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# Mgmt600 1002 A 03 P1 T1 Ip Carl Wills

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### Mgmt600 1002 A 03 P1 T1 Ip Carl Wills

1. 1. Applying Statistics to Business Decision Making<br />Carl Wills<br />MGMT600-1002A-03<br />Phase 1 Task 1 Individual Project<br />Professor Claude Superville<br />Colorado Technical University Online<br />April 9, 2010<br />
2. 2. Descriptive and Inferential Statistics<br />
3. 3. Snack Food Qualitative Attributes<br />Qualitative Variables<br />Ordinal – specific order or ranking such as<br />Pastry cake consumer satisfaction using a rating scale of one to five. Where five represents the highest level of satisfaction.<br />Ranking consumer confidence in which snack food brands are most desirable and <br />Nominal – measuring categorized responses such as <br />Gender, where consumers live, and favorite color etc. <br />(Levels of Measurement, n.d.) <br />
4. 4. Ordinal Attributes: Five Point Rating Scale<br />
5. 5. The Relationship between Nominal and Ordinal Data Using a Rating Scale<br />Nominal Data:<br />Data is nominal if the values / observations can be assigned a code (numbers) where the numbers are merely labels. For example, the code (number) of zero could indicate males and the code one could indicate females so on and so forth. <br />You can count nominal data but you can not place data in order or measure nominal data.<br />Ordinal Data:<br />Data is ordinal if the values / observations can be ranked (put in order) or by attaching a rating scale to it. <br />Ordinal data can be counted and placed in order as illustrated on the previous slide example of consumer satisfaction, but ordinal data cannot be measured.<br />
6. 6. Quantitative Attributes<br />Quantitative data is numerical.<br />Using quantitative data scientifically (i.e., Company W might want to consider):<br />Measuring snack food moisture content.<br />Caloric value such as sugar, fat, trans fat, and vitamin content etc.<br />
7. 7. Interval and Ratio Data<br />The difference between:<br />Interval:<br />Numerical.<br />Intervals have the same interpretation throughout.<br />Not perfect and have no true zero point.<br />Ratio:<br />Numerical and most informative.<br />Has a true zero point where the zero position indicates the absence of the quantity being measured.<br />
8. 8. Population, Sample, Avoiding Bias<br />Population: The upper case “N” represents the total population.<br />Nationally – N=6 million.<br />State – N=500,000<br />City – N=50,000<br />Sample: The lower case “n” represents the sample of the population.<br />Nationally – n=5000<br />State – n=500<br />City – n= 50<br />Bias<br />Evil intent.<br />Unintentional (i.e., miss representation of information, errors, etc.).<br />Possible populations for statistical analysis:<br />Mothers and children (ages between 12-16).<br />
9. 9. References<br />Bowerman, B. O’Connell, R. Orris, J. Murphree, E. (2010). Essentials of business statistics (3rd ed.). McGraw-Hill Irvin.<br />Colorado Technical University Online. (2010). Applied managerial decision-making: Task list. Retrieved April 2, 2010, from https://campus.ctuonline.edu/classroom/...<br />Croucher, J. (2001). Statistics: Making business decisions. McGraw-Hill<br />Levels of Measurements. (n.d.). Types of scales. Retrieved April 7, 2010, from http://onlinestatbook.com/chapter1/levels_of_measurement.html<br />Triola, M. (2008, p. 8). Elementary statistics (10th ed.). Pearson. Addison Wesley<br />
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