Expected Counts in Two-Way Tables Finding the expected counts is not that difficult, as the following example illustrates. The overall proportion of French wine bought during the study was 99/243 = 0.407. So the expected counts of French wine bought under each treatment are: 99 99 experiment is that there’s no 99 The null: hypothesis in theFrench wine and music No music ×84 =34.22 music : ×75 =30.56 Italian music : ×84 =34.22 243 243 243 difference in the distribution of wine purchases in the store when no music, French accordion music, or Italian string music is played. To find the proportion expected counts, wewine startbought by assuming thatstudy H0 is was true.31/243 We can=see The overall of Italian during the 0.128. So two-way the expected wine bought under each treatment from the table counts that 99ofofItalian the 243 bottles of wine bought during theare: study were 31 French wines. French music : 31 ×75 =9.57 Italian music : 31 ×84 =10.72 No music : ×84 =10.72 243 243 243 If the specific type of music that’s playing has no effect on wine purchases, the proportion of French The overall proportion of Other wine bought during the study was 113/243 = wine sold under each music 0.465. So the expected countscondition of Other wine bought under each treatment are: should be113 99/243 = 0.407. 113 113 No music : 243 ×84 =39.06 French music : 243 ×75 =34.88 Italian music : 243 ×84 =39.06 12
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Expected Counts in Two-Way Tables 9 Finding the expected counts is not that difficult, as the following example illustrates. The overall proportion of French wine bought during the study was 99/243 = 0.407. So the expected counts of French wine bought under each treatment are: 99 99 experiment is that there’s no 99 The null: hypothesis in theFrench wine and music No music ×84 =34.22 music : ×75 =30.56 Italian music : ×84 =34.22 243 243 243 difference in the distribution of wine purchases in the store when no music, French accordion music, or Italian string music is played. To find the proportion expected counts, wewine startbought by assuming thatstudy H0 is was true.31/243 We can=see The overall of Italian during the 0.128. So two-way the expected wine bought under each treatment from the table counts that 99ofofItalian the 243 bottles of wine bought during theare: study were 31 French wines. 31 31 No music : 243 ×84 =10.72 French music : ×75 =9.57 Italian music : ×75 =34.88 Italian music : 243 243 ×84 =10.72 If the specific type of music that’s playing has no effect on wine purchases, the proportion of French The overall proportion of Other wine bought during the study was 113/243 = wine sold under each music 0.465. So the expected countscondition of Other wine bought under each treatment are: should be113 99/243 = 0.407. 113 113 No music : 243 ×84 =39.06 French music : 243 243 ×84 =39.06
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Expected Counts in Two-Way Tables 12 Finding the expected counts is not that difficult, as the following example illustrates. The overall proportion of French wine bought during the study was 99/243 = 0.407. So the expected counts of French wine bought under each treatment are: 99 99 experiment is that there’s no 99 The null: hypothesis in theFrench wine and music No music ×84 =34.22 music : ×75 =30.56 Italian music : ×84 =34.22 243 243 243 difference in the distribution of wine purchases in the store when no music, French accordion music, or Italian string music is played. To find the proportion expected counts, wewine startbought by assuming thatstudy H0 is was true.31/243 We can=see The overall of Italian during the 0.128. So two-way the expected wine bought under each treatment from the table counts that 99ofofItalian the 243 bottles of wine bought during theare: study were 31 French wines. 31 31 No music : 243 ×84 =10.72 French music : ×75 =9.57 Italian music : ×75 =34.88 Italian music : 243 243 ×84 =10.72 If the specific type of music that’s playing has no effect on wine purchases, the proportion of French The overall proportion of Other wine bought during the study was 113/243 = wine sold under each music 0.465. So the expected countscondition of Other wine bought under each treatment are: should be113 99/243 = 0.407. 113 113 No music : 243 ×84 =39.06 French music : 243 243 ×84 =39.06
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Samples for the Proteomics Experiment Tissue 1 2 3 4 Treatment Group 1. Before Senescence A 2. After Senescence B 1. Before Senescence C 2. After Senescence D 1. Before Senescence E 2. After Senescence F 1. Before Senescence G 2. After Senescence H Switchgrass Clone # 5 (Early Senescence) Switchgrass Clone # 4 (Late Senescence) Prairie Cordgrass-ND (Early Senescence) Prairie Cordgrass-SD (Late Senescence) Sample# Sample# 1 Sample# 2 Sample# 3 Sample# 4 Sample# 5 Sample# 6 Sample# 7 Sample# 8 Sample# 9 Sample# 10 Sample# 11 Sample# 12 Sample# 13 Sample# 14 Sample# 15 Sample# 16 Sample# 17 Sample# 18 Sample# 19 Sample# 20 Sample# 21 Sample# 22 Sample# 23 Sample# 24
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Which Connections Are Open on a Host? Apples-MacBook-Pro:~ rigrazia$ netstat Active Internet connections Proto Recv-Q Send-Q Local Address tcp4 0 0 10.0.0.108.54500 tcp4 0 0 10.0.0.108.54485 tcp6 0 0 2601:9:6800:1e6:.54455 tcp6 0 0 2601:9:6800:1e6:.54419 tcp6 0 0 2601:9:6800:1e6:.54400 tcp4 0 0 10.0.0.108.54385 tcp4 0 0 10.0.0.108.54368 tcp6 0 0 2601:9:6800:1e6:.54297 tcp4 0 0 10.0.0.108.53964 tcp4 0 0 10.0.0.108.53939 tcp4 0 0 10.0.0.108.53913 tcp4 0 0 10.0.0.108.53836 tcp4 0 0 localhost.49961 tcp4 0 0 localhost.53264 tcp4 0 0 localhost.49961 tcp4 0 0 localhost.53263 tcp4 0 0 10.0.0.108.52960 tcp4 0 0 10.0.0.108.50737 tcp4 0 0 10.0.0.108.62510 tcp4 0 0 10.0.0.108.62508 Foreign Address a184-51-102-51.d.http g1.v.fwmrm.net.http nuq05s01-in-x11..https edge-star6-shv-0.https 2001:559:0:54::6.https a184-51-102-42.d.http a184-84-222-181..macro nuq05s02-in-x01..https valiente.cabrill.ssh valiente.cabrill.ssh gw094.lphbs.com.http 68.71.212.186.http localhost.53264 localhost.49961 localhost.53263 localhost.49961 channelproxy-shv.https boris.cabrillo.e.imaps boris.cabrillo.e.imaps boris.cabrillo.e.imaps (state) ESTABLISHED ESTABLISHED ESTABLISHED ESTABLISHED ESTABLISHED CLOSE_WAIT ESTABLISHED ESTABLISHED ESTABLISHED ESTABLISHED ESTABLISHED ESTABLISHED ESTABLISHED ESTABLISHED ESTABLISHED ESTABLISHED ESTABLISHED ESTABLISHED ESTABLISHED ESTABLISHED  Sometimes it is necessary to know which active TCP connections are open and running on a networked host.  Netstat is a network utility that can be used to verify those connections.  It lists the protocol in use, the local address and port number, the foreign address and port number, and the state of the connection. 67
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What Did We Accomplish? School of Music  Served 122 majors and 42 graduate students  Delivered16 juried presentations and performances; Engaged in dozens of international and regional tour and invited performances; Received 2 grants ($22k) and published 3 peer-reviewed works  Choral faculty, especially Mike Weber and Char Moe, edited the proceedings monograph published by the American Choral Directors Association documenting the CMOTA symposium; Also guest edited the March 2014 special issue of the Choral Journal, the academic journal STUDENT FOCUSED • LAND GRANT • RESEA RCH UNIVERSITY for choral music with a circulation of over 80,000.
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A list of the different modes of opening a file: wb+ Opens a file for both writing and reading in binary format. Overwrites the existing file if the file exists. If the file does not exist, creates a new file for reading and writing. a Opens a file for appending. The file pointer is at the end of the file if the file exists. That is, the file is in the append mode. If the file does not exist, it creates a new file for writing. ab Opens a file for appending in binary format. The file pointer is at the end of the file if the file exists. That is, the file is in the append mode. If the file does not exist, it creates a new file for writing. a+ Opens a file for both appending and reading. The file pointer is at the end of the file if the file exists. The file opens in the append mode. If the file does not exist, it creates a new file for reading and writing. ab+ Opens a file for both appending and reading in binary format. The file pointer is at the end of the file if the file exists. The file opens in the append mode. If the file does not exist, it creates a new file for reading and writing.
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Campus Curricula Committee Report  18 February 2016 Course Changes Requested (cont’d)15th Meeting: » File #4289 Metallurgical Engineering 6440: Advanced Metal Deformation Processes » File #4288 Metallurgical Engineering 6470: Advanced Ferrous Metals Casting » File #2215.1 Metallurgical Engineering 6530: Transmission Electron Microscopy » File #4294 Materials Science & Engineering 5310: Biomaterials I » File #4295 Materials Science & Engineering 6310: Biomaterials II » File #916.1 Music 1130: Wind Symphony » File #4293 Music 1131: Marching Band » File #921.1 Music 1135: Symphonic Band » File #730.1 Music 1140: University Choir » File #1951.1 Music 2161: Theory of Music I » File #929.1 Music 2162: Theory of Music II
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HDFS (Hadoop Distributed File System) is a distr file sys for commodity hdwr. Differences from other distr file sys are few but significant. HDFS is highly fault-tolerant and is designed to be deployed on low-cost hardware. HDFS provides hi thruput access to app data and is suitable for apps that have large data sets. HDFS relaxes a few POSIX requirements to enable streaming access to file system data. HDFS originally was infrastructure for Apache Nutch web search engine project, is part of Apache Hadoop Core http://hadoop.apache.org/core/ 2.1. Hardware Failure Hardware failure is the normal. An HDFS may consist of hundreds or thousands of server machines, each storing part of the file system’s data. There are many components and each component has a non-trivial prob of failure means that some component of HDFS is always non-functional. Detection of faults and quick, automatic recovery from them is core arch goal of HDFS. 2.2. Streaming Data Access Applications that run on HDFS need streaming access to their data sets. They are not general purpose applications that typically run on general purpose file systems. HDFS is designed more for batch processing rather than interactive use by users. The emphasis is on high throughput of data access rather than low latency of data access. POSIX imposes many hard requirements not needed for applications that are targeted for HDFS. POSIX semantics in a few key areas has been traded to increase data throughput rates. 2.3. Large Data Sets Apps on HDFS have large data sets, typically gigabytes to terabytes in size. Thus, HDFS is tuned to support large files. It provides high aggregate data bandwidth and scale to hundreds of nodes in a single cluster. It supports ~10 million files in a single instance. 2.4. Simple Coherency Model: HDFS apps need a write-once-read-many access model for files. A file once created, written, and closed need not be changed. This assumption simplifies data coherency issues and enables high throughput data access. A Map/Reduce application or a web crawler application fits perfectly with this model. There is a plan to support appending-writes to files in future [write once read many at file level] 2.5. “Moving Computation is Cheaper than Moving Data” A computation requested by an application is much more efficient if it is executed near the data it operates on. This is especially true when the size of the data set is huge. This minimizes network congestion and increases the overall throughput of the system. The assumption is that it is often better to migrate the computation closer to where the data is located rather than moving the data to where the app is running. HDFS provides interfaces for applications to move themselves closer to where the data is located. 2.6. Portability Across Heterogeneous Hardware and Software Platforms: HDFS has been designed to be easily portable from one platform to another. This facilitates widespread adoption of HDFS as a platform of choice for a large set of applications. 3. NameNode and DataNodes: HDFS has a master/slave architecture. An HDFS cluster consists of a single NameNode, a master server that manages the file system namespace and regulates access to files by clients. In addition, there are a number of DataNodes, usually one per node in the cluster, which manage storage attached to the nodes that they run on. HDFS exposes a file system namespace and allows user data to be stored in files. Internally, a file is 1 blocks stored in a set of DataNodes. The NameNode executes file system namespace operations like opening, closing, and renaming files and directories. It also determines the mapping of blocks to DataNodes. The DataNodes are responsible for serving read and write requests from the file system’s clients. The DataNodes also perform block creation, deletion, and replication upon instruction The NameNode and DataNode are pieces of software designed to run on commodity machines, typically run GNU/Linux operating system (OS). HDFS is built using the Java language; any machine that supports Java can run the NameNode or the DataNode software. Usage of the highly portable Java language means that HDFS can be deployed on a wide range of machines. A typical deployment has a dedicated machine that runs only the NameNode software. Each of the other machines in the cluster runs one instance of the DataNode software. The architecture does not preclude running multiple DataNodes on the same machine but in a real deployment that is rarely the case. The existence of a single NameNode in a cluster greatly simplifies the architecture of the system. The NameNode is the arbitrator and repository for all HDFS metadata. The system is designed in such a way that user data never flows through the NameNode. 4. The File System Namespace: HDFS supports a traditional hierarchical file organization. A user or an application can create directories and store files inside these directories. The file system namespace hierarchy is similar to most other existing file systems; one can create and remove files, move a file from one directory to another, or rename a file. HDFS does not yet implement user quotas or access permissions. HDFS does not support hard links or soft links. However, the HDFS architecture does not preclude implementing these features. The NameNode maintains the file system namespace. Any change to the file system namespace or its properties is recorded by the NameNode. An application can specify the number of replicas of a file that should be maintained by HDFS. The number of copies of a file is called the replication factor of that file. This info is stored by NameNode. 5. Data Replication: HDFS is designed to reliably store very large files across machines in a large cluster. It stores each file as a sequence of blocks; all blocks in a file except the last block are the same size. The blocks of a file are replicated for fault tolerance. The block size and replication factor are configurable per file. An application can specify the number of replicas of a file. The replication factor can be specified at file creation time and can be changed later. Files in HDFS are write-once and have strictly one writer at any time. The NameNode makes all decisions regarding replication of blocks. It periodically receives a Heartbeat and a Blockreport from each of the DataNodes in the cluster. Receipt of a Heartbeat implies that the DataNode is functioning properly. A Blockreport contains a list of all blocks on a DataNode
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HCI Software Morae Recorder creates a complete chronicle of the events that occur behind the scenes in applications and the operating system, as well as the onscreen and keyboard activity of the user. Morae Observer provides support for one computer to connect over a network to another computer running Recorder, allowing the usability team to observe the screen and camera video and hear the audio of the user, streaming from the Recorder source computer. These data streams are recorded in sync with video and audio of the user. Anyone logged into an Observer computer can log tasks and add markers during recording complete with text notes. Because Recorder runs silently in the background, it never disturbs the CS 321 user. Lesson One Interaction Design Page 11 Observer automatically saves and indexes the markers and tasks with the accompanying video and audio streams. The camera video, screen Within Morae Manager, a usability team can start new projects and edit existing projects, configure Recorder settings, open and analyze recordings, create graphs of the team’s analysis and metrics, and create a presentation video. The screen and video recordings collected by Recorder are automatically indexed, allowing for easy searches through recordings in Morae Manager. Manager allows the team to isolate tasks, important points in the video, add text notes, annotate with audio, quickly create video highlights to share and display the screen video
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Choral Reading    Choral reading is an oral literacy activity in which several readers read a selection in unison with the direction of a leader Choral reading was an important element of Greek drama Choral reading was used during the early history of schools because they didn’t have enough books
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Planning Choral Reading      Teachers can help young children respond to language rhythms by clapping or tapping to the rhythm Teachers may chant the rhyme and have the kids join in with the last line or last couple lines It is good to use a single selection with various choral methods so the children can learn to use various ways of expressing meanings After students have several experiences with various choral readings they can then plan their own choral readings When students have developed their understanding of chanting in unison they can move to longer selections
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A list of the different modes of opening a file: Modes Description r Opens a file for reading only. The file pointer is placed at the beginning of the file. This is the default mode. rb Opens a file for reading only in binary format. The file pointer is placed at the beginning of the file. This is the default mode. r+ Opens a file for both reading and writing. The file pointer will be at the beginning of the file. rb+ Opens a file for both reading and writing in binary format. The file pointer will be at the beginning of the file. w Opens a file for writing only. Overwrites the file if the file exists. If the file does not exist, creates a new file for writing. wb Opens a file for writing only in binary format. Overwrites the file if the file exists. If the file does not exist, creates a new file for writing. w+ Opens a file for both writing and reading. Overwrites the existing file if the file exists. If the file does not exist, creates a new file for reading and writing.
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EDC 601 Instructional Technologies Audio Software Media Player (Windows) - player for WAV, WMA, MID, and many other audio file types. Sound Recorder (Windows) - player, recorder, and limited editor for WAV files. (Only a recorder with Vista.) Goldwave (www.goldwave.com) - editor, player, recorder, and converter for numerous audio file types. Audactiy (audacity.sourceforge.net) - editor, player, recorder, and converter for numerous audio file types. © Anthony J. Nowakowski, Ph.D.
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      Two-Way Tables (a) When no music was playing, the distribution of wine purchases was 30 11 43 French : =0.357 Italian : =0.131 Other : =0.512 84 84 84 When French accordion music was playing, the distribution of wine purchases was 39 1 35 French : =0.520 Italian : =0.013 Other : =0.467 75 75 75 When Italian string music was playing, the distribution of wine purchases was 30 19 35 French : =0.357 Italian : =0.226 Other : =0.417 84 84 84 The type of wine that customers buy seems to differ considerably across the three music treatments. Sales of Italian wine are very low (1.3%) when French music is playing but are higher when Italian music (22.6%) or no music (13.1%) is playing. French wine appears popular in this market, selling well under all music conditions but notably better when French music is playing. For all three music treatments, the percent of Other wine purchases was similar. 8
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      Two-Way Tables 6 (a) When no music was playing, the distribution of wine purchases was 30 11 43 French : =0.357 Italian : =0.131 Other : =0.512 84 84 84 When French accordion music was playing, the distribution of wine purchases was 39 1 35 French : =0.520 Italian : =0.013 Other : =0.467 75 75 75 When Italian string music was playing, the distribution of wine purchases was 30 19 35 French : =0.357 Italian : =0.226 Other : =0.417 84 84 84 The type of wine that customers buy seems to differ considerably across the three music treatments. Sales of Italian wine are very low (1.3%) when French music is playing but are higher when Italian music (22.6%) or no music (13.1%) is playing. French wine appears popular in this market, selling well under all music conditions but notably better when French music is playing. For all three music treatments, the percent of Other wine purchases was similar.
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6. The Persistence of File System Metadata: The HDFS namespace is stored by the NameNode. The NameNode uses a transaction log called the EditLog to persistently record every change that occurs to file system metadata. For example, creating a new file in HDFS causes the NameNode to insert a record into the EditLog indicating this. Similarly, changing the replication factor of a file causes a new record to be inserted into the EditLog. The NameNode uses a file in its local host OS file system to store the EditLog. The entire file system namespace, including the mapping of blocks to files and file system properties, is stored in a file called the FsImage. The FsImage is stored as a file in the NameNode’s local file system too. The NameNode keeps an image of the entire file system namespace and file Blockmap in memory. This key metadata item is designed to be compact, such that a NameNode with 4 GB of RAM is plenty to support a huge number of files and directories. When the NameNode starts up, it reads the FsImage and EditLog from disk, applies all the transactions from the EditLog to the in-memory representation of the FsImage, and flushes out this new version into a new FsImage on disk. It can then truncate the old EditLog because its transactions have been applied to the persistent FsImage. This process is called a checkpoint. In the current implementation, a checkpoint only occurs when the NameNode starts up. Work is in progress to support periodic checkpointing in the near future. The DataNode stores HDFS data in files in its local file system. The DataNode has no knowledge about HDFS files. It stores each block of HDFS data in a separate file in its local file system. The DataNode does not create all files in the same directory. Instead, it uses a heuristic to determine the optimal number of files per directory and creates subdirectories appropriately. It is not optimal to create all local files in the same directory because the local file system might not be able to efficiently support a huge number of files in a single directory. When a DataNode starts up, it scans through its local file system, generates a list of all HDFS data blocks that correspond to each of these local files and sends this report to the NameNode: this is the Blockreport. 7. The Communication Protocols: All HDFS communication protocols are layered on top of the TCP/IP protocol. A client establishes a connection to a configurable TCP port on the NameNode machine. It talks the ClientProtocol with the NameNode. The DataNodes talk to the NameNode using the DataNode Protocol. A Remote Procedure Call (RPC) abstraction wraps both the Client Protocol and the DataNode Protocol. By design, the NameNode never initiates any RPCs. Instead, it only responds to RPC requests issued by DataNodes or clients. 8. Robustness: The primary objective of HDFS is to store data reliably even in the presence of failures. The three common types of failures are NameNode failures, DataNode failures and network partitions. 8.1. Data Disk Failure, Heartbeats and Re-Replication: Each DataNode sends a Heartbeat message to the NameNode periodically. A network partition can cause a subset of DataNodes to lose connectivity with the NameNode. The NameNode detects this condition by the absence of a Heartbeat message. The NameNode marks DataNodes without recent Heartbeats as dead and does not forward any new IO requests to them. Any data that was registered to a dead DataNode is not available to HDFS any more. DataNode death may cause the replication factor of some blocks to fall below their specified value. The NameNode constantly tracks which blocks need to be replicated and initiates replication whenever necessary. The necessity for re-replication may arise due to many reasons: a DataNode may become unavailable, a replica may become corrupted, a hard disk on a DataNode may fail, or the replication factor of a file may be increased. 8.2. Cluster Rebalancing: HDFS arch is compatible with data rebalancing . A scheme might automatically move data from 1 DataNode to another if the free space on a DataNode falls below a certain threshold. In the event of a sudden high demand for a particular file, a scheme might dynamically create additional replicas and rebalance other data in the cluster. These types of data rebalancing schemes are not yet implemented. 8.3. Data Integrity: It is possible that a block of data fetched from a DataNode arrives corrupted. This corruption can occur because of faults in a storage device, network faults, or buggy software. The HDFS client software implements checksum checking on the contents of HDFS files. When a client creates an HDFS file, it computes a checksum of each block of the file and stores these checksums in a separate hidden file in the same HDFS namespace. When a client retrieves file contents it verifies that the data it received from each DataNode matches the checksum stored in the associated checksum file. If not, then the client can opt to retrieve that block from another DataNode that has a replica of that block.
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Recognition – SEM Taskforce RESEARCH Timekeeper: Sharon Parker Recorder: Nick Hamel Blog Oversight: Shelly Parini Facilitator: Judy Redder Additional Members: Terry Mackey  Recommendations: #2 and #12 TEACHING AND LEARNING Timekeeper: Jim Stekelberg Recorder: Becky Ogden/Kate Constable Blog Oversight: Doug Cross Facilitator: Renee Harber Additional Members: Margaret Mallat, Kathy Christiansen, Paul Creighton Recommendations: #6, #11 and #13  ACCESS Timekeeper: Aulani Wehage Recorder: Michael Vu/Mindy Brown Blog Oversight: Darcie Iven Facilitator: Jessica Walters Additional Members: Stefan Baratto, Armondo Borboa, Tara Davisson, Len Eaton, David Mount Recommendations: #15, #16, #17, #20  ENROLLMENT Timekeeper: Janet Paulson Recorder: MollyWilliams Blog Oversight: Mike Caudle Facilitator: Mike Caudle Additional Members: Joe Austin, Jackie Flowers, Donna Ford, Fayne Griffiths, Sheyl Sinclair, Student Recommendations: #1, #10, #14 and #10  PROCESS IMPROVEMENT Timekeeper: Dena Gillenwater Recorder: Tara Sprehe Blog Oversight: Kim Carey Facilitator: Pam Clem Additional Members: Ariane Amstutz, Stephen Browers, Kim Hyatt, Cheryl Willemse
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Producing Class Materials on Cassette Tapes •Need a blank audiotape, a tape recorder, a microphone • Prepare class materials to need to recorder Attention: • Maintain a constant distance from the microphone • Speak over the top of the microphone, not directly into it • If you make an error while recorder, stop the tape recorder, reverse it, and then continue recorder.
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Types of Samples LO3 Probability Probability Samples Samples Non-Probability Non-Probability Samples Samples Simple SimpleRandom Random Sample Sample Convenience Convenience Sample Sample Stratified Stratified Sample Sample Judgment Judgment Sample Sample Cluster Cluster Sample Sample Quota Quota Sample Sample Systematic Systematic Sample Sample Snowball Snowball Sample Sample 33
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Using the Chi-Square Table* H0: There is no difference in the distributions of wine purchases at this store when no music, French accordion music, or Italian string music is played. Ha: There is a difference in the distributions of wine purchases at this store when no music, French accordion music, or Italian string music is played. Our calculated test statistic is χ2 = 18.28. To find the P-value using a chi-square table look in the df = (3-1)(3-1) = 4. P df .0025 .001 4 16.42 18.47 The small P-value (between 0.001 and 0.0025) gives us convincing evidence to reject H00 and conclude that there is a difference in the distributions of wine purchases at this store when no music, French accordion music, or Italian string music is played. 20
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Using the Chi-Square Table* 17 H0: There is no difference in the distributions of wine purchases at this store when no music, French accordion music, or Italian string music is played. Ha: There is a difference in the distributions of wine purchases at this store when no music, French accordion music, or Italian string music is played. Our calculated test statistic is χ2 = 18.28. To find the P-value using a chi-square table look in the df = (3-1)(3-1) = 4. P df .0025 .001 4 16.42 18.47 The small P-value (between 0.001 and 0.0025) gives us convincing evidence to reject H00 and conclude that there is a difference in the distributions of wine purchases at this store when no music, French accordion music, or Italian string music is played.
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Using the Chi-Square Table* 20 H0: There is no difference in the distributions of wine purchases at this store when no music, French accordion music, or Italian string music is played. Ha: There is a difference in the distributions of wine purchases at this store when no music, French accordion music, or Italian string music is played. Our calculated test statistic is χ2 = 18.28. To find the P-value using a chi-square table look in the df = (3-1)(3-1) = 4. P df .0025 .001 4 16.42 18.47 The small P-value (between 0.001 and 0.0025) gives us convincing evidence to reject H00 and conclude that there is a difference in the distributions of wine purchases at this store when no music, French accordion music, or Italian string music is played.
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//******************************************************************** // Tunes.java Java Foundations // // Demonstrates the use of an array of objects. //******************************************************************** public class Tunes { //----------------------------------------------------------------// Creates a CDCollection object and adds some CDs to it. Prints // reports on the status of the collection. //----------------------------------------------------------------public static void main (String[] args) { CDCollection music = new CDCollection (); music.addCD("Storm Front", "Billy Joel", 14.95, 10); music.addCD("Come On Over", "Shania Twain", 14.95, 16); music.addCD("Soundtrack", "Les Miserables", 17.95, 33); music.addCD("Graceland", "Paul Simon", 13.90, 11); System.out.println(music); music.addCD("Double Live", "Garth Brooks", 19.99, 26); music.addCD("Greatest Hits", "Jimmy Buffet", 15.95, 13); System.out.println(music); } } Code\chap7\Tunes.java Java Foundations, 3rd Edition, Lewis/DePasquale/Chase 7 - 23
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SATIE’S MUSIC • A new kind of music, neoclassicism that went against the time periods romantic music • After Satie’s death neoclassicism was embraced as the music of the time period • Satie was an artist who dabble in many different ideas such as Cubism, Dadaism, and Surrealism and he also started to intertwine composing music with other art mediums such as plays (Fung 2009). • He started to gain fame when his precursors played his music before their own concerts • Satie wanted to compose music that went back to classical music with traditionally themes to inspire a sense of calm and peace. His music tended to be very short piano pieces where he used
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