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Bài giảng Quản trị Dự Án Project management lecture slide for MBA
... leader Program Objectives Understand the fundamentals of project management Identify the essential qualities of a project leader / member Conceptualize and develop project objectives with desired ... Responsibility v.s your Key Stakeholders’ Position Developing Stakeholder Strategy ▪ State your objectives of the project ▪ Develop a map of key stakeholder groups ▪ Determine your power status of ... 27 10 11 12 13 16 17 18 Derive marketing plan TM Derive budget for marketing plan TM Obtain management approval TM Source welcome gifts AK Design promotion materials HL Design spending program...
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Vocabulary for MBA
... done deal (the agreement is completed, accepted)." (Verb) To be included in the relationship "You will have to deal with me (You have to include me You will have to get my permission.)." "Deal ... me in (include me)." For more material and information, please visit Tai Lieu Du Hoc at www.tailieuduhoc.org Deng Xiao Ping - (Proper Noun - Name) Former leader of China "Madame Mao had Deng Xiao ... After spending decades trying to shed the Bubba image it contracted in the 1960s when its prime-time lineup included a slew of backcountry characters, CBS has decided to embrace once again its...
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Some studies on a probabilistic framework for finding object-oriented information in unstructured data
... probabilistic framework is described in [14] The system consists of several modules which are divided into two parts: domain-independent and domain-dependent modules • Domain independent modules These ... Krishna-murthy, Sriram Raghavan, Shivakumar Vaithyanathan, and Huaiyu Zhu Avatar information extraction system IEEE Data Eng Bull [23] Sándor Dominich The Modern Algebra of Information Retrieval The Book Published ... Object-level Ranking: Bringing Order to Web Objects International World Wide Web Conference 2007 [29] Zaragoza, H., and Robertson, S The probabilistic relevance model: BM25 and beyond, 2007 42 ...
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FOOD AND BEVERAGE INDUSTRY MARKETING PRACTICES AIMED AT CHILDREN: DEVELOPING STRATEGIES FOR PREVENTING OBESITY AND DIABETES pdf
... family happiness, or allow Mountain Dew or 7-UP to equate sodas with adolescent values of independence and identity Targeted marketing not only links cultural values and norms with brand name ... schools have expanded during the last decade as a result of marketers’ taking advantage of schools’ financial vulnerability due to chronic funding shortages, coupled with their wish to increase sales ... advertising messages, which play into their vulnerabilities, including concerns related to appearance, self-identity, peers, and sexuality Are Low-Income Children and Children of Color at Greater...
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Anti-corruption guidelines (“Toolkit”) for MBA curriculum change 2012 pdf
... Core concepts Core concepts Core concepts Core concepts Core concepts Core concepts Core concepts Core concepts Core concepts Core concepts Core concepts Core concepts Core concepts Core concepts ... Core concepts Core concepts Core concepts Core concepts Core concepts Core concepts Core concepts Core concepts Core concepts Core concepts Core concepts Core concepts Core concepts Core concepts ... Spence, E 2005 Corruption and Anti-corruption An applied philosophical approach Pearson Core concepts Core concepts Core concepts Core concepts Core concepts Core concepts Core concepts Core concepts...
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lecture 8 for student
... absorbance of filtrate N H + Histidine Dye Binding Method Dye Binding Method Absorbance of dye bound by protein = A initial (free) die concentration - A filtrate die concentration Absorbance at ... K Na Tartrate-Na2CO3 solution is added after cooling and incubated at room temperature for 10 CuSO4-K Na Tartrate-NaOH solution is added after cooling and incubated at room temperature for 10min ... reagent is added and then the reaction mixture is mixed and incubated at 50◦C for 10 Absorbance is read at 650 nm A standard curve of BSA is carefully constructed for estimating protein concentration...
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Data Mining: Introduction Lecture Notes for Chapter 1 Introduction to Data Mining ppt
... Mining 26 Regression Predict a value of a given continuous valued variable based on the values of other variables, assuming a linear or nonlinear model of dependency Greatly studied in statistics, ... introduced before • We know which customers decided to buy and which decided otherwise This {buy, don’t buy} decision forms the class attribute • Collect various demographic, lifestyle, and companyinteraction ... – Use some variables to predict unknown or future values of other variables Description Methods – Find human-interpretable patterns that describe the data From [Fayyad, et.al.] Advances in Knowledge...
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Data Mining: Data Lecture Notes for Chapter 2 Introduction to Data Mining potx
... permutation of values If all employee ID numbers were reassigned, would it make any difference? Ordinal An order preserving change of values, i.e., new_value = f(old_value) where f is a monotonic function ... mode, entropy, contingency correlation, χ2 test Ordinal The values of an ordinal attribute provide enough information to order objects () hardness of minerals, {good, better, best}, grades, ... attribute: distinctness & order Interval attribute: distinctness, order & addition Ratio attribute: all properties © Tan,Steinbach, Kumar */ Introduction to Data Mining Attribute Type Description...
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Data Mining: Exploring Data Lecture Notes for Chapter 3 Introduction to Data Mining potx
... Introduction to Data Mining Frequency and Mode The frequency of an attribute value is the percentage of time the value occurs in the data set – For example, given the attribute ‘gender’ and a representative ... Introduction to Data Mining Measures of Spread: Range and Variance Range is the difference between the max and The variance or standard deviation is the most common measure of the spread of a set ... pth percentile is x a value xp x such that p% of the observed of values of x are less than xp p For instance, the 50th percentile is the value x50% such that 50% of all values of x are less than...
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Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining pptx
... Computing Gini Index Use Binary Decisions based on one value Several Choices for the splitting value – Number of possible splitting values = Number of distinct values Each splitting value has a count ... set is used to determine the accuracy of the model Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it ... reach a node t General Procedure: – If Dt contains records that belong the same class yt, then t is a leaf node labeled as yt – If Dt is an empty set, then t is a leaf node labeled by the default...
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Data Mining Classification: Alternative Techniques - Lecture Notes for Chapter 5 Introduction to Data Mining pdf
... Rule Evaluation Metrics: – Accuracy nc = n nc + = – Laplace n +k nc + kp = n +k – M-estimate © Tan,Steinbach, Kumar Introduction to Data Mining n : Number of instances covered by rule nc : Number ... positive class – Negative class will be default class For multi-class problem – Order the classes according to increasing class prevalence (fraction of instances that belong to a particular class) ... a rule is added to the rule set, compute the new description length • stop adding new rules when the new description length is d bits longer than the smallest description length obtained so far...
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Data Mining Association Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 6 Introduction to Data Mining pdf
... B C D E AB AC AD AE BC BD BE CD CE DE ABC ABD ABE ACD ACE ADE BCD BCE BDE CDE ABCD ABCE ABDE ACDE ABCDE © Tan,Steinbach, Kumar Introduction to Data Mining BCDE Given d items, there are 2d possible ... B AD Closed but not maximal null ACE BE ADE BCD E 24 CD BCE Closed and maximal 34 CE BDE 45 DE CDE ABCD ABCE ABDE ACDE BCDE # Closed = # Maximal = ABCDE © Tan,Steinbach, Kumar Introduction to ... {B,C,D,E} B C D E AB AC AD AE BC BD BE CD CE DE ABC ABD ABE ACD ACE ADE BCD BCE BDE CDE Possible Extension: E(ABC) = {D,E} ABCD ABCE ABDE ACDE BCDE ABCDE © Tan,Steinbach, Kumar Introduction to Data...
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Data Mining Association Rules: Advanced Concepts and Algorithms Lecture Notes for Chapter 7 Introduction to Data Mining docx
... completeness measure to determine how much information is lost C: frequent itemsets obtained by considering all ranges of attribute values P: frequent itemsets obtained by considering all ranges over ... the discretized intervals affect support & confidence {Refund = No, (Income = $51,250)} → {Cheat = No} {Refund = No, (60K ≤ Income ≤ 80K)} → {Cheat = No} {Refund = No, (0K ≤ Income ≤ 1B)} → {Cheat ... sequence, |s|, is given by the number of elements of the sequence © Tan,Steinbach, Kumar Introduction to Data Mining A k-sequence is a sequence that contains k events 28 Examples of Sequence Web...
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Data Mining Cluster Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining pot
... objectives – A variation of the global objective function approach is to fit the data to a parameterized model • Parameters for the model are determined from the data • Mixture models assume that ... Mining 13 Types of Clusters: Density-Based Density-based – A cluster is a dense region of points, which is separated by low-density regions, from other regions of high density – Used when the clusters ... represent a particular concept Overlapping Circles © Tan,Steinbach, Kumar Introduction to Data Mining 15 Types of Clusters: Objective Function Clusters Defined by an Objective Function – Finds clusters...
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Data Mining Cluster Analysis: Advanced Concepts and Algorithms Lecture Notes for Chapter 9 Introduction to Data Mining pot
... Clusters are defined as densely populated regions of the space • Clusters have arbitrary shapes, orientation, and non-uniform sizes • Difference in densities across clusters and variation in density ... Mining 32 SNN Density a) All Points c) Medium SNN Density © Tan,Steinbach, Kumar b) High SNN Density d) Low SNN Density Introduction to Data Mining 33 SNN Clustering Can Handle Differing Densities ... uses the proximity graph – Start with the proximity matrix – Consider each point as a node in a graph – Each edge between two nodes has a weight which is the proximity between the two points –...
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LECTURE NOTES-For Environmental Health Science Students: Air Pollution ppt
... Contain no water vapor and dust • Amount of ozone vary depending on location and season of the year Ozone concentration are lowest above the equator, increasing towards the poles, they also increased ... atmospheric condition in which certain substances are present in such concentrations that they can produce undesirable effects on man and his environment These substances include gases (SOx, NOx, CO, HCs, ... use by health care workers, students or faculty All copies must retain all author credits and copyright notices included in the original document Under no circumstances is it permissible to sell...
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Responsibility of Applicants for Promoting Objectivity in Research for which Public Health Service Funding is Sought and Responsible Prospective Contractors pot
... to include the information required under the 1995 regulations plus the value of the financial interest or a statement that a value cannot be readily determined, the nature of the FCOI, a description ... Component We have included this definition in the final rule for the same reasons we have included the ‘‘disclosure of SFIs’’ definition discussed above Financial interest We proposed this definition ... whether this definition includes unfunded collaborators, we note that the definition refers to the function of the individual on the PHS-funded project; i.e., his/her responsibility for the design,...
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Praise for Learning Objective-C 2.0 potx
... compiler about any variables that you are going to use by declaring them A variable declaration has the form: variabletype name; C allows multiple variables in a single declaration: variabletype name1, ... A variable declaration causes the compiler to reserve storage (memory) for that variable The value of a variable is the contents of its memory location The next chapter describes variable declarations ... that depends on the structure’s binary layout typedef The typedef declaration provides a means for creating aliases for variable types: typedef float Temperature; Temperature can now be used to declare...
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Manual for MBA Internship Programme doc
... should include the scheme of report such as: Chapter-1 includes background, purpose, scope, methodology, and scheme of report Chapter-2 includes review of literature; Chapter-3 includes analysis ... analysis, financial analysis, marketing analysis etc Improve report writing skills, Help develop concrete plans, including long and short-term action plans, along with financial details 1.2 Selection ... Review of the various departments of the overall organization in terms of their functions and responsibilities, etc ii) Structure and functions of the Accounts/Finance Department a) Describe the...
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Essential Texts for MBA Students 2012 pptx
... ANOVA (GLM 1) \ Analysis of Covariance, ANCOVA (GLM 2) \ Factorial ANOVA (GLM 3) \ Repeated-Measures Designs (GLM 4) \ Mixed Designs (GLM 5) \ Non-Parametric Tests \ Multivariate Analysis of Variance ... that encourages students to think in fresh and innovative ways This book will not only make you a better student of leadership, but a better leader too Readership Researchers, students and leaders ... \ Leadership and Values \ Leadership and Strategy \ Leadership and Empowerment \ Leadership and Engagement \ Multiple Intelligences of Leadership \ The Assessment and Development of Leadership...
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