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Goal Sharing Team Training Statistical Thinking and Data Analysis (I). Peter Ping Liu, Ph D, PE, CQE, OCP and CSIT Professor and Coordinator of Graduate Programs School of Technology Eastern Illinois University Charleston, IL 61920. Meet the Instructor. BS, MS and Ph D in Engineering. - PowerPoint PPT Presentation
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Goal Sharing Team Training
Statistical Thinking and Data Analysis (I)
Peter Ping Liu, Ph D, PE, CQE, OCP and CSITProfessor and Coordinator of Graduate Programs
School of TechnologyEastern Illinois University
Charleston, IL 61920
Meet the Instructor
• BS, MS and Ph D in Engineering.• Registered Professional Engineer (PE) in
Illinois.• Certified Quality Engineer (CQE).• Oracle Certified Professional (OCP).• Research: Biomedical materials, total
replacement implants, database and quality management.
Goals for the Training
• To be able to measure work performance (and goals) quantitatively and objectively—Goal setting and achieving.
• To be able to understand the data (goals) across the organization – Goal sharing.
Objectives
• To have fun.• To learn something
useful.
Data: A Way of Life
• Data is everywhere we go and in everything we do.
• Examples: time, salary, ???• Our challenge is how to use the
data to our benefits.
Data Summary: Finding the basic facts
• We use a simple example to illustrate ways to organize data in order to find some basic facts.
120 153 186 117 140
165 125 128 129 120
123 132 111 117 93
205 130 112 120 180
150 130 120 140 118
130 126 166 110 112
110 185 105 112 132
125 150 116 95 145
119 135 118 139 150
125 112 116 114 125
117 116 95
The following table shows weights of college students.
Statistical thinking I:
Data has to tell a true story.
Statistical thinking II:
Data has to be organized to become useful (information).
Step 1: Tabulate the data into one column (Due to space limitation, the column was broken into 3 pieces.)
120
165
123
205
150
130
110
125
119
125
153
125
132
130
130
126
185
150
135
112
116
186
128
111
112
120
166
105
116
118
116
117
129
117
120
140
110
112
95
139
114
140
120
93
180
118
112
132
145
150
125
Step 2: Sort the data from the largest to the smallest
205
…
120
120
120
120
119
118
118
117
117
117
…
…
93
Data Interpretation: Minimum, Maximum and Range.
• Minimum value: smallest, shortest, lightest.
• Maximum value: largest, tallest, heaviest.
• Range=Maximum value – Minimum value.
Statistical thinking III:
Range is related to the consistency.
Smaller range means better consistency. In many applications, our objective is to achieve the best consistency, or smallest range.
Step 3: Divide the entire range approximately into 10 cells (parts/divisions).
200-209
190-199
…
…
90-99
Step 4: Tally each data point.
Weight Tally
200 - 209 /
190 - 199
180 – 189 ///
170 – 179
160 – 169 //
150 – 159 ////
140 - 149 ///
130 – 139 ///// //
120 – 129 ///// ///// //
110 – 119 ///// ///// ///// //
100 – 109 /
90 – 99 ///
Worksheet: Tally each data point.
Tally
Statistical thinking IV:
Historical data can be used to predict future performance.
Step 5: Frequency (Number of Observations)
Weight Tally Frequency
200 - 209 / 1
190 – 199 0
180 – 189 /// 3
170 – 179 0
160 – 169 // 2
150 – 159 //// 4
140 - 149 /// 3
130 – 139 ///// // 7
120 – 129 ///// ///// // 12
110 – 119 ///// ///// ///// // 17
100 – 109 / 1
90 – 99 /// 3
Total 53
Worksheet: Frequency (Number of Observations)
Tally Frequency
Step 6a: Relative Frequency (Proportion) = Frequency/Total
Weight Tally Frequency Relative Frequency (Proportion)
200 - 209 / 1 0.018868
190 – 199 0 0.00
180 – 189 /// 3 0.056604
170 – 179 0 0.00
160 – 169 // 2 0.037736
150 – 159 //// 4 0.075472
140 - 149 /// 3 0.056604
130 – 139 ///// // 7 0.132075
120 – 129 ///// ///// // 12 0.226415
110 – 119 ///// ///// ///// // 17 0.320755
100 – 109 / 1 0.018868
90 – 99 /// 3 0.056604
Total 53 1.0
Worksheet: Relative Frequency (Proportion) = Frequency/Total
Tally Frequency Relative Frequency (Proportion)
Step 6b: Relative Frequency (Percentage)= (Frequency/Total)x100Weight Tally F Relative Frequency
(Proportion)Relative Frequency (Percentage)
200 - 209 / 1 0.018868 1.8868
190 – 199 0 0.000000 0.0000
180 – 189 /// 3 0.056604 5.6604
170 – 179 0 0.000000 0.0000
160 – 169 // 2 0.037736 3.7736
150 – 159 //// 4 0.075472 7.5472
140 - 149 /// 3 0.056604 5.6604
130 – 139 ///// // 7 0.132075 13.2075
120 – 129 ///// ///// // 12 0.226415 22.6415
110 – 119 ///// ///// ///// // 17 0.320755 32.0755
100 – 109 / 1 0.018868 1.8868
90 – 99 /// 3 0.056604 5.6604
Total 53 1.0 100
Worksheet: Relative Frequency (Percentage)= (Frequency/Total)x100Tally F Relative Frequency
(Proportion)Relative Frequency (Percentage)
What weight range has the highest frequency?
Step 7a: Cumulative Frequency: Total number of observations at or below the class (value)
Weight Tally Frequency Cumulative Frequency
200 - 209 / 1 53
190 – 199 0 52
180 – 189 /// 3 52
170 – 179 0 49
160 – 169 // 2 49
150 – 159 //// 4 47
140 - 149 /// 3 43
130 – 139 ///// // 7 40
120 – 129 ///// ///// // 12 33
110 – 119 ///// ///// ///// // 17 21
100 – 109 / 1 4
90 – 99 /// 3 3
Total 53
Worksheet: Cumulative Frequency: Total number of observations at or below the class (value)
Tally Frequency Cumulative Frequency
Step 7b: Cumulative Frequency: Cumulative ProportionWeight Tally F Cumulative
FrequencyCumulative Proportion
200 - 209 / 1 53 1.00
190 – 199 0 52 0.98
180 – 189 /// 3 52 0.98
170 – 179 0 49 0.92
160 – 169 // 2 49 0.92
150 – 159 //// 4 47 0.89
140 - 149 /// 3 43 0.81
130 – 139 ///// // 7 40 0.75
120 – 129 ///// ///// // 12 33 0.62
110 – 119 ///// ///// ///// // 17 21 0.40
100 – 109 / 1 4 0.08
90 – 99 /// 3 3 0.06
Total 53
Worksheet: Cumulative Frequency: Cumulative ProportionTally F Cumulative
FrequencyCumulative Proportion
Step 7c: Cumulative Frequency: Cumulative PercentWeight F Cumulative
FrequencyCumulative Proportion
Cumulative Percent
200 - 209 1 53 1.00 100
190 – 199 0 52 0.98 98
180 – 189 3 52 0.98 98
170 – 179 0 49 0.92 92
160 – 169 2 49 0.92 92
150 – 159 4 47 0.89 89
140 - 149 3 43 0.81 81
130 – 139 7 40 0.75 75
120 – 129 12 33 0.62 62
110 – 119 17 21 0.40 40
100 – 109 1 4 0.08 8
90 – 99 3 3 0.06 6
Total 53
Worksheet: Cumulative Frequency: Cumulative PercentF Cumulative
FrequencyCumulative Proportion
Cumulative Percent
Data Interpretation
• What percent of students whose weight is at or below 109 lb?
• What percent of students whose weight is at or below 159 lb?
• What percent of students whose weight is at or below 199 lb?
Step 8: Percentile Ranks
The percentile rank indicates the percentage of observations with similar and smaller values than certain value in the entire population.
Refer to Step 7c: If my weight is 135 lb, 75% of people weigh equal or less than me. My percentile rank is 75%.
Data Interpretation (Refer to Step 7c)
What is your weight percentile rank? (pick up any weight you like)
Statistical thinking V:
Data can tell where we stand compared with others.
