Analysis of variance Analysis of variance (ANOVA) is a collection of statistical models used to analyze the differences between group means and their associated procedures (such as "variation" among and between groups), developed by R.A. Fisher. In the ANOVA setting, the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether or not the means of several groups are equal, and therefore generalizes the t-test to more than two groups. Doing multiple two-sample t-tests would result in an increased chance of committing a type I error. Motivating example[edit] No fit. Fair fit Very good fit The analysis of variance can be used as an exploratory tool to explain observations. Background and terminology[edit] ANOVA is a particular form of statistical hypothesis testing heavily used in the analysis of experimental data. "Classical ANOVA for balanced data does three things at once: Blocking
NodeMind: Software for planning, visualization and scheme design Six Sigma The common Six Sigma symbol Six Sigma is a set of techniques and tools for process improvement. It was developed by Motorola in 1986.[1][2] Jack Welch made it central to his business strategy at General Electric in 1995.[3] Today, it is used in many industrial sectors.[4] Six Sigma seeks to improve the quality of process outputs by identifying and removing the causes of defects (errors) and minimizing variability in manufacturing and business processes. The term Six Sigma originated from terminology associated with manufacturing, specifically terms associated with statistical modeling of manufacturing processes. Doctrine[edit] Six Sigma doctrine asserts that: Features that set Six Sigma apart from previous quality improvement initiatives include: The term "six sigma" comes from statistics and is used in statistical quality control, which evaluates process capability. "Six Sigma" was registered June 11, 1991 as U.S. Methodologies[edit] DMAIC[edit] The DMAIC project methodology has five phases:
Wikiversity Main Page - FreeMind - free mind mapping software Customer lifetime value In marketing, customer lifetime value (CLV) (or often CLTV), lifetime customer value (LCV), or user lifetime value (LTV) is a prediction of the net profit attributed to the entire future relationship with a customer. The prediction model can have varying levels of sophistication and accuracy, ranging from a crude heuristic to the use of complex predictive analytics techniques. One of the first accounts of the term Customer Lifetime Value is in the 1988 book Database Marketing, which includes detailed worked examples.[2] Purpose[edit] The purpose of the customer lifetime value metric is to assess the financial value of each customer. Customer lifetime value (CLV): The present value of the future cash flows attributed to the customer during his/her entire relationship with the company.[1] Present value is the discounted sum of future cash flows: each future cash flow is multiplied by a carefully selected number less than one, before being added together. Construction[edit] Methodology[edit]
Statistical hypothesis testing A statistical hypothesis test is a method of statistical inference using data from a scientific study. In statistics, a result is called statistically significant if it has been predicted as unlikely to have occurred by chance alone, according to a pre-determined threshold probability, the significance level. The phrase "test of significance" was coined by statistician Ronald Fisher.[1] These tests are used in determining what outcomes of a study would lead to a rejection of the null hypothesis for a pre-specified level of significance; this can help to decide whether results contain enough information to cast doubt on conventional wisdom, given that conventional wisdom has been used to establish the null hypothesis. Variations and sub-classes[edit] Statistical hypothesis testing is a key technique of both Frequentist inference and Bayesian inference although they have notable differences. The testing process[edit] An alternative process is commonly used: Interpretation[edit] Example[edit]
Systems thinking Impression of systems thinking about society[1] A system is composed of interrelated parts or components (structures) that cooperate in processes (behavior). Natural systems include biological entities, ocean currents, the climate, the solar system and ecosystems. Designed systems include airplanes, software systems, technologies and machines of all kinds, government agencies and business systems. Systems Thinking has at least some roots in the General System Theory that was advanced by Ludwig von Bertalanffy in the 1940s and furthered by Ross Ashby in the 1950s. Systems thinking has been applied to problem solving, by viewing "problems" as parts of an overall system, rather than reacting to specific parts, outcomes or events and potentially contributing to further development of unintended consequences. Systems science thinking attempts to illustrate how small catalytic events that are separated by distance and time can be the cause of significant changes in complex systems.
P-value In statistical significance testing, the p-value is the probability of obtaining a test statistic result at least as extreme as the one that was actually observed, assuming that the null hypothesis is true.[1][2] A researcher will often "reject the null hypothesis" when the p-value turns out to be less than a certain significance level, often 0.05 or 0.01. Such a result indicates that the observed result would be highly unlikely under the null hypothesis. Many common statistical tests, such as chi-squared tests or Student's t-test, produce test statistics which can be interpreted using p-values. In a statistical test, the p-value is the probability of getting the same value for a model built around two hypotheses, one is the "neutral" (or "null") hypothesis, the other is the hypothesis under testing. An informal interpretation with a significance level of about 10%: Definition[edit] Example of a p-value computation. is the observed data and , then (right tail event) or and interval. .
Agile software development Agile software development is a set of principles for software development in which requirements and solutions evolve through collaboration between self-organizing,[1] cross-functional teams. It promotes adaptive planning, evolutionary development, early delivery, and continuous improvement, and it encourages rapid and flexible response to change.[2] Agile itself has never defined any specific methods to achieve this, but many have grown up as a result and have been recognized as being 'Agile'. The Manifesto for Agile Software Development,[3] also known as the Agile Manifesto, was first proclaimed in 2001, after "agile methodology" was originally introduced in the late 1980s and early 1990s. The manifesto came out of the DSDM Consortium in 1994, although its roots go back to the mid 1980s at DuPont and texts by James Martin[4] and James Kerr et al.[5] History[edit] Incremental software development methods trace back to 1957.[6] In 1974, E. The Agile Manifesto[edit] Agile principles[edit]
Dunning–Kruger effect Cognitive bias about one's own skill The Dunning–Kruger effect is a cognitive bias in which people with limited competence in a particular domain overestimate their abilities. It was first described by Justin Kruger and David Dunning in 1999. Numerous similar studies have been done. There is disagreement about the causes of the Dunning–Kruger effect. There is also disagreement about where the effect applies and about how strong it is, as well as about its practical consequences. The Dunning–Kruger effect is defined as the tendency of people with low ability in a specific area to give overly positive assessments of this ability. Not knowing the scope of your own ignorance is part of the human condition. David Dunning Some researchers include a metacognitive component in their definition. Among laypeople, the Dunning–Kruger effect is often misunderstood as the claim that people with low intelligence are more confident in their knowledge and skills than people with high intelligence. [edit]
Strategic management Strategic management involves the formulation and implementation of the major goals and initiatives taken by a company's top management on behalf of owners, based on consideration of resources and an assessment of the internal and external environments in which the organization competes.[1] Strategic management provides overall direction to the enterprise and involves specifying the organization's objectives, developing policies and plans designed to achieve these objectives, and then allocating resources to implement the plans. Academics and practicing managers have developed numerous models and frameworks to assist in strategic decision making in the context of complex environments and competitive dynamics. Strategic management is not static in nature; the models often include a feedback loop to monitor execution and inform the next round of planning.[2][3][4] Corporate strategy involves answering a key question from a portfolio perspective: "What business should we be in?"