1. Bennie D Waller, Longwood University Business Statistics Bennie Waller wallerbd@longwood.edu 434-395-2046 Longwood University 201 High Street Farmville, VA 23901

2. Bennie D Waller, Longwood University What is Statistics

3. Bennie D Waller, Longwood University  Why should you study statistics? Statistics impacts our lives, both personally and professionally everyday.  Examples include the probability that our pizza arrives in 30 minutes or less to the likelihood that a particular toy will be available for Christmas to the probability and extent of the impact of poultry prices due to a bird flu outbreak.  Regardless of one’s concentration, statistics is involved. In the legal profession, medical profession and certainly in business.  You likely already know a great deal about statistics. For example, I bet you know the probability of getting a “Heads” on a single flip of the coin? Key Issues

4. Bennie D Waller, Longwood University  Descriptive Statistics – just as the name sounds, it provides a description of the data. According to the US Census, 2013 median household income was $52,250. Last year’s stats class had an average final grade of 82. Both of these are examples of descriptive statistics.  Inferential Statistics – just as its name implies, these statistics provide for an inference to be made based on a particular data set. For example, we could take a sample of 100 students to ascertain the average GPAs of all Longwood students. The larger the sample, the more precise the inference to the larger population. Key Issues

5. Bennie D Waller, Longwood University Key Objectives A population is a collection of all possible individuals, objects, or measurements of interest. A sample is a portion, or part, of the population of interest

6. Bennie D Waller, Longwood University  Qualitative variables – deals with attributes of interest that are non-numeric, e.g., race, hair color, sex  Quantitative variables – deal with numeric data such as income, home prices, temperature  Discrete variables – can only assume certain values. Examples include the number of students in a class or the number of bedrooms.  Continuous variables – can assume any value within a given range, e.g., weight, tire pressure. Key Issues

7. Bennie D Waller, Longwood University Types of data

8. Bennie D Waller, Longwood University Measures of Location and Dispersion

9. Bennie D Waller, Longwood University Measures of Location MEAN MEDIAN MODE # KIDS 1 2 3 4 5

10. Bennie D Waller, Longwood University Understanding Statistics A B

11. Bennie D Waller, Longwood University Measures of Dispersion  Most widely used measure of dispersion is variance and standard deviation.  Standard deviation measure the dispersion or volatility around a benchmark (mean or average)

12. Bennie D Waller, Longwood University Understanding Statistics A B

13. Bennie D Waller, Longwood University Measures of Dispersion

14. Bennie D Waller, Longwood University Empirical Rule 68%

15. Bennie D Waller, Longwood University Empirical Rule 95%

16. Bennie D Waller, Longwood University Empirical Rule 99.7%

17. Bennie D Waller, Longwood University Variance/Standard Deviation Class A Class B 9579 9575 9078 9076 1577 Mean77 Range804 Variance9662 Std Dev.31.081.41

18. Bennie D Waller, Longwood University Empirical Rule Comparison between A & B Mean77 Variance9662 Std Dev.31.081.41

19. Bennie D Waller, Longwood University Variance

20. Bennie D Waller, Longwood University Empirical Rule Consider an example of Pizza deliveries where the mean is 30 minutes with a standard deviation of 5 minutes. Using your knowledge of the empirical rule, what is the likelihood of a pizza arriving in <30 minutes?

21. Bennie D Waller, Longwood University Empirical Rule Consider an example of Pizza deliveries where the mean is 30 minutes with a standard deviation of 5 minutes. Using your knowledge of the empirical rule, what is the likelihood of a pizza arriving in < 20 minutes

22. Bennie D Waller, Longwood University Empirical Rule Consider an example of Pizza deliveries where the mean is 30 minutes with a standard deviation of 5 minutes. Using your knowledge of the empirical rule, what is the likelihood of a pizza arriving in > 35 minutes

23. Bennie D Waller, Longwood University Empirical Rule Consider an example of Pizza deliveries where the mean is 30 minutes with a standard deviation of 5 minutes. Using your knowledge of the empirical rule, what is the likelihood of a pizza arriving in >20 & <35 minutes?

24. Bennie D Waller, Longwood University End