1. Real Data: Collect Your Own
And Use It
Michael D. Miner
Assistant Professor – STEM - American Public University System
Adjunct Professor – Saint Leo University (Hampton Roads Campus)
Both come to present our ongoing research

2. Michael D. Miner Higher Education – 26years
Traditional – 4 years (USMA)
Institutional – 3 years (US Army Intelligence Center)
Nontraditional / Online – 15 years / 12 years
Current- APUS (Online): Assistant Professor, Lead Faculty for MATH302 (Stats), MATH200 (Analytic Geometry), MATH 229 (Data Analysis and Presentation), MATH 340 (Multivariate Statistics), MATH 440 (Stochastic Processes).
Saint Leo Univ. (Campus): Adjunct Professor; MAT 141 (College Algebra) MAT 201 (Introduction to Statistics), and GBA 334 (Decision Analysis)
Slide 2: The non-traditional learning environment primarily involves adult learners who have for various reasons returned to or initiated studies in higher education to be more competitive in a highly competitive job market. To that end, the preponderance of these adult learners seek comparable degrees relevant to career fields. The majority of the programs available to them involve a business statistics course or a related research course that introduces research and statistics used in business decision making. A key practice that is highly effective in reaching course objectives and enabling students’ understanding and success is to have students research current and relevant issues associated with their work environments, collect data on key variables, apply statistical processes, and provide a detailed report on findings along with the impact on the managerial decision making structure. This presentation will show the effectiveness of incorporating current and relevant real world problems that spans the statistics course. Two sample projects will be presented to show how students were successful in implementing changes in work environments as a result of completing the class long project.

3. Data, Data Everywhere, But Not a Drop to Use
Diane Kline
A statistics course should facilitate statistical thinking.
Results:
Appreciation of statistics
Collection
Organizing
Analyzing
Interpreting
Presenting
Thinking statistically in relevant situations
Slide 3: A statistics course should facilitate statistical thinking. Students should emerge from statistics classes with an appreciation for when and how the application of statistics in their professional or personal lives is warranted, and with a willingness to think statistically (or probabilistically) in relevant situations.
Gal and Ginsburg, 1994, The Role of Beliefs and Attitudes in Learning Statistics: Towards an Assessment Framework, Journal of Statistics Education v.2, n.2

4. What We Will Talk About Learning Objectives
First Class Data Collection Exercise
Using the Exercise to Support the Learning Objectives
Data collected used throughout the class
A key first night class participation exercise that is highly effective and engaging, a catalyst for critical thinking, an enabler for students to learn and understand course concepts, and breaks down the intense level of math anxiety (statistaphobia) that students come into class with is discussed in this presentation.
The first night of statistics class is a very frightful event for many learners. This presentation will highlight a technique used on the first night of statistics class that not only abates the fears of students but also engages students in a meaningful data collection exercise. The data collected is then used throughout the Introduction to Statistics course for critical thinking and analysis using statistical concepts.

5. Objectives 1. Understand Data Collection and Sampling Techniques
(Random Sampling, Stratified Random Sampling, Cluster Sampling, etc.)
2. Identify: Elements, Variables, and Observations
3. Identify the Types of Data
Qualitative (Categorical)
Quantitative
Discrete
Continuous
4. Identify the Levels (Scales) of Measurement
Nominal
Ordinal
Interval
Ratio
5. Use Tabular, Graphical, and Numerical Methods to Organize Data
Frequency Distributions
Bar Charts, Pie Charts, Histograms, etc.
Measures of Central Tendency
Measures of Dispersion
Measures of Position

6. First Class Exercise
Have each student come to the front of the class and introduce themselves. Structure the introduction to collect information on different variables. Use an Excel Spreadsheet
Here are some examples of data collected on students:
POO – Point of Origination: Where they were born
Number of Pets in Household – People in general love their pets
Birth Order - Given some idea of there personality
Service Affiliation – Provides and interesting perspective on statistical analysis!
Type of Car – Americans and their cars (‘nuff said)
Score on Last IQ Test – Most do not know or have not taken one ask for a good guess, interesting to see what they come up with.
Number of People in Work Space – Another quantitative variable
Statistical Process in Work Place – Reinforces the concepts of statistics are all around them.

7. First Class Exercise

8. Exercise vs. Objectives
1. Understand Data Collection and Sampling Techniques
(Random Sampling, Stratified Random Sampling, Cluster Sampling, etc.)
2. Identify: Elements, Variables, and Observations
VARIABLES
20 OBSERVATIONS
ELEMENTS

9. Exercise vs. Objectives
3. Identify the Types of Data
Qualitative
Quantitative
Discrete
Continuous
4. Identify the Levels of Measurement
Nominal
Ordinal
Interval
Ratio

10. Exercise vs. Objectives
5. Use Tabular and Graphical Methods to Organize Data
Frequency Distributions
Bar Charts, Pie Charts, Histograms, etc.
Age Group
Frequency
0-10
20-29 4
30-39 2
40-49 11
50+ 3
Total 20

11. Descriptive Statistics Tool Frequency Distribution Table
Other Concepts IQ
Descriptive Statistics Tool
Mean
116.8
Standard Error
2.71
Median
120
Mode
Standard Deviation
12.12
Sample Variance
146.91
Kurtosis
0.84
Skewness
-1.04
Range 45
Minimum 90
Maximum
135
Sum
2336
Count 20
Coefficient of Var.
10.38
Discrete Probability Distribution Table
Pets
P(x)
0.3 1
0.45 2
0.15 3
0.05 4
Total
Frequency Distribution Table
Pets
Frequency
Relative Frequency 6
6/20 1 9
9/20 2 3
3/20
1/20 4
Total 20
20/20
STEM and LEAF PLOT
# People in Work Place
Stem
Leaf 1 5 2 00 3 4 6 7 IQ
5 Number Sumary IQ
Min 90
25th
110
Median
120
75th
121.25
Max
135
Age Group
Compute Probability (Normal Distribution)
Interval Estimation (Confidence Intervals)
Hypothesis Testing (Means and Proportion)
Regression and Correlation Analysis
Chi Square Goodness of Fit

12. Final Exam Scenario

14. Serendipity
Builds Students’ critical thinking (Students find out something about each other, builds relationships)
Curbs students "statistics anxiety“
Providing some background information on the students’ (presentation skills, level of enthusiasm, Excel skills, and in some case “what moves them”)
Provides students a greater appreciation for data collection
Enabling students see first hand data collection and associated concepts in practice
Enhances the community of learners

15. Off the Stage

16. Questions? ?

17. Questions? ?