a Correlation
    35. 36. Example for correlation coefficient Student Age Blood Pressure A 43 128 B 48 120 C 56 135 D 61 143 E 67 141 F 70 152
    36. 37. Example for correlation coefficient <ul><li>Using the data on age and blood pressure, let’s calculate the  x,  y,  xy,  x 2 and  y 2 . </li></ul>Student Age Blood Pressure Age*BP age 2 BP 2 A 43 128 5504 1849 16384 B 48 120 5760 2304 14400 C 56 135 7560 3136 18225 D 61 143 8723 3721 20449 E 67 141 9447 4489 19881 F 70 152 10640 4900 23104 Sum 345 819 47634 20399 112443
    37. 38. Example for correlation coefficient <ul><li>Substitute in the formula and solve for r : </li></ul><ul><ul><li>r= {(6*47634)-(345*819)}/{[(6*20399)-345 2 ][(6*112443)-819 2 ]} 0.5 . </li></ul></ul><ul><ul><li>r= 0.897. </li></ul></ul><ul><li>The correlation coefficient suggests a strong positive relationship between age and blood pressure. </li></ul>
    38. 39. <ul><li>The conceptual formula for the correlation coefficient is a little daunting, but it looks like this: </li></ul>
    39. 40. Example for correlation coefficient Shyness X Speeches Y 0 8 2 10 3 4 6 6 9 1 10 3
    40. 41. Computational Example of r for the relationship between Shyness and Speeches (6 X 107) – 30 (32) [6 (230) – 30 2 ] [6 (226) – 32 2 ] r = -.797 Shyness X Speeches Y XY X 2 Y 2 0 8 0 0 64 2 10 20 4 100 3 4 12 9 16 6 6 36 36 36 9 1 9 81 1 10 3 30 100 9 30 32 107 230 226
    41. 42. Alternative Formula for the Correlation Coefficient
    42. 44. Partial correlation <ul><li>Sometimes it is desirable to know the relationship between two variables with the effects of a third variable held constant. </li></ul><ul><li>E.g: Both intelligence and number of hours worked are correlated with exam marks, and further that intelligence and number of hours worked are also correlated. </li></ul>
    43. 45. Partial correlation <ul><li>Partial correlation is the correlation of two variables while controlling for a third or more other variables. </li></ul><ul><li>Partial correlation coefficient  is a measure of the linear association between two variables after adjusting for the linear effect of a group of other variables. </li></ul>
    44. 46. <ul><li>Partial correlation analysis is important when studying relationship in linear form between more than two variables </li></ul><ul><li>It measures the strength of a linear relationship between two variables, while controlling the effect of other variables. </li></ul>
    45. 47. <ul><li>For example  r 12.34  is the correlation of variables 1 and 2, controlling for variables 3 and 4. </li></ul><ul><li>If partial correlation  r 12.34  is equal to uncontrolled correlation  r 12  , it implies that the control variables have no effect on the relationship between variables 1 and 2.. </li></ul>
    46. 48. Types of Partial correlation <ul><li>If the number of other variables is equal to 1, the partial correlation coefficient is called the first order coefficient. </li></ul><ul><li>If the number of other variables is equal to 2, the partial correlation coefficient is called the second order coefficient, and so on. </li></ul>
    47. 49. Types of Partial correlation
    48. 53. <ul><li>Problems </li></ul><ul><li>A. Calling exam marks (1), intelligence (2) and hours worked (3), and given r12 = .50, and r13=.40, and r23 of .40 </li></ul><ul><li>work out the value of r12.3. </li></ul>
    49. 55. <ul><li>Given three variables (1) prognosis in terms of weeks to recover, (2) an anxiety questionnaire, (3) a physiological measure, and r12= .40; r13= .30, and r23= .80, what is the correlation of the physiological measure with prognosis with the anxiety questionnaire results partialled out from both variables? </li></ul>
    50. 57. <ul><li>Partial correlation of (1) (actual sale price ÷ $1000) with (2) (living space), controlling for (3) (current taxes) and (4) (number of rooms) </li></ul>