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  1. 1. Business  <ul><li>Applied Managerial Decision Making  </li></ul>
  2. 2. Qualitative attributes <ul><li>The following are the quality attributes that the snack food might need to ask the consumers:- </li></ul><ul><li>Storage level </li></ul><ul><li>technology </li></ul><ul><li>Energy size </li></ul><ul><li>Response time </li></ul>
  3. 3. Assign names for the end points <ul><li>Ordinal attribute endpoint names:- </li></ul><ul><li>Web Method- Tunnel- client-auth ID </li></ul><ul><li>Web Service- web service N </li></ul><ul><li>Energy size </li></ul><ul><li>Storage level </li></ul>
  4. 4. Difference between nominal and ordinal data <ul><li>The difference between nominal and ordinal data is that in nominal data, objects are given names which are considered to be labels whereas in the ordinal data, rank order is represented by the numbers given to various objects. </li></ul>
  5. 5. How nominal and ordinal data relate to a rating scale <ul><li>ordinal data relates to a rating scale through the scale of pH and Mohs scale of mineral hardness and the relation to less and greater is determined in addition to inequality and equality. It also relates in terms of measurement power. One can order and count but the ordinal data may not be measured. </li></ul><ul><li>Nominal data relates to a rating scale through the relation of inequality and equality and there is no presence of greater than and less than relations between the relation of classifications of names and the only central tendency measure is mode since both mean and median cannot be defined or explained, Data is put into categories without following any structure or order. One may not measure or order nominal data but one may count. </li></ul><ul><li>They also relate to rating scale because they both have statistical measures. </li></ul>
  6. 6. Quantitative attributes <ul><li>A quantitative attribute can be measured since it exists in magnitudes range. In relation to snack food the scientists may want to measure the following quantitative attributes: </li></ul><ul><li>Mass </li></ul><ul><li>Heat </li></ul><ul><li>Angular separation </li></ul><ul><li>vector and scalar quantities </li></ul><ul><li>distance </li></ul>
  7. 7. Difference between interval and ratio data <ul><li>The difference between interval and ratio data is that interval data is seen to be at a level of interval if there is a meaningful measurement of a continuous scale in a way that the real differences between quantities that are considered to be physical and are measured using scale corresponds to differences that are equal between scale values. Whereas ratio data is whereby there is a meaningful outcome in the ratio between two values or measurements. </li></ul><ul><li>It has a definition that is considered to be clear of 0.0.Examples of ratio data include weight, height and activity of enzyme. </li></ul>
  8. 8. Difference between interval and ratio data <ul><li>One example of level of interval group is a measurement collection of height. </li></ul><ul><li>All data of the level of interval can be put in the order of a rank that is the level of interval can be lowered to the data of ordinal level. </li></ul><ul><li>The data of internal level has more information in comparison to the data of ordinal level. </li></ul><ul><li>Ratio and interval data are put together as variables that are continuous. </li></ul>
  9. 9. Difference between a population and a sample <ul><li>The difference between Population and a sample is that a population is a region where by one is trying to receive data or certain information from whereas a sample is just a populations section that one is taking into consideration on going to survey it. One should note that it is very wise to select a sample where by the total population will be represented in order to reduce biases. </li></ul><ul><li>When conducting a research it is advisable not to be biased because one may not get the intended results and this may end up wasting your time and resources and the research will be tampered with. </li></ul>
  10. 10. Examples of possible populations for the test <ul><li>Teenagers between 13 years to around 30 years. </li></ul><ul><li>People without kids </li></ul><ul><li>People living in urban areas. </li></ul>
  11. 11. References <ul><li>Stevens, S.S. (1946). On the theory of scales of measurement. Science , 103, 677-680. </li></ul><ul><li>Velleman, P. F. & Wilkinson, L. (1993). Nominal, ordinal, interval, and ratio typologies are misleading . The American Statistician , </li></ul><ul><li>Aumann, Y. and Lindell, Y. (1999). A Statistical Theory for Quantitative Association Rules. </li></ul><ul><li>M. H. Haggag (2003) integration of quantitative and qualitative knowledge for online decision support . </li></ul><ul><li>Duncan, O. D. (1984). Notes on social measurement: historical and critical . New York: Russell Sage Foundation </li></ul>