Data Stream Classification Of Red And White Wines Marketing Essay

Data Stream Classification Of Red And White Wines Marketing Essay Introduction Well we got 2 data sets to analysis using SPSS PASW 1) Wine Quality Data Set and 2) The Poker Hand Data Set. We can do this using CRISP methodology. Let us look what is CRISP by wikipedia CRISP-DM stands for Cross Industry Standard Process for Data Mining It is a data mining process model that describes commonly used approaches that expert data miners use to tackle problems. PASW Modeler is a data mining workbench that enables you to quickly develop predictive models using business expertise and deploy them into business operations to improve decision making. Designed around the industry-standard CRISP-DM model, IBM SPSS PASW Modeler supports the entire data mining process, from data to better business results. CRISP DM, Clementines own lightweight methodology of 5 stages Business Understanding, Data Understanding, Data Preparation Modelling, Evaluation and Deployment. CRISP Methodology Business Understanding: Understanding the project requirements objectives from a business perspective, and then converting this knowledge into a data mining problem definition Data understanding In this step following activities are going on, Data understanding, Collecting Initial Data then describing Data, Exploring Data and lastly verifying Data Quality The data preparation phase Tasks include table, record, and attribute selection as well as transformation and cleaning of data for modeling tools.Cleaning Data using appropriate cleaning and cleansing strategies then Integrating Data into a single point. Modeling: Selection and application of various modeling techniques done in this phase, and their parameters are adjusted to optimal values. Basically, there are more than one technique for the same data mining problem type. Some techniques have specific requirements on the form of data. Therefore, stepping back to the data preparation phase is often needed. Steps consist of Generating a Test Design, Building the Models assessing the Model Evaluation Building of model (or models) takes place in this phase. Before proceeding to final deployment of the model, it is important to more thoroughly evaluate the model, and review the steps executed to construct the model. Deployment In the final stage Knowledge gained is organized presented so that an end user can easily use it. As per the requirements this can be a report or a complex data mining process. Normally Customers carry out the deployment step Wine quality data set Wine quality is modeled under classification and regression approaches, which preserves the order of the grades. Explanatory knowledge is given in terms of a sensitivity analysis, which measures the response changes when a given input variable is varied through its domain The red wine data set contains 1600 samples out of which I have selected 200 random samples and doing the analysis(Data mining cannot discover patterns that may be present in the larger body of data if those patterns are not present in the sample being mined ) .So I selected the data set bearing in mind. The data set I have selected has high confidence. With measurements of 13 chemical constituents (e.g. alcohol, Mg) and the goal is to find the quality of red and white wine. Input variables 1 fixed acidity 2 volatile acidity 3 citric acid 4 residual sugar 5 chlorides 6 free sulphur dioxide 7 total sulfur dioxide 8 density 9 pH 10 sulphates 11 alcohol Output variable is quality (score between 0 and 10) CRISP methodology has been followed through out the phase .By checking the web site and resources learned about the wine domain .the next step was to check whether incorrect, missing or abnormal values in the data set end ensure the data quality. Data quality of the data set is very good. PASW Data stream classification of red and white wines Classification for Red and White wine 2 data sets red wine and white wine have been imported using variable file nodes Use of type node here is to describe the characteristics of data. . The Classification and Regression (CR) Tree node is a tree-based classification and prediction method. Similar to C5.0, this method uses recursive partitioning to split the training records into segments with similar output field values. The CR Tree node starts by examining the input fields to find the best split, measured by the reduction in an impurity index that results from the split. The split defines two subgroups, each of which is subsequently split into two more subgroups, and so on, until one of the stopping criteria is triggered. All splits are binary (only two subgroups) Red Wines variable importance White wine variable importance From variable importance diagram we can say that important attribute to determine Red wine quality is pH. The variable importance is in the order pH, citric acid, chloride as shown in the figure1. But for determining White wines quality the most contributing attribute is chloride and 2nd attribute is Alcohol. Decision Tree Model of white wine (only a portion) Analysis and conclusion The above generated tree consists of nodes and its children. The top node represent the total number of wine samples and how many number belongs to different categories(1 to 9).The first split is on chloride. This implies that most of the wine belongs to chloride level0.041.We see that good quality wine has chloride level It has been found from count Vs Quality graph that how many belongs to good quality categories. Alcoholic concentration of white wine samples is more than that of red wine sample. Good wines normally have high concentration. So we can conclude that White wine samples are good. In the white wine chloride level is normally high that implies it has got good Aroma. Where as in red wine the citric level is between particular levels that shows the red wine is very tasty!! PASW has got a number of 2-D and 3-D charts like bar, pie, histogram, scatter etc for time being I am using linear graph and 3-d scatter graph. You can use any of the graph as per the requirements. Some graphs are easy to interpret .Let us consider a 2-D graph between most contributing variable pH and quality from the graph it is clear that the relation ship between pH and quality is in such a way that if pH is in between 3.23 and 3.27 quality is good. Quality is very low for 3.38 and 3.50.We can plot similar graph between quality and citric acid or towards what ever contributing variable then find out the relation ship between them 2-D graph represent the relation ship between quality and pH of Red wine Let us plot a graph between chloride and Quality for the white wine. In the below figure it shows the quality is very good when chloride level below 0.036.And quality in the range 5 to 6 when chloride level is above .048. Like this if plot a graph between quality and alcohol we will see the quality is too good if alcoholic concentration in between 12.5 and 13(as per the sample I have analyzed) 3D graph which shows the relation ship between alcohol, quality and chloride level of white wine from the 2d analysis it was shown how the quality is being affected by single variable. If the one variable does not tell about how quality being related we can check relation ship between 3 variables using a 3d graph. It is having 3 axes. How Regression is useful In this multiple regression ,Predictors such as (Constant), alcohol, fixed acidity, residual sugar, chlorides, volatile acidity, free sulfur dioxide, sulphates, pH, total sulfur dioxide, citric acid, density determine the value of quality. Below gave a Pasw stream for regression. As per the variable importance graph volatile acidity, total SO2 and alcohol are most important variables in Regression analysis. Model R R Square Adjusted R Square Std. Error of the Estimate 1 .792(a) .626 .474 .542 Each by changing the independent variables value we can get value of dependent variable quality. With the help of a hypothesis we need to understand and build a relation ship among the variables. To predict the mean quality value for a given independent variable (say volatile acidity) we need a line which passes between the mean value of both quality and volatile acidity and which minimize the sum of distance between each of the points and predictive line. This fits into a line. The Poker Hand Data Set Each record is an example of a hand consisting of five playing cards drawn from a standard deck of 52. Each card is described using two attributes (suit and rank), for a total of 10 predictive attributes. There is one Class attribute that describes the Poker Hand. The order of cards is important and there are 480 possible Royal Flush hands. Below discussing about how to determine poker hands using data mining. I am considering classification only. If we consider clustering/Regression it does not make any sense PASW MODEL CLASSIFICATION USING CRT ALGORITHAM We got training and testing data set .First applying a model on training data set. Source file is a Comma separated file (CSV) with 1 million rows. It is difficult to do analyse on this input data set so selected sample data set and doing the analysis. Problem faced The given source data was not in a meaning full format so I have given meaningful attribute name and Values by using Vlookup function in MS excel, now the data has become more meaning full and it looks like below. Data cleansing is very important and comes under data preparation phase of the methodology Accuracy of predictive model The accuracy of predictive model is checked by analysis node. It has been found that accuracy is 90%. Using the Algorithm need to predict any of these: 0: Nothing in hand; 1: One pair;2: Two pairs;3: Three of a kind;4: Straight;5: Flush; 6: Full house;7: Four of a kind;8: Straight flush;9: Royal flush; Pocker hands variable importance diagram Let me say what did I understood from the diagram. Rank2 (rank of card2) is most contributing variable to predict poker hands. It is clear that Rank of 1st, 4th and 2nd cards are more contributing than suit of those cards. The different section of pie chart represents number of cards in a particular poker category. Blue represents No Poker; Red represents ONE PAIR, Green represent Royal flesh How Pasw helps to do classification Pasw has got number tree constructing algorithms(CR, c5.0) to do classification. I considered Classification and Regression (CR) though this is not a time efficient algorithm time complexity is more when compared to c5.0)I selected CR.The data set I have got is simple one and I am not considering the deep analysis all I need to do is to predict poker hands so CR can do it. Below shows the constructed tree using CR (Ashort description of tree already given above) Analysis Data has been classified into Training set and Testing set .Here most of the data set into a training set and small portion of data is used for testing.After a model has been processed by using the Training set, we can test the model by making predictions against the Test set. Since the data in the training set already contains known values for the attribute that you want to predict. Below giving the portion of training set being used. Integrating classification and association rule mining can produce more efficient and accurate classifiers.Here each row is an instance Trial: pair of 5 attributes (SUIT and RANK) + classification class. So this can be used to predict the classification of other unclassified instances. consider the training set given below suit1 rank1 suit2 rank2 suit3 rank3 suit4 rank4 suit5 Rank5 poker Heart ASS heart KING spades 4 Spades 3 heart QUEEN Nothing in hand Diamonds QUEEN diamonds 2 diamonds JACK Clubs 5 spades 5 ONE PAIR Hearts 10 hearts jack hearts king Hearts queen hearts 1 royal flesh Spades Jack spades king spades 10 Spades queen spades 1 royal flesh Diamond queen diamond jack diamond king diamond 10 diamond 1 royal flesh Hearts 5 diamond king spades king spades 7 clubs 5 two pairs Hearts 4 hearts 1 hearts 3 diamond 5 diamond 2 straight Suppose want to predict below hand is what type of Poker hand? suit1 rank1 suit2 rank2 suit3 rank3 suit4 rank4 suit5 rank5 poker Club 10 club jack club A club king club queen From the training set data the testing set is predicted, answer is Royal Flesh Conclusion Two data sets the wine and poker have been analysed using CRISP methodology and using the tool IBM SPSS PASW, used different modelling techniques which suits. Analysed the knowledge elicited by each model DATA MINING KNOWLEDGE DISCOVERY IN MARKETING (PART 2) Abstract Now-a-days Using the high power computing and information technology enables to collect store and process complex Marketing data. Data mining is used to extract knowledge from this marketing data. This report discuss about Data mining process, short discussion about different mining techniques such as classification tree, neural network, Regression and their application in marketing domain. My report Also cover different type of analyzes and tasks being used Introduction From the given topics I have selected the topic Data mining and Knowledge discovery for marketing since my cup of tea is Business and computing. I would always like to do research in Business analytics .Well let us look at what is data mining Data mining is the process of discovery of interesting, meaningful and actionable patterns hidden in large amounts of data . This is one of the tools to transform data into information. It is widely used in almost all fields of science and business profiling practice such as marketing, fraud detection, and scientific discovery. The technique to uncover pattern on data can also apply on sample data .so the sample data should be so the sample should be a good representative of larger data set. data mining can not find out the pattern which may be present in larger body of data and not contains in the small sub set of data. So this is very useful when sufficiently represented data are collected Most well known branches of data mining is knowledge discovery or KDD It derives knowledge from input data .This knowledge which have got from the process will become additional data and can be used for further discovery in related field normally an analyst can analysis and predict it.DM can generate thousands of pattern but all these patterns are not interested and useful. In this I am considering Data mining in a marketing field prospective. The data coming from different sources like transactions, loyalty cards, and discount coupons; customer complaint calls public life style studies using this data we can make Target marketing like to identify appropriate customer segments for new marketing initiatives determine customer purchasing pattern over time associations/co-relations between product sales, predict based on such association I mean cross market analysis what type of customer buys what type of product that is customer profiling Predict likelihood of customer churn and target those likely to leave with retention campaigns Customer requirement analysis like Identify the best products for different groups of customers and Predict what factors will attract new customers Provision of summary information such as Multidimensional summary reports and Statistical summary information (data central tendency and variation) Another question is why can not we go for a traditional data analysis instead of data mining? Answer is the field like marketing has tremendous Amount of data and it has multi dimension and complexity.A Marketing firm would likely to segment their customers into similar groups or clusters in order to better understand consumer behavior and more effectively market their products. In the past for a small business initiatives did not have trouble to understand their customers. They knew what they have to do once a customer approach them .Todays business is more competitive, more customer oriented, more products oriented so it is very difficult to understand the customer behavior, wants, needs the hidden relation ship between the data and preferences. With the help of data mining an analyst can deliver timely, personalized promotional offers. 1 Knowledge Discovery (KDD) Process S2 S1 S3 Data Cleaning Data Integration Databases Data Warehouse Knowledge Task-relevant Data Selection Data Mining Pattern Evaluation Normally in the huge DWH data mining environment data coming from various sources integrated and put it in data warehousing. Various data mining soft wares like teradata intelligent miners are used to mine Tera bytes of data and find market prediction. As I mentioned the DM is a Tools for developing predictive and descriptive models. Some are statistical method such as regression. Other use non statistical method like neural networks, classification trees. Here I considered some important tools then their How Classification trees are being used in marketing data mining Classification tree partition the data to maximize the difference in the dependent variable. it is also called a decision tree. Aim of classification tree is to classify the data into distinct groups or branches that create the strongest separation in the values of the dependent variables.The tree can identify segments. This can be helpful when a company is trying to understand what is driving market behavior. It detects nonlinear relationship. Mailed 10000 2.6% Male 4677 3.2% Female 2.15 2.1 % 1.7%  £30-45 3.6% > £45 4.1% Age>40 4.3% Age 0.7% Box shows resp rate in percentage The tree growth is through series of steps and rules .say for example sales pieces were mailed to 100000 names and yielded a response rate of 2.6%.the first split is on gender. This indicates that greatest difference between responders and non responders is gender. We see that males are much more responsive than females. We would consider males the better target group If we stop after one split. Our goal is to find out group with in both genders that discriminates between responders and non responders. In the next level split male and female groups are considered separately The second level split from the male node is on income, this implies that the income level varies in most between responders and non responders among the males. For female greatest difference is among the age group .It is very easy to identify the group with the highest response rate. Lets say that management decides to mail only to groups where the response rate is more than 3.5%.the offers would be directed to males who makes more than  £30000 a year and female over age 40 Some typical Classification tree Algorithms are 1) C4.5: Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann., 1993. 2) CART: L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth, 1984 Linear regression and its applicability in marketing Knowledge of deviation from normal is very important for a marketer. In the past such deviations were very difficult to detect. Now-a-days data mining tools give great flexibility to detect and classify these changes. It is a statistical technique that quantifies the relationship between dependent variable and the independent variable, these are continuous. Consider the below equation, it shows a relation ship between sales and advertising along the regression equation .Our goal is to predict the sales based on the amount spend on advt. Plot a graph sales vs. advt that would be linear. A key measure of the strength of the relationship is the R-square. It measures the amount of overall variation in data that explained by the model. More than 70% Of the variation in sales can be explained by variation in advertising. Some times the relationship between sales and Advt is non linear (may be curvilinear) .By using the square root of advertising we are able to find better fit for the data. Sales=17.813+.0897*Advertising  £120  £1,503  £160  £1,755  £205  £2,971  £210  £1,682  £225  £3,497  £230  £1,998  £290  £4,598  £315  £2,937  £375  £3,622  £390  £4,402  £440  £3,844  £475  £4,470  £490  £5,492MINIMIZE SQUARED ERROR Advt sales ADVEGRTISIN -Æ’Â   x axis When building targeting models for marketing, risk and CRM, it is common to have much predictive variable. Using multiple predictive or independent continuous variables to predict a single continuous variable is called multiple linear regression .Targeting model created using linear regression is generally very robust. In marketing they can be used alone or in combination with other model. Neural Networks and its applicability in marketing Neural network does not follow any statistical distribution (Neural network is very vast topic a complete discussion is beyond the scope of this report) .it is modeled after the function of the human brain. The process is one of pattern recognition and error minimization. we can say it as nodes that are arranged in layers. The figure tells simple neural network with one hidden layer. Data has been classified into training and testing set (before the process).Then weight or input is assigned to each of the nodes in the first layer. During each iteration ,the input are processed through the system and compared to the actual value .the error is measured and fed back through the system to adjust the weights. The weights get better at predicting the actual results. A error limit is defined and it check with the error limit the process finishes when the minimum error limit reached One specific type of neural network commonly used in marketing uses sigmoidal functions to fit each node. This technique is very powerful in fitting a binary or twoilevel outcome such as response to an offer or a default on a loan Neural network not only pick linear data but also do a good pick up with non linear relation ship in the data. So this allows fitting data which is not possible to fit using regression. One disadvantage we can say that the result of neural net work is some what difficult to interpret A brief description on how Clustering can applicable in data mining Cluster analysis Cluster analysis group respondents with similar behaviors, preferences, or characteristics into segments. By doing so we can understand important similarities and differences between the respondents. Analyst can use this information to develop targeted marketing strategies, or to provide subgroups for analysis. In market survey data, clustering enables market researchers to group respondents who provide similar responses on several questions. In Clustering we use more than one variable that analyzes responses to several questions in order to find similar respondents. Clustering is based on the concept of creating groups based on their proximity to, or distance from, each other. Respondents within a cluster, therefore, are relatively homogenous. Most widely used Algorithms are 1)K-Means: MacQueen, J. B., Some methods for classification and analysis of multivariate observations, in Proc. 5th Berkeley Symp. Mathematical Statistics and Probability, 1967 2) BIRCH: Zhang, T., Ramakrishna, R., and Livny, M. 1996. BIRCH: an efficient data clustering method for very large databases. In SIGMOD 96 Let us look at some more major areas of application of data mining in the marketing like Customer profiling, Deviation analysis and Trend analysis. The pattern which formed after mining the data helps in analytics. Customer profiling This help to predict several marketing decision. A customer profile is a model of customer based on this marketer decides on the right strategies and tactics to meet the needs of that customer .The data mining task used in customer profiling can be dependency analysis, class identification and concept description. Below giving set of transaction that can help marketer to construct useful customer profiles. Frequency of purchases Marketing firm can build targeted promotion offer such as frequent buyer programs by looking how often their customer purchases product from their shop. Rcency of purchases The meaning of term is How long has it been since this customer last placed an order? Suppose a customer frequently visit the shop.It has been found that the specific customer or customer group not visiting the firm over long period of time .Market investigate the reason. By knowing this they can take appropriate offer or action. Size of purchases It tells, on a particular transaction how much he or she spends. This information helps to give resources to those customer groups. Identifying typical customer groups It gives characteristics of each group .For example a profile indicating that the customer has purchased a WINDOWS 7 SOFTWARE CD may hold to the marketer offering a special deal for MICROSOFT OFFICE SOFTWARE CD. Prospecting Customer profiles like buying patterns, give clues to the marketer on prospective customers. Say for example, consider the pattern Purchase of Norton Anti Virus package with one year validity is followed by purchase of Norton Up gradation version /or new version within 11 months about 85% of the time by high income customers discovered by data mining. Analyst who analysis pattern can identify the prospective customers for Upgraded/new version based on first time purchase details and tailor the mail catalog accordingly, thus, increasing the prospect of sales. 2 Deviation analysis Deviation analysis is one of the important analysis for example a higher than normal credit purchase on a credit card can be a fraud anomaly or a genuine purchase by the customer changes.Once a deviation has been discovered as a fraud, the marketer takes appropriate steps to prevent such frauds and initiates corrective action.If the deviation has been discovered as a change, further information collection is necessary. For example, a change can be that a customer got a new job and moved to a new house. In this case, the marketer has to update the knowledge about the customer. 3) Trend analysis Trends are patterns that persist over a period of time. Trends could be short-term trends like the immediate increase and subsequent slow decrease of sales following a sales campaign. Or, trends could be long-term, like the slow flattening of sales of a product over a few years. Data mining tools, such as visualization, help us detect trends, sometimes very subtle and hidden in the database, which would have been missed using traditional analysis tools like scatter plots. In marketing decisions, trends can be used for evaluating marketing programs or to forecast future sales. Data mining task in marketing data mining domain These tasks present in all data mining process we are just looking it into marketing prospective Dependency analysis Data Visualization Class identification Deviation Detection Concept Description Dependency analysis The market basket analysis gives the relationship between different product purchased by a customer .Using this techniques we can develop marketing strategy for promoting product that have dependency relationship in customers mind. Class identification It groups customers into classes which are defined in advance. Mathematical taxonomy and clustering are being used for class identification task. What the first one does is it maximizes the similarity with in classes but minimize similarity between classes. In clustering approach it determine the clustering according to attribute similarity as well as conceptual cohesiveness as defined by domain knowledge (describe above). A company doing business over the net, based on the session log data of internet users, the firm can classify the web users into email only users Surfers or Just for fun Surfer etc Concept description Comparison analysis will be done using statistical techniques. Using this we can compare marketing and customer knowledge. Deviation detection This helps us to determine the anomaly and changes. We can find the anomaly from various statistical techniques. This is already being explained above. Data visualization This kind of softwares allows the market research team or concerned people to view complex 3-D and 2-D patterns. They also provide drill down drill up slice facilities. In the KDD (knowledge discovery from data base) process, data visualization is used in association with other tasks such as dependency analysis, class identification, deviation detection and clustering. IBM SPSS PASW has got good data visualization techniques. Some of them are explained in Part 1 of the report. Conclusion Report discussed about Data mining process,

Apple Case Study

Please divide Apple's history into periods and trace the changes in business models that took place over these periods. Apple history is explained in the case history from 1972 – 2006. Apples history is described below, reflecting on the changes In Business Model (how the company generated revenue). The Beginning years, 1976 – 1 985: Apple was founded In 1976 and they built a computer circuit board named the Apple l. Within span of 4 years they went to PIP with the help of Venture capitalist Markup, Jar. Its original business model was based on selling a computer that could e used straight out of the box with a closed platform.In 1981 IBM introduced a Microsoft's DOS operating system and it's an open system and easily cloned, whilst a lack of compatible software on Macintosh (Mac) made net income fall 17%. Steve Jobs was removed from his operational role by the board. Jobs left Apple to find a new company NeXT. The sculls years, 1985-1993: In 1985 John Sculls was appoin ted as CEO. Sculls was an operations and marketing expert from Pepsi. Scull's strategy was focused on taking Apple into the corporate world, which he did successfully, making Apple a well-known brand name.During the Sculls years Apple was able to monopolize on the â€Å"love affair† for Macs by selling at a premium. In 1990 Sculls changed the business model to sell their computers at lower prices to get more market share, while also delivering new ‘hit products' every 6-12 months. Apple embarked on a Joint venture with MOM to create a new operating model, multimedia applications, etc. Sculls also reduced headcount by 10% and moved much of the manufacturing to contractors. Sculls also made himself Technology officer despite having no skills In this area.He was then relinquished of his duties In 1993. The spindled years, 1993 – 1995: Spindled was an engineer and had successfully headed Apple Europe and changed the business model once more to focus on education and publishing. He killed the plan to put the SO on Intel and instead he would license other companies to produce MAC clones. He focused on international growth I. E china. Spindlier business model also focused on the fact that the new operating model would save the company. Like Sculls, Spindled had to slash costs but Apple still had problems.In first quarter of 1996, apple reported $69 million in losses and more layoffs and Spindled was replaced. Amelia years, 1996 – 19971 ND a high pricing / differentiation strategy and slash payroll. Amelia wanted to turn apple back to its premium price differentiation strategy. He cancelled the next generation Mac SO, which had already cost $mom in R&D. Instead apple would acquire Next along with Steve Jobs. Amelia brought NeXT and brought Jobs as an advisor. Apple still suffered financially and Amelia was forced out. Steve Jobs became the temporary CEO. 997- Steve Jobs era: Steve Jobs re-joined the company in 1996 after Apple acquired NeXT. He made several drastic changes; investment into Apple, a commitment to develop core reduces, ended the Mac licensing program while buying the assets of the leading clone maker, consolidated the product ranges, and launched the Apple website to sell products directly. Jobs' business model turned the company around. He agreed that Microsoft would invest in core products for MAC ii office. Also he rationalized product range from 15 to 3, research projects by 70% and reduced staffing and outsourcing.Comment on what you learn about business model and business model change from this case. When evaluating Apple's business model, it's useful to think in terms of the pre-1996 era and the post-1996 era. Apple has always been and continues to be a manufacturer of computers and electronics with a focus on complete hardware and software integration. Prior to 1996, the company focused almost exclusively on personal computers in the Macintosh line, with the occasional foray into innovative produ cts like the Newton.When Steve Jobs re-joined the company in 1996, that mission evolved beyond personal computing into products like the pod, phone, and pad. Apple is positioned well for the future, and it's not a company that's willing to settle for current success. Unafraid of centralization, the company continues to churn out Phones that make the pod look like a hobby, as well as the pad Mini that unashamedly steals market share from its big brother. Notoriously secret, the company reveals little about the product pipeline, but it's believed that Steve Jobs has left a product roadman for more than a decade.How do other theories of strategy such as capability theory fit with this story? Apple was unable to maintain any strategy over this period since every CEO inconsistently changed the business model and strategy of Apple. Apple's most important resources and capabilities are Steve Jobs, and the integrated system of hardware and software hat the firm has developed and successfull y marketed to derive value. Steve brought Apple back to tremendous success following a decline in relevance and heads the continued creation of billions of dollars of value.While Apple's designers, programmers, and engineers each represent key resources, the ability of the firm to exploit their abilities to create their entire software/hardware ecosystem is the firm's Apple Stores – Retail Locations The introduction of Apple stores has provided the company with an important physical presence to act as both a sales location and an advertisement. Apple tops any retailers in in-store sales, generating $4,032 per retail square foot per year, beating other retailers like Tiffany & Co. At $2,666 and Best Buy at only $930.Relationship with Moms Apple has outsourced all of its manufacturing processes to MEMO partners in China, like Foxing and Hon.. Ha Precision Industry while focusing on design internally. The relationships between Apple and their MEMO partners are very close to prov ide Apple with excellent service and high quality products. Industrial Design Capability Apple's incredible industrial design capability is a function of their innovative design names, led by Jonathan Eve, senior vice president of industrial design, and the firm's parameterization of design and outsourced production.Talented Software Development Teams Apple's software developers are carefully selected and talented programmers. They've produced industry award winning software and the highly regarded iterations of Macintosh SOX operating system. Tailored Hardware/Software Systems One of Apple's most important capabilities is their ability to develop and build highly integrative systems with software designed specifically for the hardware it runs on. Apple Case Study A. The PC industry is much older than the MP3 player industry. As it is an older market, its structure is also more consolidated with only a few builders accounting for the majority of the market. On the other hand, the first MP3 players only surfaced less than ten years ago. While Apple’s Ipod may dominate the market, there are much more MP3 player manufacturers compared to PC manufacturers.Another difference between the two industries is that PCs tend to be commodity products as opposed to MP3 players which are seen as lifestyle or luxury products. This means that PCs for the most part are considered disposable items, especially to bulk buyers like firms or educational institutions. As such, value for money is a premium for PC manufacturers and the trend for the industry is to have better products per new generation at a lower price.Compare this to MP3 players which are for the most part used for personal entertainment. The popularity of the Ipod has cemented the MP3 player as a fashion accessory in addition to a consumer electronics product. Consumers also tend to be more concerned with the form of their MP3 players as opposed to the form of their PCs.Both industries are also experiencing the trend of convergence with PCs having more and more features oriented towards entertainment and MP3 players having more features geared for productivity. Additionally, the cellphone is also encroaching on both products. Smartphones are slowly gaining more PDA and laptop like features. These same devices are also starting to gain more and more entertainment features with newer models coming equipped with the ability to snap pictures and play music.B. Apple products have always been known for its innovation, ease of use and high price. One could say that while PCs in general are a commodity product, Apple computers are marketed like luxury items. Apple computers look better than the competition, are easier to use than the competition, and cost more than the competit ion. As opposed to its PC competitors who used different strategies to be able to offer the lowest prices possible, Apple’s offerings have always been about function and form first, cost second.Apple’s initial foray into the MP3 market touted ease of use as its main strategy. The Ipod featured a thumbwheel that simplified the access of thousands of songs stored in the player. The Ipod’s ease of use was essential to its adoption by newcomers who were unfamiliar to MP3. Secondly, ITunes was very innovative in the way that it simplified the process of legally obtaining music over the internet.Apple made possible a new distribution method for music through ITunes wherein the customer can access exactly the tracks he wants, anywhere in the world and the music is delivered directly to him. Lastly, the success of the Ipod has allowed Apple to leverage its brand strength as a key strategy for the Ipod. Apple successfully marketed the Ipod as a lifestyle accessory and a successful marketing campaign has made an Ipod (not an MP3 player, an Ipod) a must have.C. One key strength for Apple has been its brand. Successful products in the past have infused the Apple Brand with the values of ease of use, innovation and style. This is best seen through the cult of Apple fanaticism with Apple devotees preaching the values of Apple products to their peers. Like whole foods, the Apple brand has formed its own following that serves to promote the values of Apple products.While marketing strength may be a great strength of Apple, their culture of innovation allows them to maintain a lead over their competitors in terms of ease of use and new features. The key to Apple’s innovation is their focus on satisfying the needs and uses of the customer as opposed to simply cramming their products full of gadgetry.This is best seen during the years before Apple switched to Intel processors. While relegated to using the antiquated PowerPC processor from Motorola, Ap ple was able to maintain interest in their computers by innovating on the software side, coming out with the much acclaimed OS X operating system and its succeeding incarnations.Apple PCs have also been known to be better in design related applications. Macintosh computers are seen by many to be better suited for creative applications such as image processing, music creation and video editing. Apple itself has invested heavily in these areas with their free, pre-installed versions of these types of programs being far better than the offerings of their competitors.Lastly, Apple’s policy of being tight with its technology has allowed it to keep a high level of control over products associated with Apple products. Unlike other PC manufacturers whose technology is available to all OEMs, Apple technology is shut out to third party manufacturers. For the most part, Apple controls who makes Apple peripherals.While this limits the expansion of Apple and keeps the cost of its peripher als high, it has allowed Apple to maintain a high level of quality for its peripherals and its own products as their computers are less likely to crash due to shoddy third party products.

Statistical Significance and Homemade Shampoo

A Study on Gugo and Okra as Homemade Shampoo A Research Done by: Francine Faye A. Jumaquio Majaline Faye A. Tolentino Romer T. Nepumoceno Talavera National High School Talavera Nueva Ecija A Study on Gugo and Okra as a Homemade Shampoo Claudine M. Lajara I-Rosal Introduction This study was conducted to determine the effectiveness of a homemade shampoo out of the native Gugo, scientific name Entada phaseuoliodes and Okra, scientific name Abelomoschus Esculentus L. in making different type of hair stronger. Four phases were done: Phase 1, the control treatment; Phase 2, homemade shampoo compared to control treatment; Phase 3, homemade shampoo compared to varied concentration of gugo and okra; and Phase 4, where the acceptability of the homemade shampoo was determine in terms of smoothness, softness, and manageability. Statement of the Problem: Specifically, the researchers aimed to answer the following questions: 1. Can gugo and okra be used as raw material in making shampoo? 2. How effective are gugo and okra on the tensile strength of the hair? 3. Which treatment is more effective – treatments with greater concentration of okra han gugo or more gugo than okra? Procedure A. Preparation of Materials About 10,000 hair strands were gathered from four respondents having different types of hair, (normal, and dry, ethnic, curly). In each type of hair, 2020 strands were used: 240 strands for water, okra, 10 percent gugo, and 100 percent gugo; 240 strands for seven brands of shampoo; 12 0 strands for gugo and okra; and 600 strands for 10 treatments with varied concentration of okra and gugo. Five hundred grams of gugo bark were boiled in 70 ml of water for 30 minutes, and strained to extract the juice. The decoction was placed in a clean bottle. To prepare okra decoction, 200 grams of okra fruits were boiled in 200 ml tap water for10 minutes. The cooked okra was masked for extraction and decoction was strained for the preparation of solution. The homemade shampoo was prepared from 50 ml gugo decoction and 50 ml okra decoction. A 58ml coconut oil was added to the mixture and placed in an earthen pot. It was heated for 5 minutes and placed in a clean bottle. The homemade shampoo was then prepared into two setups: setup A and setup B. The treatment involves four type of hair (normal, dry, ethnic, curly). Setup A Treatment |Gugo (ml) |Okra (ml) | |1 |50 |50 | |2 |40 |60 | |3 |30 |70 | |4 |20 |80 | |5 |10 |90 | Setup B Treatment |Gugo (ml) |Okra (ml) | |1 |50 |50 | |2 |60 |40 | |3 |70 |30 | |4 |80 |20 | |5 |90 |10 | B. Soaking Process and Determination of the Hair Strength In phase 1, four treatments were prepared: †¢ Treatment 1: water, †¢ Treatment 2: Okra , †¢ Treatment 3: 10 percent gugo, †¢ Treatment 4: 100 percent gugo. These are the control treatments. Six bowls were prepared and labeled as 5, 10, 15, 20, 25, 30 minute, respectively. Sixty strands of normal hair were used and divided into 10 strands. The hair strands were simultaneously soaked in the respective bowls with 100 ml tap water and were removed when the time allotted for each bowl had elapsed. Then they were rinsed separately. They were placed in clean sheets of paper labeled according to the length of time they were soaked, (such as T1- water: 5 minutes; T2 – water: 10 minutes; and so on). The bowl used from the first treatment was washed thoroughly and were used again for the other treatments. The process was repeated for treatments 2, 3, 4. To determine the strength of the hair strands, a spring scale was used and five trials were done. From the 10 strands of normal hair, 5 strands from Treatment were tested. The hair strands were tied up to the spring scale at one end. A 15 cm length of the hair strands were maintained between the spring scale and the weight. The weight was pull until the hair snaps. The amount of force in Newton (I Newton = 100 grams) registered on the spring scale prior to the breaking of the hair was recorded and the average result from the five trial was computed. The process was repeated for treatments 2, 3, 4. Also the same process was done for ethnic, dry, and curly hair. In the second phase, 480 strands from four hair types were used. Out 480 strands, 120 strands of the hair were prepared for trial 1 and trial 2, using the homemade shampoo (gugo and okra). The same procedure ion phase 1 was done for these treatments. In the third phase, 2,400 strands of hair were prepared from the four types of hair. Out of 2,400 strands, 1,200 strands were used in setup A and another 1,200 in set up B. Each set up has 5 treatments and 60 hair strands were divided into ten, and each 10 were soaked separately in six bowls labeled 5, 10, 15, 22, 25, and 3 minutes, respectively. The same procedure from the previous phases was done to determine the hair strength. In the 4th phase, 20 female respondents, who had normal and dry hair were asked to apply Treatment 1 in setup A: 10 percent gugo + 90 percent okra. Most of their hairs were equal in length. The respondents treated their hair one by one. They wet their hair first and 20ml of this treatment was applied to the entire crown and was massaged on the scalp. After 1 min. , the hair was rinsed thoroughly with tap water. A clean towel was used to dry and comb their hair slowly. After 1 hour, the effect on the hair was observed using 1 to 4 scales. The following scales were used: |A. Softness |B. Smoothness |C. Manageability | |1 – slightly soft |1 – slightly smooth |1 – slightly manageable | |2 – fairly soft |2 – fairly smooth |2 – fairly manageable | |3 – soft |3 – smooth |3 – manageable | |4 – very soft |4 – very smooth |4 – very manageable | After having applied and observed the effects of treatment 1; treatments with 90% gugo + 10 % okra were used by the same respondents with the same procedure as of Set up A. Results, Discussion and Conclusion Phase 1: Significant comparison on the hair strength among the control treatments: There was no significant difference on the hair strength, considering the different types of hair. However, the longer the longer the time each type of hair was soaked, the greater the hair strength. Among the four treatments in this phase, the hair strength when soaked in 10 %gugo, were the strongest while water was the weakest. Phase 2: Significant comparison between homemade shampoo and control treatment: Normal hair was significantly strongest compared to curly, dry and ethnic. Among the control treatment, hair strength was the strongest when soaked in treatment three: 10% gugo. Treatment 1: water was registered the weakest. It was also observed that as the soaking time increased, the hair strength also increased. Phase 3: Significant comparison among homemade shampoo, control treatment, Setup A and Setup B: Normal hair was significantly stronger, curly hair was the weakest, while dry and ethnic hair were almost comparable to each other. 10% gugo registered the strongest hair strength, followed by okra, then okra and gugo. Together, these three treatments were significantly different from all other treatment. The longer the soaking time, the stronger the hair strength. Phase 4: Acceptability of treatments. For normal hair, the two treatments showed no significant differences in terms of smoothness, softness and manageability. The 90% gugo+ 10%okra treatment was fairly manageable and the 10% gugo + 90% okra treatment was manageable. For dry hair, the two treatments showed no significant difference in terms of smoothness, softness. But there was a significant difference of manageability at 0. 5 probability level. Recommendations Based on the findings, the researcher recommends the following: 1. Use okra as raw material for making shampoo; 2. Further study of the properties of the homemade shampoo to establish the effect on hair; 3. Follow-up research must be conducted on the acceptability for other types of hair; 4. This research would provide information to those who are interested in the production of this product. Bibliography Jumaquio, Francine Faye A. , et. al. , â€Å"A Feasibility Study of Gugo and Okra as Homemade Shampoo†.

Donor Donations And The Pros Of Being Compensated With...

Abstract For the past few weeks of class, our focus was to be on the topic of organ donation. More specifically if we feel that donors should be compensated with money for their donation. Through the discussions and arguments from classmates, I have found there to be many different takes on this subject, some with which I agree and others disagree. While all arguments will lead to disagreements on some level, I have found a chance to explain why I disagree with their disagreements, while still holding strong to my belief. We learned in class that every great argument presents with the main argument and sub-arguments or premises that thoroughly elaborate on the main argument in greater detail. My goal is to enhance and enlighten your†¦show more content†¦Secondly, the donor would be the only person not benefiting from the donation, so it only seems morally right to give back. The hospital staff are getting paid for their work and the individual receiving the organ may n ot be getting paid with money, but instead are receiving a very special gift. Third, the increase of organ donations due to a money incentive could potentially make donations safer by driving the Black Market to extinction. The Black Market would no longer be needed if more people are being taken off the wait list because of the increased influx of organ donations. While reiterated many times, I feel it is important to clearly convey my argument with the three premises that follow. Organ donors should be paid for their donations because it can reduce the shortage of organs, which in turn could save lives; they are doing an incredible thing for someone else, so it seems morally just; it could drive the Black Market to extinction, making donations much safer. This brings about an important word: altruism. By definition from Merriam Webster Dictionary, altruism is defined as â€Å"†¦feelings and behaviors that show a desire to help other people and a lack of selfishness† (2015). To me, individuals that donate their organs for another human beings prosperity can be defined as being altruistic. The shortage of organs is the biggest issue our country faces when it comes to their