This document discusses applying statistics to business decision making. It covers descriptive and inferential statistics, qualitative attributes like ordinal and nominal data, quantitative attributes that can be measured, and concepts like population, sample, and bias. Ordinal data involves ranking with a rating scale, while nominal data uses categorical responses. Quantitative data includes measurable values like moisture content and calorie levels. The document also distinguishes between interval and ratio levels of measurement and defines population and sample sizes for statistical analysis.

1. Applying Statistics to Business Decision Making Carl Wills MGMT600-1002A-03 Phase 1 Task 1 Individual Project Professor Claude Superville Colorado Technical University Online April 9, 2010

2. Descriptive and Inferential Statistics

3. Snack Food Qualitative Attributes Qualitative Variables Ordinal – specific order or ranking such as Pastry cake consumer satisfaction using a rating scale of one to five. Where five represents the highest level of satisfaction. Ranking consumer confidence in which snack food brands are most desirable and Nominal – measuring categorized responses such as Gender, where consumers live, and favorite color etc. (Levels of Measurement, n.d.)

4. Ordinal Attributes: Five Point Rating Scale

5. The Relationship between Nominal and Ordinal Data Using a Rating Scale Nominal Data: 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. You can count nominal data but you can not place data in order or measure nominal data. Ordinal Data: Data is ordinal if the values / observations can be ranked (put in order) or by attaching a rating scale to it. 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.

6. Quantitative Attributes Quantitative data is numerical. Using quantitative data scientifically (i.e., Company W might want to consider): Measuring snack food moisture content. Caloric value such as sugar, fat, trans fat, and vitamin content etc.

7. Interval and Ratio Data The difference between: Interval: Numerical. Intervals have the same interpretation throughout. Not perfect and have no true zero point. Ratio: Numerical and most informative. Has a true zero point where the zero position indicates the absence of the quantity being measured.

8. Population, Sample, Avoiding Bias Population: The upper case “N” represents the total population. Nationally – N=6 million. State – N=500,000 City – N=50,000 Sample: The lower case “n” represents the sample of the population. Nationally – n=5000 State – n=500 City – n= 50 Bias Evil intent. Unintentional (i.e., miss representation of information, errors, etc.). Possible populations for statistical analysis: Mothers and children (ages between 12-16).

9. References Bowerman, B. O’Connell, R. Orris, J. Murphree, E. (2010). Essentials of business statistics (3rd ed.). McGraw-Hill Irvin. Colorado Technical University Online. (2010). Applied managerial decision-making: Task list. Retrieved April 2, 2010, from https://campus.ctuonline.edu/classroom/... Croucher, J. (2001). Statistics: Making business decisions. McGraw-Hill Levels of Measurements. (n.d.). Types of scales. Retrieved April 7, 2010, from http://onlinestatbook.com/chapter1/levels_of_measurement.html Triola, M. (2008, p. 8). Elementary statistics (10th ed.). Pearson. Addison Wesley