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[object Object],[object Object]
[object Object]
BROAD CLASSIFICATION 02/01/12 QUANTITATIVE TECHNIQUES STATISTICAL TECHNIQUES OPERATION RESEARCH (OR PROGRAMMING) TECHNIQUES
STATISTICAL TECHNIQUES ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
OPERATION RESEARCH (OR PROGRAMMING) TECHNIQUES ,[object Object],[object Object],[object Object],[object Object]
QT IN BUSINESS AND MANAGEMENT ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],02/01/12
QT IN BUSINESS AND MANAGEMENT ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],02/01/12
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],02/01/12 QT IN BUSINESS AND MANAGEMENT
Understanding Research ! ,[object Object],[object Object],[object Object],[object Object]
Objectives of Research ,[object Object],[object Object],[object Object],[object Object]
Features of a Good Research ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Ten Steps in the Marketing Research Process ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Step 1: Define the research problem I ,[object Object],[object Object],[object Object],[object Object]
Step 1: Define the research problem II ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Step 2: Establish Research Objectives ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Step 3: Research Design ,[object Object],[object Object]
Step 4: Specify the information required. Step 5: Design the method of collecting the needed information. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Step 6: Design the questionnaire. ,[object Object]
Step 7: Decide on the sampling design. Step 8: Manage and implement the data collection. ,[object Object],[object Object],[object Object],[object Object]
Step 9:Analyze and interpret the results. Step 10: Communicate the findings and implications. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Correlation Correlation measures the strength of a relationship between two variables.
Correlation determines whether values of one variable are related to another.
Independent and Dependent Variables ,[object Object],[object Object]
Example ,[object Object],[object Object],[object Object],[object Object],Student Hours studied % Grade A 6 82 B 2 63 C 1 57 D 5 88 E 3 68 F 2 75
Correlation Coefficient ,[object Object],[object Object],[object Object]
Positive and Negative Correlations ,[object Object],[object Object]
The Correlation Positive Correlation 0 < R < 1 No Correlation R = 0 Negative Correlation -1 < R < 0
Range of correlation coefficient ,[object Object],[object Object]
Range of correlation coefficient ,[object Object],[object Object]
Range of correlation coefficient ,[object Object]
Computational Formula for Correlation ,[object Object]
Cigarettes ( X ) Lung Capacity ( Y ) 0 45 5 42 10 33 15 31 20 29
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
Computing a Correlation
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
Example for correlation coefficient ,[object Object],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
Example for correlation coefficient ,[object Object],[object Object],[object Object],[object Object]
[object Object]
Example for correlation coefficient Shyness X Speeches Y 0 8 2 10 3 4 6 6 9 1 10 3
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
Alternative Formula for the Correlation Coefficient
 
Partial correlation ,[object Object],[object Object]
Partial correlation ,[object Object],[object Object]
[object Object],[object Object]
[object Object],[object Object]
Types of Partial correlation ,[object Object],[object Object]
Types of Partial correlation
 
 
 
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16497 mgt 252

Hinweis der Redaktion

  1. Whether it is a manufacturing unit, or a service organization, the resources have to be utilized to its maximum in an efficient manner.
  2. 5 5
  3. 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.
  4. 9 9
  5. 11 10
  6. 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.
  7. 15 13
  8. 16 15
  9. 17 16