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# INT MGT INF SYS MAJR ISDS 1102

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This 39 page Class Notes was uploaded by Velma Toy on Tuesday October 13, 2015. The Class Notes belongs to ISDS 1102 at Louisiana State University taught by Staff in Fall. Since its upload, it has received 29 views. For similar materials see /class/222500/isds-1102-louisiana-state-university in Info Systems & Decision Sciences at Louisiana State University.

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Date Created: 10/13/15

Business Statistics A First Course iii iii i iii Introduction and 1 I Data Collection Levine o Krehbiel o Berenson Learning Objectives In this chapter you learn ltgt How Statistics is used in business ltgt The sources of data used in business ltgt The types of data used in business ltgt The basics of Microsoft Excel Why Do We Need Statistics ltgt Statistics for description ltgt Statistics for drawing conclusions ltgt Statistics in practice 10162010 10162010 What is Statistics ltgt A branch of mathematics taking and transforming numbers into useful information for decision makers ltgt Methods for processing amp analyzing numbers ltgt Methods for helping reduce the uncertainty inherent in decision making Population vs Sample Population Sample lllll39lllll39l ll lll lillllliill quotli Measures used to describe the Measures computed from population are ca led sample data are called statis ics Basic Concepts Population Sample pulau39on consisls of Asample is the portion of all the items or individuals a population selected for about which you want to analysis draw a conclusion A parameter is a numerical A statistic is a numerical measure tnat describes a measure that describes a cnaracteristic of a population cnaracteristic ot a sample 10162010 Types of Statistics Popula on Descriptive Statistics sample Collecting summaizing an describing data 4 1 Size ii Inferenlial Statistics Drawing condusions aridor making decisions concerning Size N a population based on y on sarrple ata Descriptive Statistics ltgt Collect data D eg Survey quot ltgt Present data D eg Tables and graphs ltgt Characterize data 2X D eg Sample mean n Inferentiai Statistics ltgt Estimation I eg Estimate the population mean weight using the mple man we39ght ltgt Hypothesis testing I eg Test the claim that the population mean weight is 120 pounds a 10162010 Statistical Vocabulary Variable A variable is a characteristic ofan item or individual Data Data are the different valus associated with a ariable Operational Definitions Data valuesare meaningless unless their variables have operational de nitions universally accepte meanings that are clear in all associated with an analysis Why Collect Data ltgt A marketing research analyst needs to assess the effectiveness of a new teIeVISIon advertisement ltgt A pharmaceutical manufacturer needs to determine whether a new drug is more effective than those currently In use ltgt An operations manager wants to monitor a manufacturing process to find out whether the quality of the product being manufactured is conforming to company standards Sources of Data Primary Sources The data is collected by the researcher directly from herhis own observations and experiences om a political suney I Data collected from an experiment I Obsene ltgt Secondary Sources The data is originally collected internal or external elsewhere rather e researcher herhimself 1 Analyzing census data I Examining data from printjournals or data published on the internet 10162010 Categories of Data Sources Sources of data fall into four categories ltgt Data distributed by an organization or an individual ltgt A designed experiment ltgt A survey ltgt An observational study Types of Variables ltgt Categorical also known as qualitative variables have values that can only be placed into categories such as yes and oquot ltgt Numerical also known as quantitative variables have values that represent quantities Example Example Examles Examples MarilalExams x55 M 1 x1 Numberofbooks Waitingtime Gender AA BBC Numberoftext Weight VesNoquestions gs 15 messages Measured De ned eeiegenes Meaningful ordering Counted items materiel urdislances 10162010 Types of Data o Categorical variables have values that can only be placed into categories such as ya and no c Ordinal variables have valus that can indicate maningful ordering or distancs ltgt Discrete variables have numerical valus that arise from a counting procss 0 Continuous variables produce numerical rsponses that arise from a measuring procss Used For Statistics c SPSS a Astatistical package to perform statistical analysis a Designed to perform advanced statistical analyses c Microsoft Excel u A multrfunctional data analysis too a Can perform many functions but none as well as programs lhat are dedicated to a single function 0 Both SPSS and Excel use worksheeis to store dala Statistical Programs You are using programs properly if you can ltgt Understand how to operate the program ltgt Understand the underlying statistical concepts ltgt Understand how to organize and present information ltgt Know how to review resulB for errors ltgt Make secure and clearly named backups of your work 10162010 Chapter Summary In this chapter we have Reviewed why a manager needs to know statistics Introduced key de nitions Popuiation v5 Sampie Primarv v5 Secondarv data types Categoricai v5 Numericai data Examined dscriptive vs inferential statistics Reviewed data typs Discussed SPSS and Microsoft Excel terms 10242010 Business Statistics A First Course lil ill i iii Numerical Descriptive A 7 Measures Levine o Krehbiel o Berenson Learning Objectives In this chapter you learn ltgt To describe the properties of central t ndency variation and shape in numerical data ltgt To calculate descriptive summary measures for a population ltgt To construct and interpret a bogtltplot ltgt To calculate the covariance and the coef cient of correlation Summary De nitions ltgt The central tendency is the extent to which all the data values group around a typical or central value ltgt The variation is the amount of dispersion or scattering of values ltgt The shape is the pattern of the distribution of values from the lowest value to the highest value 10242010 The Mean ltgt The arithmetic mean often just called mean is the most common measure of central tendency o 39 For a sample of Size n n Z Thei hvalue 25 7171 X1Xz X n n Observed values The Mean c The most common measure of central tendency ltgt Man sum ofvalus divided by the number ofvalues c Affected by extreme valus outliers pm jm l 012345578910 012345578910 Me 4 123457E73 1273410 Q 4 5 5 5 5 The Median ltgt In an ordered array the median is the middle number 50 above 50 below 012 45578910 012i45678910 Not affected by extreme values 10242010 Locating the Median ltgt The location of the median when the values are in numerical order smallst to largest ltgt If the number of values is odd the median is the middle number ltgt If the number of values is even the median is the average of the two middle numbers Locating the Median Note t t 114 i rrh39e di honlvthe in the rankedidata E ofcthe ofgth39e39mediaf l The Mode ltgt Value that occurs most often ltgt Not affected by extreme values ltgt Used for either numerical or categorical data 01234567891011121314 10242010 The Mode ltgtThere may be no mode ltgtThere may be several modes 3133 11 01234557891011121314 M 9381 Not affected by extreme values Review Example thiVsEFrices39 Mean 30000005 1909719 600300 500000 Median middle value of ranked 300000 33391 0 0 000 data 00039 300000 sum 3000000 Mode most frequent value 100000 Which Measure to Choose ltgt The mean is generally used unless extreme values outliers eXIst ltgt The median is often used since the median IS not sensitive to extreme values For exam le median home prices may be reported or a region it is less sensitive to outliers ltgt In some situations it makes sense to report both the mean and the median 10242010 Summary Central Tendency ALILlalm Middgvalue Most in the ordered frequenth array obse Measures of Variation Measures of variation give information on line 5 read or varlability or dispersion or line data values The Range ltgt Simplest measure of variation ltgt Difference between the largest and the smallest values Range Xlargest X smallest Example 01234557391011121314 l l Range13112 10242010 Misleading Range ltgt Ignores the way in which data are distributed 10 11 12 ltgt Sensitive to outliers 111llllllllZZZZZZZZ333345 L 1 1 1 1 1 1 1 1 1 122zzzzzz333341zn The Variance ltgt Average approximately of squared deviations of values from the mean Sample variance Where X arithmetic mean n sample Size x ith value of the variable x The Standard Deviation ltgt Most commonly used measure of variation ltgt Shows variation about the mean ltgt Is the square root of the variance ltgt Has the same units as the original data Sample standard deviation 10242010 The Standard Deviation Steps for Computing Standard Deviation Compute the difference between each value and Square each difference Add the squared differences Divide this total by n1 to get the sample variance 5quot 9 Take the square root of the sample variance to get the sample standard deviation Calcuiation Example Sample Data XI 10 12 14 15 17 18 18 24 n 8 Mean 7 15 7 n 1 8 1 7 130 39V Amasureofme aveage scatter i7 39 aroundme mean Comparing Standard Deviations 1112131415161718192021 1112131415161718192021 1112131415161718192021 Comparing Standard Deviations c The more the data are spread out tne greater the range variance and standard deviation ltgt The more the data are concentrated the smaller tne range variance and standard deviation ltgt ittnevaides re aiitne same no variation all these measures Will be zero ltgt None of tnese measures are ever negative Smaller std dev vs Larga39 std dev Locating Extreme Outliers ZScore c To compute the Zsonre ofa data value subtract the man and divide by the standard deviation 0 The Z score is the number ofstandard deviations a data value is from the man 0 Adata value is considered an extreme oudier if its Z score is las than 30 or grater than 30 the data value Is from the m o The larger the absolute value of the Z score the farther an ZScore Z H S where X represents the data value Y is the sample mean S is the sample standard deviation 10242010 10242010 Z Score ltgt Suppose the mean math SAT score is 490 with a standard deviation of 100 ltgt Compute the Z score for a test score of 620 Zixii 7 520490 E s 100 13 Ascure uf ZEI 15 1 3 Standard devialmns abuve Human and vmuld 139 arm cunsidaed an undue Shape of a Distribution ltgt Describes how data are distributed ltgt Measures of shape D Symmetric or skewed Leftskewed Symmetric Rightskewed Munlt Median MnMdin Medinlt Mun General Descriptive Stats Using Microsoft Excel 1 Select Tools 2 Select DataAnalysis 3 SelectDescriptive swim and click OK 10242010 General Descriptive Stats Using Microsoft Excel 4 Enter the cell range 5 Check the Summary Statistics box 6 Click OK Excel output Mqom Excel l Hm pm descrlpuve stausucs output 7 Me 1 m using ne house prloe data 357nm 5754 33030 House Prices 0mm 03mm 7 5 tEm 2000000 39 mm s mum 500000 2 105835333 300000 1930303 umu 100900 2030303 100000 Numerical Descriptive Measures for a Population ltgt Dacriptive statistics discussed previously described a sample not the population ltgt Summary measures describing a population called parameters are denoted with Greek letters ltgt Important population parameters are the population mean variance and standard deviation 10 10242010 The mean u ltgt The population mean is the sum of the values in the population divided by the population Size N Where u population mean N population size x ii value of the variable x The Variance o2 ltgt Average of squared deviations of values from the mean D Population variance 0 hi N 209 ally z i N Where H population mean N population Size xi ii value or the variable x The Std Deviation o ltgt Most commonly used measure of variation ltgt Shows variation about U16 mean ltgt Is the square root of the population variance ltgt Has the same units as the ori inal data D Population std deviation 0 11 10242010 Sample statistics versus Population parameters Measure Population Sample Slau39su39c Mean 7 1 X V mance 52 S Smndard U S Deviation The Empirical Rule ltgt The empirical rule approximates the variation of data in a bellshaped distribution ltgt Approximately 68 of the data in a bell shaped distribution is within 1 standard deviation of the mean or p t 10 The Empirical Rule ltgt Approximately 95 ofthe data in a bell shaped distribution Iis within two slandard deviations of the man or u l 20 ltgt Approximately 997 ofthe data in a bell sha ed P distribution Iis within three standard deviations of the man or u t 12 10242010 Using the Empirical Rule ltgt Suppose that the variable Math SAT scores is bellshaped with a mean of 500 and a standard deviation of 90 Then n 68 ofall tst takers scored between 410 and 590 500 l 90 n 95 ofall tat takers scored between 320 and 680 500 l 180 n 997 ofall tst takers scored between 230 and 770 500 l 270 The Five Number Summary The five numbers that help describe the center spread and shape of data are Xsmallest a First Quartile Q n Median Q a Third Quartile Q3 Xargest Quartiles ltgt First quartile Q1 Lu ranked value ltgt lird quartile 3 l 1 Q3 4 ranked value ltgt Interquartile range IQ Q3 Q1 13 10242010 The five number Relationships and Distribution Shape Five Number and The Boxplot ltgt The Boxplot A Graphical display of the data based on the fivenumber summary Xsmallas1 quot Q1 quot Madial l Q3 Xiarggst Exarrple 25 of data 25 25 25 of data of data OF data Xsmallest Q1 Madian Q3 Xiargg ltgt A Boxplot can be shown in either a vertical or horizontal orientation Shape of Boxplots o Ifdala are symmetric around the median then the box and central line are centered between the endpoints Xsmallest Q1 Median Q3 Xlargest o A Boxplot can be shown in either a vertical or horizontal orientation 14 10242010 Distribution Shape and The Boxplot Leftskewed Symmetric Rightskewed maze WQZ L l CDll lIl llEEI l Boxplot Example ltgt Below is a Boxplot for the following data Xlargest Xsmallesi Q1 Q2 Q3 2 2 3 4 5 9 I ltgt The data are right skewed as the plot depicts Boxplot and Outliers ltgt The boxplot below of the same data shows the outlier value of 27 plotted separately 0 Avalue is considered an outlier if it is more than 15 tims the interquartile range below Q1 or above Q3 Example Bumlul Showing Pa Outllel Sample Dela 15 10242010 Pitfalls in Numerical Descriptive Measures ltgt Data analysis is objective D Should report the summary measures that best describe and communicate the important aspecB of the data set ltgt Data interpretation is subjective D Should be done in fair neutral and clear manner Ethical Considerations Numerical descriptive measures ltgt Should document both good and bad results ltgt Should be presented in a fair objective and neutral manner ltgt Should not use inappropriate summary measures to distort facts Chapter Summary ltgt Described measures of central tendency D Mean median mode ltgt Described measures of variation a Range lnterquartile range variance and standard deviation coefficient of variation ascores ltgt Illustrated shape of distribution a y metric skewe ltgt Described data using the 5number surrrnary a Boxplom ltgt Discussed covariance and correlation coefficient ltgt Addressed pitfalls in numerical descriptive measures and ethical considerations 16 Business Statistics A First Course Presenting Data in 122quot I Tables and Charts Levine o Krehbiel o Berenson Learning Objectives In this chapter you learn ltgt To develop tables and charts for categorical data ltgt To develop tables and charts for numerical data ltgt The principles of properly presenting graphs ltgt To construct your own project Tables amp Graphs for Categorical Data Categorical Data Graphing Data 10162010 10162010 Summary Table A summary iable indicats the frequency amount or percentage of items in a set of categoris so that you can see dif39ferencs between categories Bar and Pie Charts ltgt Bar charts and Pie charts are often used for categorical data ltgt Pie chart is used when the portion of the whole is important ltgt Bar chart is appropriate if category comparison is important Bar Chart In a bar chart a bar shows each category the length of which reprsenis the amount frequency or perceniage of valus falling into a category Banking irreverence imemet in Person a branch onyamughsemmu branch Amammedm iweieiephane I NM 10162010 Pie Chart The pie chart is a circle broken up into slices that reprsent categoris The size of mch slice of line pie varis according to the percentage in ach category Banking Preference mm uman ailivelelephane DDme lmauvhsewimalbianch mu Pusan branch Ilnlemel Tables and Charts for Numerical Data Numerical Data Ordered Array ltgt An ordered array is a sequence ofdata in rank order from the smallst value to the largst value Shows range minimum value to maximum value May help identify outliers unusual obsenations 10162010 Stem andLeaf Display ltgt A simple way to see how the data are distributed and where concentrations of data exist ltgt MEI39HOD Separate the sorted data series into leading digits the stems an the trailing digits the leaves Stern and Leaf Display ltgt A stemandlmfdisplay organizes data into groups called stems so that the valus within mch group the leavs branch out in the right on each row Age at Cnllege Students Day Students Night Students Stan 1 67788899 1 Z I l 2257 Z 3 28 3 23 4 4 Frequency Distribution ltgt The frequency distribution is a summary table in which the data are arranged into numerically ordered classes ltgt Ex mple A manufacturer of insulation randomly selects 20 winter days and records the daily high temperature 24 35 17 21 24 37 26 46 58 3o 32 13 12 3s 41 43 44 27 53 27 10162010 Frequency Distribution Example ltgt Sort raw data in ascending order 12 13 17 21 24 24 26 27 27 3O 32 35 37 38 41 43 44 46 53 58 39 ltgt Find range 58 12 46 ltgt Select number of classes 5 usually between 5 and 15 ltgt Compute class interval width 10 46 then round up Frequency Distribution The number of classes depends on the number of values in the data With a larger number of values typically there are more classes general a frequency distribution should have at least 5 but no more than 15 classes To determine the width of a class interval you divide the range Highest value Lowest value of the data by the number of class groupings desired Frequency Distribution Example ltgt Determine class boundaries limiB Class 1 10 to less than 20 Class 2 20 to less than 30 Class 3 30 to less than 40 Class 4 40 to less than 50 Class 5 50 to less than 60 ltgt Compute class midpoints 15 25 35 45 55 ltgt Count observations amp assign to classes 10162010 Frequency Distribution Example Data in nmeredanay 12 15 1721 2a 24 2527 27 5115235 57 5541 a5 M as 55 SE glass Frequency Reletlv Percentage Frequency 10 but less hm 2o 3 20 but less then 30 5 30 but less then 40 5 25 25 4 2 40 but less hm SO 50 but less H31 60 Total 20 1 00 100 Cumulative Frequency Data in nmeredanay 12 15 1721 2a 24 2527 27 5115255 57 5541 a5 M as 55 55 mag Fre 1n butless than 211 2 but less than an s 3D 3D 9 45 3D butiess than 411 5 25 25 14 7m 4B but less than 5n 4 2m 2m 13 an 5B but less than 6n 2 1m 1m 20 mm Tutsi 2n 1 DD mu Why Use a Frequency Distribution ltgt It condenses the raw data into a more useful form ltgt It allows for a quick visual interpretation of the data ltgt It enables the determination of the major characteristics of the data set including ata are concentrated clustered 10162010 Frequency Distributions Some Tips ltgt Different class boundaris may 394 39 39 for the same data especially for smaller data sets Shi s in data concentration may show up when different class boundaris are chosen ltgt As the size of the data set incrmss the impa t alterations in the selection ofclass boundaris is grmtly en comparing two or more groups with different sample sizes you must use either a relative frequency or a percenmge distribution The Histogram ltgt A vertical bar chart of the data in a frequency distribution is called a histogram ltgt A histogram is a bar chart for grouped numerical data in which the frequencies or per ntages of eac group of numerical data are represented as indivi ual vertical ars ltgt In a bars histogram there are no gaps between adjacent ltgt The height of the bars represent the frequency ge relative frequency or percenta The Histogram m but less than 2n 2n but less than an am but less than 40 m but less than an an but less than an m 7 HistogramDaily High Temperature m aenq e vertical aXiS W0 dehne to w m a percentage of observations per s 15 2s 5 ts sswe class 10162010 The Frequency Polygon m Mid inhut ess than 2m 15 20 but 255 than an 25 5 3B but 255 than 40 35 s m but 25 than 5U 45 4 Fvequencv Pn vgnn DaW Huh Temperature su but 255 than an ss 2 7 a Ap me e po ygon 1 35 ormed by havmg e so mdpomt of eadn dass is represent use data m E d and mm connecung the 1 0 NB thuHessthanZD 0 2 but 255 than 3n 2 45 an huuess than 40 1 7n oguemallyllu umumue l huHessthanSD 4 9n mu SDbut essthan D su mu g an E Bu ecurmauvepercen g E 40 pdygon or ogwe dran ays ne 2n v1b efrtres ta o m X E amend me curru auve u m an m 50 an peoentages a ong me Vans mwermax Eour iw Time Series Plot ATme Saris Plot is used to NJrrberof Yea Fmd g stfudy patterns In b1evalus 19 43 a a numeric varla eover 1997 54 me39 1998 60 Nurrher anrznchisvs 19961m6 1999 73 mm 39 o 2000 82 Ea an 2001 95 g En E E 2002 107 g on o 2003 99 In 2004 95 1 a 1996 1598 mm mm mm 20E 10162010 Principles of Excellent Graphs The graph should not distort the data The graph should not contain unnecessary adornmenls sometimes referred to as chart junk The scale on the vertical axis should begin at zero All axes should be properly labeled The graph should contain a title The simplest possible graph should be used for a given set of data Graphical Errors Chart Junk and Presentalinn Gnnd Presentzlinn Minimum Wage Minimum Wage 1960 1970 1980 1990 Graphical Errors No Relative Basis S receive Freq students quot 2nn 2quot 1nn 1n n n m so 1n sn FR so 1n sn 8 Bad Presentation Good Presentation A39 a by l FR Freshmen so Suphumnre JR Juniur SR Seniur l 10162010 Graphical Errors Compressing the Vertical Axis 8 Bad Presentation Good Presentation Quarterly Sales Quzrmiysziex Graphical Errors No Zero Point on the Vertical Axis Had Presentation Gnnd Presentations Mumth Sales s Mumhly Sales Chapter Summary In this chapter we have Organized categorical data using the summary table bar chart pie chart and Pareto chart Organized numerical data using the ordered array stemandleafdisplay frequency distribution histogram polygon an ogive Examined cross tabulated data using the contingency ta le Developed scatter plos and time series graphs Examined the do39s and don39ts ofgraphically displaying data 10 Study Guide for Business Statistics ISDS 1102 KEY TERMS Study the Key Terms at the end of each chapter especially those discussed in class Make sure you not only know the definition of each key term but also how to apply each term KEY EQUATIONS Study the key equations at the end of each chapter and understand them You will not be required to memorize these formulas but you will need to understand them LECTURE SLIDES There is a lot of content in the PowerPoint Lecture Slides for Chapters 13 Make sure you study all of the examples in the slides and understand each of the calculations ADVANCED EXCEL PROJECT Make sure you look over your project and understand what you learned by doing the project Know the difference between the different graphs you created You will not be tested on how to complete the steps in Excel The project showed that you understood how to use Excel to produce the graphs charts and tables CHAPTER REVIEW PROBLEMS Chapter 1 Problems for Section 16 Applying the Concepts 14 15 16 17 and 18 Chapter 1 Review Problems Checking Your Understanding 110 111 112 113 and 114 Chapter 1 Review Problems Applying the Concepts 120 121 and 124 Chapter 2 Problems for Section 21 Learning the Basics 21 a b c Chapter 2 Problems for Section 22 Learning the Basics 29 210 211 212 Chapter 3 Problems for Sections 31 and 32 Learning the Basics 31 32 33 and 34 Chapter 3 Problems for Sections 31 and 32 Applying the Concepts 35 38 and 39 Chapter 3 Problems for Section 34 Learning the Basics 324 a b c and 330 Chapter 3 Chapter Review Problems Checking Your Understanding 341 342 343 344 347 349 CLOUD COTVIPUTING Cloud Computing is a model for enabling convenient on demand network access to a shared pool of con gurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction Promotes availability and is composed of ve general characteristic on demand self service broad network access resource pooling rapid elasticity and measured service 1 On demand self service 0 The customer or subscriber without the need to contact or interact with a human from the cloud provider can increaser or decrease computing requirements as needed Can include such necessities as server use network storage and software applications 2 Broad network access a The most significant component of cloud computing b The services offered by a provider must be accessible over a network from any location and on any standardized platform including mobile pones and PDAs There s a fee The Internet the only network that provides this scope of capability is the core cloud computing and is associated with the cloud image that is part of every cloud computing illustration 3 Resource pooling a The provider s ability to pool services to accommodate they use by multiple subscribers at the same time b The subscriber is unaware of the location of the resources her or she is using or any reassignmenttaking place c The user gets the programs and hardware support that they need and the provider gets paid 4 Rapid elasticity a Ability of a subscriber to increase computing resources in spike or peak times without having to worry about overloading a system or having to purchase additional hardware for a minimal amount of highperformance need b The size and capacity of a cloud provider allow the subscriber to sale up or down as need and pay for only the time and amount of services used 5 Measured service a The cloud provide must meter usage to use this information for billing but more importantly to analyze usage and respond appropriately b Must meet the subscriber s needs 0 9 Main components of cloud computing 0 Hosting computers and subscribers 3 major differences In cloud computing the delivery of the services from the provider to a subscriber must be over the Internet The services provided over the cloud by a provider are scalable they can be increased or decreased as the needs of an individual subscriber or company change Services are typically offered and billed by the minute or hour The services provided are managed completely by the cloud provide the owner or manager of the host The client of subscriber of the service does not have to worry about having a speci c computer or operating system or a certain processor or amount of RAM and does not need to purchase software upgrades or download service patches 0 Cloud Computing Service Categories 0 o 0000 INFRASTRUCTUREASASERVICE Infrastructure as a service Outsourcing of hardware the equipment used to sustain the operations of a company or enterprise Because it encompasses storage devices actual servers and network components Also referred to as HardwareasaService Virtualization the application and infrastructure are independent Meaning that tone physical machine can run several virtual machines Virtual machine not an actual physical machine but a softwarecreated segment of a hard drive that contains its own operating system and applications which makes it behave as a separate physical machine in the eyes of the user Grid The combination of several computers or virtual machines that are connected over a network to make them appear and function as one single computer The use of both virtualization and grids has made it possible for Infrastructureas aservice to provide virtual data characters and enable companies to eventually reduce the overall cost of doing business I Eliminate high cost of equipment and personal to manage such a facility I Focus more on their ore business objectives I Pay for only the equipment they use Main factors driving enterprises to use InfrastructureasaService I Reduce budget outlay for equipment and its continual upgrade and maintenance I Fast time to market with programs and ideas because the equipment needed to run them can be added to the cloud subscription The reassignment of IT personnel from a focus on learning and administering new equipment because that is now done b the provider of the service to more businessrelated tasks The replacement of unknown costs associated with running an inhouse datacenter with known predetermined operation costs provided through set subscription rates used on the services used over a period of time o PLATFORMASASERVICE permits subscribers to have remote access to application development interface development database development storage and testing 0 Enable s the creation and testing of subscriberdevelopment programs and interfaces using a cloud provider s hardware and development environment 0 For the subscriber this is a huge savings because the equipment and software do not have to be purchased to test a possible application or interface and the fear of crashing a system during testing is alleviated by using the provider s secure test environment be deployed fr SOFTWAREASASERVICE model of cloud computing enables software to om a cloud provider delivered over the intemet and accessed by a subscriber through a browser 0 Prim I o Twom I and physically O 3 basic I Approximately 85 of subscribers are satis ed with the service 80 would renew their subscriptions 61 would expand their services ary reasons to subscribe to SaaS Limited risk Rapid deployment Fewer upfront costs such as the expense of purchasing a server Increased reliability as seen in reduced downtime caused by service disruptions Standardized backup procedures Lower total cost of ownership through reduced hardware costs software purchases license agreements and personal to run and administer the systems ajor categories of SaaS Consumeroriented services offered to the public either on a subscription basis or if supported by advertisement for no cost Business services is sold to enterprise and business organizations of all sizes usually on subscription basis Cloud Deployment Methods the way cloud services are accessed owned used located determines the deployment of the cloud service types 1 Private Cloud operated for single organization and its authorized users The infrastructure can exist onsite or offsite and s controlled by either the organization or a contracted third party 0 Community cloud is an extension of a private cloud in which originations with similar missions share the infrastructure to reduce cost 2 Public Cloud available to the general public large organizations or a group of organizations it offers the most risk because users that have not been authenticated or established as trusted access it 0 Owned and operated by a cloud provider and is located off site 3 Hybrid cloud deployment method is a combination of two or more clouds that are unique but are connected by common standard technology that enables the sharing of applications and date 0 Infrastructure can be located both onsite and off the premises and it can be managed by both the organizations as a cloud provider 39 A big difference between cloud deployment models is the concern over security A careful needs assessment and examination of security requirements are key to making the right choices for cloud computing Hybrid cloud is a combination of both types of deployment A portion of the cloud is private and behind the rewall whereas another portion is public and outside the rewall 0 Can be used to ease the transition from a private cloud to a public cloud or to secure portions of enterprise data in the private segment while still enabling access to the wide scope of services offered by public segment 0 Pros and Cons of Cloud Computing I PROS 0 Scale and Cost 0 Origination doesn t have to purchase equipment license software and hire personal 0 Encapsulated change management 0 0 Hardware and associated technology can be maintained redistributed and redirected without major recon guration Cloud operating system is specially designed to run a cloud provider s datacenter and is delivered to subscribers over the Intemet or other network 0 Choice and agility O O O Subscriber deploys solutions that best suit current needs and trends Interoperability the ability of a service from on provider to work wit the services of another without any subscriber interaction MiddlewareO a broad term for software that enables interoperability by assisting the passing of data among applications I Provides safe interface between network nodes and services 0 Nextgeneration architectures o I CONS o Lockin 0 Innovation and foresight in IT are not a threat to the bottom line or current operations If you decide to change there s a penalty Buy and beware 0 Reliability 0 Anything could happen to it but it has an elaborate backup system 0 Lack of control 0 Controls of resources is surrender to someone in the cloud 0 Security 0 O O Concerns are centered on the data being stored on servers owned and controlled by the cloud adviser Sarbanes Oxley an act administered by the Security and Exchange Commission that speci es the type of records that need to be stored and how long they must be kept but leaves the method of storage up to the business Composite Cloud evolves when a primary cloud provider offers services that are distributed through another cloud provider Risk managementthe process of analyzing exposure to risk and determining how to best handle it within the tolerance level set b

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