Statistical Thinking<\/a><\/h2>\n\n\n\nImpression\uff1a\u7edf\u8ba1\u5b66\u7684\u6982\u5ff5<\/p>\n\n\n\n
Statistics is not only about mathematics<\/strong>. Instead, statistics is about a philosophy<\/strong>, a perspective<\/strong>, and a set of tools<\/strong> that help us understand the world. <\/p>\n\n\n\nuncertainty + variability –> outcomes –> observations –> data<\/strong><\/p>\n\n\n\nStatistics is the science of collecting<\/strong>, displaying, analyzing<\/strong>, and interpreting<\/strong> data.<\/p>\n\n\n\ndata –> information –> knowledge –> insight –> action<\/strong>
Do decision-making based on the results<\/p>\n\n\n\nTypical Tasks in Statistics<\/strong><\/p>\n\n\n\n\n- 1. Collecting data\n
\n- Observations, surveys, experiments, sampling <\/li>\n\n\n\n
- Probability, probability distribution, random variables<\/li>\n\n\n\n
- Sampling distributions<\/li>\n<\/ul>\n<\/li>\n\n\n\n
- 2. Displaying data\n
\n- Descriptive statistics: graphical, numerical<\/li>\n\n\n\n
- Exploratory Data Analysis (EDA), visualization<\/li>\n<\/ul>\n<\/li>\n\n\n\n
- 3. Analyzing data\n
\n- Inferentialstatistics: estimation, hypothesis testing<\/li>\n\n\n\n
- Regression-type models<\/li>\n\n\n\n
- Multivariate analysis<\/li>\n\n\n\n
- Advanced modelling: time series, classification, survival analysis \u2026<\/li>\n<\/ul>\n<\/li>\n\n\n\n
- 4. Interpreting data and decision making<\/li>\n<\/ul>\n\n\n\n
Process View<\/strong>
Everything that is of our interest about uncertainty and variability must be observed and collected in some processes.<\/p>\n\n\n\n![\"\"](\"https:\/\/post.kongyou.xyz\/wp-content\/uploads\/2026\/03\/image-1024x468.png\")
<\/figure>\n\n\n\nMerely collecting huge amount of data is useless!
Your data must be studied structurally to facilitate decision making and action taking
Thus,statistical modelling is essential!<\/p>\n\n\n\n
\n- Data is man-made, with purpose.<\/li>\n\n\n\n
- Data is obtained from processes, with context.<\/li>\n\n\n\n
- Data is used to gain insight, support decision<\/li>\n\n\n\n
- making, and solve problems.<\/li>\n<\/ul>\n\n\n\n
\n- Thus, data must be designed.<\/li>\n\n\n\n
- Why<\/li>\n\n\n\n
- What<\/li>\n\n\n\n
- How<\/li>\n\n\n\n
- When<\/li>\n\n\n\n
- Where<\/li>\n\n\n\n
- Who: Processors \/ Suppliers \/ Customers<\/li>\n<\/ul>\n\n\n\n
![\"\"](\"https:\/\/post.kongyou.xyz\/wp-content\/uploads\/2026\/03\/image-1.png\")
StatisticalThinking: Improvement Model<\/figcaption><\/figure>\n<\/div>\n\n\nProblem Solving<\/strong><\/p>\n\n\n\n\n- Problem Solving addresses specific problems that are not part of the normal behavior of the process.<\/li>\n\n\n\n
- These issues are often discovered in the process improvement analysis and can be resolved without fundamentally changing the process.\n
\n- 1. Document the scope of the problem.<\/li>\n\n\n\n
- 2. ldentify the root causes.<\/li>\n\n\n\n
- 3. Select,implement, and standardize corrections<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n
Process Improvement<\/strong><\/p>\n\n\n\n\n- 1. Flowcharting the process to understand it better<\/li>\n\n\n\n
- 2. Collecting data to assess the current performance<\/li>\n\n\n\n
- 3. ldentifying areas where the process could be fundamentally improved<\/li>\n\n\n\n
- 4. Changing the process to implement improvement ideas<\/li>\n\n\n\n
- 5. Checking the impact of improvement efforts<\/li>\n\n\n\n
- 6. Making the improvements part of the standard way of doing business<\/li>\n<\/ul>\n\n\n\n
Statistical Engineering<\/strong><\/p>\n\n\n\n\n- Some problems faced in business are large, complex, and unstructured. There may not be a clear understanding of what the problem actually is, or how to approach it.<\/li>\n\n\n\n
- In such cases, we need Statistical Engineering.<\/strong><\/li>\n\n\n\n
- This approach takes longer, because it requires significant effort to define and structure the problem and then engineer a strategy to solve it.<\/li>\n\n\n\n
- A company may go through this cycle several times in the course of improving a process.<\/strong><\/li>\n<\/ul>\n\n\n\n
<\/p>\n\n\n\n
The Role of Data<\/strong><\/p>\n\n\n\n\n- Data are the connectors or links between the doing and improving activities.<\/li>\n\n\n\n
- Data fuel all three of the improvement activities and increase their effectiveness.<\/li>\n\n\n\n
- Data help us document process performance, identify problems, and evaluate the impact of proposed solutions.<\/li>\n\n\n\n
- Note that data is not synonymous with information.<\/li>\n<\/ul>\n\n\n\n
Statistical Thinking<\/strong><\/p>\n\n\n\nStatistical Thinking is a philosophy of learning and action based on these fundamental principles:<\/p>\n\n\n\n
\n- 1. All work occurs in a system of interconnected processes.<\/li>\n\n\n\n
- 2. Variation exists in all processes.<\/li>\n\n\n\n
- 3. Understanding and reducing variation are keys to success.<\/li>\n<\/ul>\n\n\n\n
Statistical Point of View<\/strong>
From a statistical point of view improvement activity can be viewed as working on either of
two process characteristics:<\/p>\n\n\n\n\n- reducing variation through tighter control of the process<\/li>\n\n\n\n
- improving the overall level (average value) by changing the process target, which may also result in reduced variation.<\/li>\n<\/ul>\n\n\n\n
Principle of Statistical Thinking<\/strong><\/p>\n\n\n\n\n- The third principle of statistical thinking is that understanding and reducing variation are keys to success.<\/strong>\n
\n- The average performance of any process is a function of various factors involved in the operation of the process.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
- Three types of variations we may need to reduce are systematic variation, outliers, and random or common cause.<\/strong><\/li>\n<\/ul>\n\n\n\n
Analytics and Statistics<\/h2>\n\n\n\n\n- Analytics\n
\n- Analytics is the scientific process of transforming data into insight for making better decisions.\n
\n- Data<\/li>\n\n\n\n
- Transforming data<\/li>\n\n\n\n
- Insight<\/li>\n\n\n\n
- Decisions > Making better decisions<\/li>\n\n\n\n
- Scientific process<\/strong><\/li>\n\n\n\n
- Information-based strategy \/ Data-driven decision-making<\/li>\n\n\n\n
- Make better business decisions with analytics – data collection and analysis<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n\n\n\n
- Analytics and Business\n
\n- To provide management with a better appreciation of the value of data analytics.<\/li>\n\n\n\n
- To communicate concepts of statistics and data mining in plain language.<\/li>\n\n\n\n
- To illustrate how data can add value to the everyday life of business executives.<\/li>\n\n\n\n
- Includes:\n
\n- Statistics<\/li>\n\n\n\n
- Management Science \/Operational Research<\/li>\n\n\n\n
- (MS\/OR)<\/li>\n\n\n\n
- Optimization, Simulation, Decision Analysis \u2026<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n\n\n\n
- Levels of Analytics\n
\n- Descriptive – What is happening?<\/li>\n\n\n\n
- Diagnostic Analytics – Why did it happen?<\/li>\n\n\n\n
- Predictive Analytics – What will happen?<\/li>\n\n\n\n
- Prescriptive Analytics – Whta is the best straegy?<\/li>\n<\/ul>\n<\/li>\n\n\n\n
- <\/li>\n<\/ul>\n\n\n\n
<\/p>\n
Views: 0<\/p>","protected":false},"excerpt":{"rendered":"
Outlines Statistical Thinking Impression\uff1a\u7edf\u8ba1\u5b66\u7684\u6982\u5ff5 Statist […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[45,2],"tags":[],"class_list":["post-433","post","type-post","status-publish","format-standard","hentry","category-45","category-2"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/post.kongyou.xyz\/index.php?rest_route=\/wp\/v2\/posts\/433","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/post.kongyou.xyz\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/post.kongyou.xyz\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/post.kongyou.xyz\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/post.kongyou.xyz\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=433"}],"version-history":[{"count":3,"href":"https:\/\/post.kongyou.xyz\/index.php?rest_route=\/wp\/v2\/posts\/433\/revisions"}],"predecessor-version":[{"id":438,"href":"https:\/\/post.kongyou.xyz\/index.php?rest_route=\/wp\/v2\/posts\/433\/revisions\/438"}],"wp:attachment":[{"href":"https:\/\/post.kongyou.xyz\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=433"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/post.kongyou.xyz\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=433"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/post.kongyou.xyz\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=433"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
uncertainty + variability –> outcomes –> observations –> data<\/strong><\/p>\n\n\n\n Statistics is the science of collecting<\/strong>, displaying, analyzing<\/strong>, and interpreting<\/strong> data.<\/p>\n\n\n\n data –> information –> knowledge –> insight –> action<\/strong> Typical Tasks in Statistics<\/strong><\/p>\n\n\n\n Process View<\/strong> Merely collecting huge amount of data is useless! Problem Solving<\/strong><\/p>\n\n\n\n Process Improvement<\/strong><\/p>\n\n\n\n Statistical Engineering<\/strong><\/p>\n\n\n\n <\/p>\n\n\n\n The Role of Data<\/strong><\/p>\n\n\n\n Statistical Thinking<\/strong><\/p>\n\n\n\n Statistical Thinking is a philosophy of learning and action based on these fundamental principles:<\/p>\n\n\n\n Statistical Point of View<\/strong> Principle of Statistical Thinking<\/strong><\/p>\n\n\n\n <\/p>\n Views: 0<\/p>","protected":false},"excerpt":{"rendered":" Outlines Statistical Thinking Impression\uff1a\u7edf\u8ba1\u5b66\u7684\u6982\u5ff5 Statist […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[45,2],"tags":[],"class_list":["post-433","post","type-post","status-publish","format-standard","hentry","category-45","category-2"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/post.kongyou.xyz\/index.php?rest_route=\/wp\/v2\/posts\/433","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/post.kongyou.xyz\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/post.kongyou.xyz\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/post.kongyou.xyz\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/post.kongyou.xyz\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=433"}],"version-history":[{"count":3,"href":"https:\/\/post.kongyou.xyz\/index.php?rest_route=\/wp\/v2\/posts\/433\/revisions"}],"predecessor-version":[{"id":438,"href":"https:\/\/post.kongyou.xyz\/index.php?rest_route=\/wp\/v2\/posts\/433\/revisions\/438"}],"wp:attachment":[{"href":"https:\/\/post.kongyou.xyz\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=433"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/post.kongyou.xyz\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=433"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/post.kongyou.xyz\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=433"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Do decision-making based on the results<\/p>\n\n\n\n\n
\n
\n
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Everything that is of our interest about uncertainty and variability must be observed and collected in some processes.<\/p>\n\n\n\n<\/figure>\n\n\n\n
Your data must be studied structurally to facilitate decision making and action taking
Thus,statistical modelling is essential!<\/p>\n\n\n\n\n
\n
\n
\n
\n
\n
\n
\n
From a statistical point of view improvement activity can be viewed as working on either of
two process characteristics:<\/p>\n\n\n\n\n
\n
\n
Analytics and Statistics<\/h2>\n\n\n\n
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