# W20_EDN_Pareto Chart

1.       Problem Definition

My colleague and I examine why under-served communities do not have a routine habit to save money. Just like I said in blog posted June 9, 2012, we have conducted a phone survey to understand what problems under-served communities are having.

Problem Statement: What are the key problems?

2.       The Feasible Alternatives

Based on the problem, I identify two possible results:

1. The result shows the people do not have any problems with saving habit.
2. The result shows the people faced a problem when it relates with saving habit

3.       Tools and Technique

To answer the problem, there are several steps to solve the problem using Pareto Chart:

• Collect sample data;
• Based on data collection, determine problem category and frequency;
• Draw the Pareto Chart from the frequency table; and
• Interpret the Pareto Chart.

4.     Selection of the Acceptable Criteria.

Table 1 shows the problem category and frequency table which we collected from phone survey respondents.

Table 1: Problem Category and Frequency Table

We decided the problem categories on the horizontal line (x axis) and the frequencies on vertical line (y axis). Then, we draw the Pareto Chart.

Figure 1: The Pareto Chart

Figure 1 shows the frequency of there is no reason to save money is the highest (35) among others. The tallest bar indicates the biggest contributors to the overall problem. The phone survey shows that under-served communities are having a problem with motivation or goals to save money for future needs.

5.       Post-Evaluation of the Result

The under-served communities are the most vulnerable groups in our societies. Based on the calculation above, the result shows the under-served communities think that there is no reason to save money. The policy makers on banking sectors should evaluate is the current campaign to save money sufficient enough for under-served communities and think the effective campaign to raise awareness the importance to save money for future needs.

There is a clear distinction between the Histogram on blog posted June 9, 2012 with the Pareto Chart. The Histogram only displays the frequency distribution, whereas the Pareto Chart analyzes the frequency of problems.

6.       References

Brassard, M. & Ritter, D. (2010). The Memory Jogger 2: Tools for Continuous Improvement and Effective Planning, pp. 122-135.

# W19_EDN_Histogram

1.       Problem Definition

My colleague and I are planning to implement phone survey for data collection for our study’s sample. Prior the real survey, we have to test the phone questionnaire whether the survey can be finished in 20 minutes. The questionnaire consists of 30 questions, so we picked random respondents to examine how long it takes to finish the survey.

Problem Statement: does the duration of the survey within the expected duration?

2.       The Feasible Alternatives

Based on the problem, I identify three possible results:

1. The result shows the actual duration of the survey is the same with the expected duration.
2. The result shows the actual duration is less than expected duration.
3. The result shows the actual duration is more than the expected duration.

3.       Tools and Technique

To answer the problem, several steps to solve the problem:

1. Collect sample data;
2. Determine number of class and frequency;
3. Draw the histogram from the frequency table; and
4. Interpret the histogram.

4.     Selection of the Acceptable Criteria.

Table 1 shows the frequency table which we collected from respondents.

Table 1: Frequency Table

Then, we decided the number of classes that contribute to our analysis, next construct the frequency table.

Table 2: Number of Classes and Frequency

Then, we draw the histogram.

Figure 1: The Histogram

Figure 1 shows the frequency within 10-15 minutes is the highest. We are confidence the survey’s respondent can finish the interview less than 20 minutes with current questionnaire.

5.       Post-Evaluation of the Result

Based on the calculation above, the result shows the chance of interview can be finished in 10-15 minutes is higher. We have to scrutinize the current questionnaire and make sure it collects the information we needed for the report.

6.       References

Brassard, M. & Ritter, D. (2010). The Memory Jogger 2: Tools for Continuous Improvement and Effective Planning, pp. 91-100.

Henning, J. 2009. Do phone surveys have a future? Retrieved from: http://www.research-live.com/features/do-phone-surveys-have-a-future?/4000692.article

Vicente, P., Reis, E., & Santos, M. Using Mobile Phones for Survey Research: A Comparative Analysis between Data Collected via Mobile Phones and Fixed Phones. Retrieved from: http://homepages.wmich.edu/~wmartz/assets/mobile-phones-survey-research.pdf

# W18_EDN_Scatter Diagram

1.       Problem Definition

Every time we posted a blog, Dr. Paul will review and grade the blog. I would like to study the possible relationship between actual hours for preparing blog posting and the blog posting’s rating.

Problem Statement: is there any relationship between the actual hours to preparing blog posting and blog posting ratings?

2.       The Feasible Alternatives

Based on the problem, I identify that there are three feasible alternatives:

1. The result shows there is no relationship between the actual hours for preparing blog posting and the blog posting’s rating.
2. The result shows there is strong/positive relationship between the actual hours for preparing blog posting and the blog posting’s rating.
3. The result shows there is weak/negative relationship between the actual hours for preparing blog posting and the blog posting’s rating.

3.       Tools and Technique

To answer the problem, several steps to solve the problem:

1. Collect sample data from weekly report: ACWP;
2. Determine dependent and independent variables;
3. Run regression analysis; and
4. Interpret the data.

4.     Selection of the Acceptable Criteria.

Table 1 below shows the ACWP of preparing blog postings and rating the blog in each week.

Table 1: ACWP and Rating of Blog Posting

The independent variable or x is ACWP.

The dependent variable or y is Rating.

Then, run the regression analysis. Based on the regression result show in table 2, the formula to captures the relationship between ACWP and Rating is Y= 4.388 – 0.004. The result shows negative correlation (-0.004) between the ACWP and Rating.

Table 2: The Coefficients

Table 3: The Correlation Coefficients

Table 3 shows the correlation coefficient (0.011) indicates that a weak relationship between the relationship between ACWP and Rating and the result in figure 1 emphasizes the weak relationship between the relationship between ACWP and Rating.

Figure 1: Scatter Diagram

The weak relationship might happened due I was starting to work together with classmates to produce blog postings on W10 and W12 and use the same raw materials with different tools and techniques on W11 and W13 and delayed in producing the blog posting.

5.       Post-Evaluation of the Result

Based on the calculation above, the result shows weak negative relationship between ACWP and Rating. To catch up the delay blog posting, I should catch up to write at least three blogs this weekend.

6.       References

Brassard, M. & Ritter, D. (2010). The Memory Jogger 2: Tools for Continuous Improvement and Effective Planning, pp. 53-70.

Scatter Diagram. http://web2.concordia.ca/Quality/tools/25scatter.pdf

# W17_EDN_Run Chart

1.       Problem Definition

Since I am behind schedule on blog posting, I would like to study the trends or patterns of actual hours for preparing blog posting and also would like to explore whether the actual hours is different prior and after the CCC/E paper submission to AACE.

Problem Statement: Comparing the trends or patterns of preparing blog posting prior and after the paper submission using Run Chart.

2.       The Feasible Alternatives

Based on the problem, I identify that there are two feasible alternatives:

1. The result shows the performance decreased prior the paper submission and increased after the paper submission.
2. The result shows the performance remains the same prior and after the paper submission.

3.       Tools and Technique

To answer the problem, several steps to solve the problem:

1. Collect sample data from weekly report: ACWP;
2. Create x and y axis, plot the data and calculate mean of the sample;
3. Interpret the data and compare the result prior and after the paper submission.

4.     Selection of the Acceptable Criteria.

Table 1 below shows the ACWP of preparing blog postings.

Table 1: ACWP of Blog Posting

Then, calculate the mean.

Table 2: Statistical Value

Next step, create x and y axis, plot the data and calculate mean of the sample.

Figure 1: Run Chart

Then, using trend-analysis I compare the performance prior and after the paper submission.

Figure 2: The Performance Prior and After the Paper Submission

Figure 2 shows that the trends or patterns to prepare blog posting prior and after the paper submission are decreasing. The decreased trends or patterns to prepare blog posting prior and after the paper submission might happened due the learning curve is decreasing and starting to work together with classmates to produce blog postings on W10 and W12 and use the same raw materials with different tools and techniques on W11 and W13.

5.       Post-Evaluation of the Result

Based on the calculation above, the result shows the trends or patterns to prepare blog posting prior and after the paper submission are decreasing. To catch up the delay blog posting, I should catch up to write at least three blogs this weekend. When compared with blog posted on May 26, 2012, the Run Chart is only showing the trends and patterns and provide general picture of a process, meanwhile the Control Chart provide more specific information such as UCL and LCL.

6.       References

Basic Tools for Process Improvement: Module 9 Run Chart. http://www.au.af.mil/au/awc/awcgate/navy/bpi_manual/mod9-runchart.pdf

Brassard, M. & Ritter, D. (2010). The Memory Jogger 2: Tools for Continuous Improvement and Effective Planning, pp. 53-70.

Daum. S. 2009. The Difference between Run Charts and Control Charts. http://blog.pqsystems.com/2009/12/03/what-is-the-difference-between-a-run-chart-and-a-control-chart/

# W16_EDN_Control Chart

1.       Problem Definition

This week is 18th week; 5 more weeks to finish all promised deliverables. Since I am behind schedule on blog posting, I would like to evaluate and accelerate the performance of blog posting.

Problem Statement:  I would like to monitor, control, and improve the blog posting performance by studying the variations of actual hours to produce blog posting using control chart.

2.       The Feasible Alternatives

Based on the problem, I identify that there are two feasible alternatives:

1. The result shows there is indication that the process performance is out of control.
2. The result shows there is no indication that the process performance is out of control.

3.       Tools and Technique

To answer the problem, several steps to solve the problem:

1. Collect sample data from weekly report: ACWP
2. Calculate the appropriate statistics
3. Calculate the control limits
4. Construct the control chart
5. Interpreting the control chart to find out if the process is out of control due to external and special causes.
6. Removing any data points which fall outside the upper/ lower control limits and special causes.
7. Redraw the control chart after the removal of outliers
8. Comparing the result prior and after the removal.

4.     Selection of the Acceptable Criteria.

Table 1 below shows the ACWP of preparing blog postings.

Table 1: ACWP of Blog Posting

Then, calculate the appropriate statistics and control limits

Table 2: Statistical Value

Next step, construct the control chart, and interpreting the control chart to find out if the process is out of control due to external and special causes.

Figure 1: Control Chart

Figure 1 shows that there is no indication that the process performance is out of control. Since the process is in statistical control, I did not continue with step 6, 7 and 8 which are remove any data points which fall outside the upper/ lower control limits and special causes, redraw the control chart after the removal of outliers and compare the result prior and after removal of outliers.

Based on trend-line’s result in black line, the actual time to produce blog posting is decreasing. It proves that working with classmates to produce blog posting and using the same raw materials in different tools and techniques will help to decrease the actual time to produce blog posting.

5.       Post-Evaluation of the Result

Based on the calculation above, the result shows there is no indication that the process of preparing blog posting is out of control. To catch up the delay blog posting, I should work smart and allocate at least 8.25 hours to produce 3 blogs. If not, I will be in a danger zone where I cannot recover.

6.       References