Statistical power lays a foundation for a successful clinical trial, thus affecting all clinical trial professionals. Underpowered studies have a higher risk of not showing a statistically significant effect at the end of the study; whereas overpowered studies can lead to unreasonably large sample sizes, unnecessary risk to patients, and added expense. This webinar will address the basics of statistical power for non-statisticians, highlighting what you need to know about statistical power, how it affects your clinical trial, and what to ask for from your statistician.
Researchers, as a whole, tend to underestimate the need for power. I'm just now starting to get it.
I recently gave a brief, easy-to-follow presentation on statistical power, it's importance, and how to go about getting it.
Hope you find it useful.
Ijaems apr-2016-23 Study of Pruning Techniques to Predict Efficient Business ...INFOGAIN PUBLICATION
The shopping mall domain is a dynamic and unpredictable environment. Traditional techniques such as fundamental and technical analysis can provide investors with some tools for managing their shops and predicting their business growth. However, these techniques cannot discover all the possible relations between business growth and thus, there is a need for a different approach that will provide a deeper kind of analysis. Data mining can be used extensively in the shopping malls and help to increase business growth. Therefore, there is a need to find a perfect solution or an algorithm to work with this kind of environment. So we are going to study few methods of pruning with decision tree. Finally, we prove and make use of the Cost based pruning method to obtain an objective evaluation of the tendency to over prune or under prune observed in each method.
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
Statistics is a critical tool for robustness analysis, measurement system error analysis, test data analysis, probabilistic risk assessment, and many other fields in the engineering world. These basic applications are related to our basic engineering problems which help us to solve the problems and gives us the better solution and brings the efficiency to work with our real life engineering problems.
This presentation slide explains the procedure of creating a data set in the IBM SPSS statistics and also explains the way to import the excel data set into the SPSS.
Homework 1
Introduction to Statistics
Be sure you have reviewed this module/week’s lesson and presentations before proceeding to the homework exercises. Number all responses. Review the “Homework Instructions: General” document for an example of how homework assignments must look.
Homework 1 does not include any SPSS output and consists only of Part I.
Part I: Concepts
These questions cover the Nolan and Heinzen reading and end-of-chapter questions as well as basic SPSS functions covered in this module/week’s Green and Salkind reading and presentations.
Homework 1
Introduction to Statistics
Be sure you have reviewed this module/week’s lesson and presentations before proceeding to the homework exercises. Number all responses. Review the “Homework Instructions: General” document for an example of how homework assignments must look.
Homework 1 does not include any SPSS output and consists only of Part I.
Week 2 Project - STAT 3001Student Name Type your name here.docxcockekeshia
Week 2 Project - STAT 3001
Student Name: <Type your name here>
Date: <Enter the date on which you began working on this assignment.>
Instructions: To complete this project, you will need the following materials:
· STATDISK User Manual (found in the classroom in DocSharing)
· Access to the Internet to download the STATDISK program.
This assignment is worth a total of 60 points.
Part I. Histograms and Frequency Tables
Instructions
Answers
1. Open the file Diamonds using menu option Datasets and then Elementary Stats, 9th Edition. This file contains some information about diamonds. What are the names of the variables in this file?
2. Create a histogram for the depth of the diamonds using the Auto-fit option. Paste the chart here. Once your histogram displays, click Turn on Labels to get the height of the bars.
3. Using the information in the above histogram, complete this table. Be sure to include frequency, relative frequency, and cumulative frequency.
Depth
Frequency
Relative Frequency
Cumulative Frequency
57-58.9
59-60.9
61-62.9
63-64.9
a. Using the frequency table above, how many of the diamonds have a depth of 60.9 or less? How do you know?
b. Using the frequency table above, how many of the diamonds have a depth between 59 and 62.9? Show your work.
c. What percent of the diamonds have a depth of 61 or more?
Part II. Comparing Datasets
Instructions
Answers
1. Create a boxplot that compares the color and clarity of the diamonds. Paste it here.
2. Describe the similarities and differences in the data sets. Please be specific to the graph created.
Part III. Finding Descriptive Numbers
Instructions
Answers
3. Open the file named Stowaway (using Datasets and then Elementary Stats, 9th Edition). This gives information on the number of stowaways going west vs east.List all the variables in the dataset.
4. Find the Mean, median, and midrange for the Data in Column 1.
5. Find the Range, variance, and standard deviation for the first column.
6. List any values for the first column that you think may be outliers. Why do you think that?
[Hint: You may want to sort the data and look at the smallest and largest values.]
7. Find the Mean, median, and midrange for the data in Column 2.
8. Find the Range, variance, and standard deviation for the data in Column 2.
9. List any values for the second column that you think may be outliers. Why do you think that?
10. Find the five-number summary for the stowaways data in Columns 1 and 2. You will need to label each of the columns with an appropriate measure in the top row for clarity.
11. Compare number of stowaways going west and east using a boxplot of Columns 1 and 2. Paste your boxplot here
12. Create a histogram for the
Column 1 data and paste it here.
13. Create a histogram for the
Column 2 data and paste it here.
Part IV. Interpreting Statistical Information
The Stowaway data contains two columns, both of which are mea.
Statistical power lays a foundation for a successful clinical trial, thus affecting all clinical trial professionals. Underpowered studies have a higher risk of not showing a statistically significant effect at the end of the study; whereas overpowered studies can lead to unreasonably large sample sizes, unnecessary risk to patients, and added expense. This webinar will address the basics of statistical power for non-statisticians, highlighting what you need to know about statistical power, how it affects your clinical trial, and what to ask for from your statistician.
Researchers, as a whole, tend to underestimate the need for power. I'm just now starting to get it.
I recently gave a brief, easy-to-follow presentation on statistical power, it's importance, and how to go about getting it.
Hope you find it useful.
Ijaems apr-2016-23 Study of Pruning Techniques to Predict Efficient Business ...INFOGAIN PUBLICATION
The shopping mall domain is a dynamic and unpredictable environment. Traditional techniques such as fundamental and technical analysis can provide investors with some tools for managing their shops and predicting their business growth. However, these techniques cannot discover all the possible relations between business growth and thus, there is a need for a different approach that will provide a deeper kind of analysis. Data mining can be used extensively in the shopping malls and help to increase business growth. Therefore, there is a need to find a perfect solution or an algorithm to work with this kind of environment. So we are going to study few methods of pruning with decision tree. Finally, we prove and make use of the Cost based pruning method to obtain an objective evaluation of the tendency to over prune or under prune observed in each method.
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
Statistics is a critical tool for robustness analysis, measurement system error analysis, test data analysis, probabilistic risk assessment, and many other fields in the engineering world. These basic applications are related to our basic engineering problems which help us to solve the problems and gives us the better solution and brings the efficiency to work with our real life engineering problems.
This presentation slide explains the procedure of creating a data set in the IBM SPSS statistics and also explains the way to import the excel data set into the SPSS.
Homework 1
Introduction to Statistics
Be sure you have reviewed this module/week’s lesson and presentations before proceeding to the homework exercises. Number all responses. Review the “Homework Instructions: General” document for an example of how homework assignments must look.
Homework 1 does not include any SPSS output and consists only of Part I.
Part I: Concepts
These questions cover the Nolan and Heinzen reading and end-of-chapter questions as well as basic SPSS functions covered in this module/week’s Green and Salkind reading and presentations.
Homework 1
Introduction to Statistics
Be sure you have reviewed this module/week’s lesson and presentations before proceeding to the homework exercises. Number all responses. Review the “Homework Instructions: General” document for an example of how homework assignments must look.
Homework 1 does not include any SPSS output and consists only of Part I.
Week 2 Project - STAT 3001Student Name Type your name here.docxcockekeshia
Week 2 Project - STAT 3001
Student Name: <Type your name here>
Date: <Enter the date on which you began working on this assignment.>
Instructions: To complete this project, you will need the following materials:
· STATDISK User Manual (found in the classroom in DocSharing)
· Access to the Internet to download the STATDISK program.
This assignment is worth a total of 60 points.
Part I. Histograms and Frequency Tables
Instructions
Answers
1. Open the file Diamonds using menu option Datasets and then Elementary Stats, 9th Edition. This file contains some information about diamonds. What are the names of the variables in this file?
2. Create a histogram for the depth of the diamonds using the Auto-fit option. Paste the chart here. Once your histogram displays, click Turn on Labels to get the height of the bars.
3. Using the information in the above histogram, complete this table. Be sure to include frequency, relative frequency, and cumulative frequency.
Depth
Frequency
Relative Frequency
Cumulative Frequency
57-58.9
59-60.9
61-62.9
63-64.9
a. Using the frequency table above, how many of the diamonds have a depth of 60.9 or less? How do you know?
b. Using the frequency table above, how many of the diamonds have a depth between 59 and 62.9? Show your work.
c. What percent of the diamonds have a depth of 61 or more?
Part II. Comparing Datasets
Instructions
Answers
1. Create a boxplot that compares the color and clarity of the diamonds. Paste it here.
2. Describe the similarities and differences in the data sets. Please be specific to the graph created.
Part III. Finding Descriptive Numbers
Instructions
Answers
3. Open the file named Stowaway (using Datasets and then Elementary Stats, 9th Edition). This gives information on the number of stowaways going west vs east.List all the variables in the dataset.
4. Find the Mean, median, and midrange for the Data in Column 1.
5. Find the Range, variance, and standard deviation for the first column.
6. List any values for the first column that you think may be outliers. Why do you think that?
[Hint: You may want to sort the data and look at the smallest and largest values.]
7. Find the Mean, median, and midrange for the data in Column 2.
8. Find the Range, variance, and standard deviation for the data in Column 2.
9. List any values for the second column that you think may be outliers. Why do you think that?
10. Find the five-number summary for the stowaways data in Columns 1 and 2. You will need to label each of the columns with an appropriate measure in the top row for clarity.
11. Compare number of stowaways going west and east using a boxplot of Columns 1 and 2. Paste your boxplot here
12. Create a histogram for the
Column 1 data and paste it here.
13. Create a histogram for the
Column 2 data and paste it here.
Part IV. Interpreting Statistical Information
The Stowaway data contains two columns, both of which are mea.
Machine learning session6(decision trees random forrest)Abhimanyu Dwivedi
Concepts include decision tree with its examples. Measures used for splitting in decision tree like gini index, entropy, information gain, pros and cons, validation. Basics of random forests with its example and uses.
The workshop is an overview of creating predictive models using R. An example data set will be used to demonstrate a typical workflow: data splitting, pre-processing, model tuning and evaluation. Several R packages will be shown along with the caret package which provides a unified interface to a large number of R’s modeling functions and enables parallel processing. Participants should have a basic understanding of R data structures and basic language elements (i.e. functions, classes, etc).
Strategic Sourcing Quantitative Decision-Making and Analytics Mo.docxcpatriciarpatricia
Strategic Sourcing
Quantitative Decision-Making and Analytics Module 1
Comprehensive Assignment (2/7)
Please submit along with your Excel file.
If you have forgotten the name of Excel formulas, please see the lecture note I distributed on 2/5.
1. What is the most likely outcome from BackOrderData case? How would incorporate the result into you inventory (purchasing/preparation) decision? Please offer a comparison before and after your decision is augmented through the result.
2. Please normalize following data first. (Use Standardize() formula)
a. X = 34, Mean = 45, STDev.P = 6
b. X = 678, Mean = 890, STDev.S = 60, n = 36, degree of freem = 35
3. What does the normalized value tell us (in question 2) about the probability of being smaller than the value?(hint: see visualization below.)
a. Hint: =Norm.S.Dist(NormalizedValue of a, True)
b. Hint: = T.Dist(NormalizedValue of b, df = 35, True)
4. Please review SafetyStock data. Each day, the company TTM receives same quantity of shipment from its supplier. However, every day, there will be uncertain number of items shipped DOA (dead on arrival, i.e., arrived damaged.) To prevent further loss occurring from the sales, TTM plans to keep some safety inventories in its warehouse. The data file is the historical record of DOA quantity during the past 13 months. Let us consider following questions.
1) Please find out probability of following number of DOA. (hint: PivotTable will help you save time.)
DOA Scenario
Probability of Number of No-show
0
1
2
3
4
5
2) Calculate the expected value (or the most likely DOA quantity in long-run). (hint: need to use SUMPRODUCT formula.)
3) Please use the expected value as the safety stock quantity in the same data. See how many losses in sales you could have prevented. (hint: may need to use IF formula.
IF((safety stock - DOA) > 0, DOA, safety stock)) since it is a different context, the logic is a little different than the class demo.)
Total Loss in Sales Due to DOA = 693
Total Prevented Loss when Safety Stock is Used =
5. Please open DistanceTraveled data and answer following questions.
1) (BONUS QUESTION) Please collect three group of random samples (each with size of 30) from the DistanceTraveled variable. What is the average of each sample? What is the average of all three averages? (hint: for sampling, the following data analytics module needs to be used. For further assistance, please see https://www.youtube.com/watch?v=5XrJcFmbpWI 1 min. 32 sec. You will need to use random sampling.)
Average of first sample of 30 =
Average of second sample of 30 =
Average of third sample of 30 =
Average of all three averages above =
2) Calculate the actual average of DistanceTraveled variable (for all 365 observations). Then compare it with the result in #(1). Which result in #5 is in the closest range with the actual mean? Intuitively, which one should be the closest one? Please share your thoughts. Any thoughts/reasonings is .
About Your Signature Assignment.docxAbout Your Signature Assig.docxransayo
About Your Signature Assignment.docx
About Your Signature Assignment – Due Monday, 20-November
This signature assignment is designed to align with specific program student learning outcome(s) in your program. Program Student Learning Outcomes are broad statements that describe what students should know and be able to do upon completion of their degree. The signature assignments might be graded with an automated rubric that allows the University to collect data that can be aggregated across a location or college/school and used for program improvements.
Purpose of Assignment
The purpose of this assignment is for students to synthesize the concepts learned throughout the course. This assignment will provide students an opportunity to build critical thinking skills, develop businesses and organizations, and solve problems requiring data by compiling all pertinent information into one report.
Assignment Steps
Resources: Microsoft Excel®, Signature Assignment Databases, Signature Assignment Options, Part 3: Inferential Statistics
Scenario: Upon successful completion of the MBA program, say you work in the analytics department for a consulting company. Your assignment is to analyze one of the following databases:
· Manufacturing
· Hospital
· Consumer Food
· Financial
Select one of the databases based on the information in the Signature Assignment Options.
Provide a 1,600-word detailed, statistical report including the following:
· Explain the context of the case
· Provide a research foundation for the topic
· Present graphs
· Explain outliers
· Prepare calculations
· Conduct hypotheses tests
· Discuss inferences you have made from the results
This assignment is broken down into four parts:
· Part 1 - Preliminary Analysis
· Part 2 - Examination of Descriptive Statistics
· Part 3 - Examination of Inferential Statistics
· Part 4 - Conclusion/Recommendations
Part 1 - Preliminary Analysis (3-4 paragraphs)
Generally, as a statistics consultant, you will be given a problem and data. At times, you may have to gather additional data. For this assignment, assume all the data is already gathered for you.
State the objective:
· What are the questions you are trying to address?
Describe the population in the study clearly and in sufficient detail:
· What is the sample?
Discuss the types of data and variables:
· Are the data quantitative or qualitative?
· What are levels of measurement for the data?
Part 2 - Descriptive Statistics (3-4 paragraphs)
Examine the given data.
Present the descriptive statistics (mean, median, mode, range, standard deviation, variance, CV, and five-number summary).
Identify any outliers in the data.
Present any graphs or charts you think are appropriate for the data.
Note: Ideally, we want to assess the conditions of normality too. However, for the purpose of this exercise, assume data is drawn from normal populations.
Part 3 - Inferential Statistics (2-3 paragraphs)
Use the Part 3: Inferential Statistics document.
· Create (f.
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ACE 427 Homework 1
Spring 2016
Homework 1
The purpose of this homework is to introduce you to one of the most important concepts in
commodity price analysis—the random walk model. To obtain the data for the homework, go to
the farmdoc website and collect the US monthly average price received for hogs from January
1960 – November 2015. A downloadable Excel file with the data can be found at this link:
http://www.farmdoc.illinois.edu/manage/uspricehistory/us_price_history.html. The link for the
price data is at the bottom of the purple area of the tool.
1. Produce a line plot of the entire data series on one page. Do your best to format the chart
in a useful manner.
2. Produce a scatter plot where x is the previous month hog price and y is the current month
hog price. Note you will lose one observation when you construct the series for this plot.
In other words, generate a second price column which is the original data lagged by one
month, e.g. row #1 Feb 60 Jan 60; row #2 Mar 60 Feb 60, and so on. Show the
regression of y on x on the chart along with the equation and R2.
3. Generate the monthly change in hog prices. Simply subtract last month’s price from this
month’s price.
4. Produce a scatter plot where x is the previous change in the monthly hog price and y is
the current change in the monthly hog price. Note you will lose two observations when
you construct the series for this plot. Show the regression of y on x on the chart along
with the equation and R2.
5. Discussion:
a. What does the plot in #2 suggest about the predictability of monthly hog prices?
b. What does the plot in #4 suggest about the predictability of monthly hog prices?
c. How can the two predictability results be reconciled? Do some digging on the
random walk model. One well written paragraph is sufficient.
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PSYC 354 Homework 5
Z-Scores
Questions 1–9
Part I: Concepts
These questions are based on the Nolan and Heinzen reading and end-of-chapter questions.
What are always the mean and standard deviation of the z-distribution? (2 pts)
2) Define the central limit theorem. (1 pt)
3)
Fill in the blanks (1 pt): A z-score is based on a distribution of equally likely events, while a z- statistic is based on a distribution of sample percentage or average.
Part I: Questions 4-8
Module 5 Lesson 21 Exercise File 1
Part II: SPSS Analysis
Open the “Lesson 21 Exercise File 1” document (found in the course’s Assignment Instructions folder) in order to complete these exercises.
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RMD24 | Debunking the non-endemic revenue myth Marvin Vacquier Droop | First ...BBPMedia1
Marvin neemt je in deze presentatie mee in de voordelen van non-endemic advertising op retail media netwerken. Hij brengt ook de uitdagingen in beeld die de markt op dit moment heeft op het gebied van retail media voor niet-leveranciers.
Retail media wordt gezien als het nieuwe advertising-medium en ook mediabureaus richten massaal retail media-afdelingen op. Merken die niet in de betreffende winkel liggen staan ook nog niet in de rij om op de retail media netwerken te adverteren. Marvin belicht de uitdagingen die er zijn om echt aansluiting te vinden op die markt van non-endemic advertising.
Explore our most comprehensive guide on lookback analysis at SafePaaS, covering access governance and how it can transform modern ERP audits. Browse now!
Discover the innovative and creative projects that highlight my journey throu...dylandmeas
Discover the innovative and creative projects that highlight my journey through Full Sail University. Below, you’ll find a collection of my work showcasing my skills and expertise in digital marketing, event planning, and media production.
India Orthopedic Devices Market: Unlocking Growth Secrets, Trends and Develop...Kumar Satyam
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Enterprise Excellence is Inclusive Excellence.pdfKaiNexus
Enterprise excellence and inclusive excellence are closely linked, and real-world challenges have shown that both are essential to the success of any organization. To achieve enterprise excellence, organizations must focus on improving their operations and processes while creating an inclusive environment that engages everyone. In this interactive session, the facilitator will highlight commonly established business practices and how they limit our ability to engage everyone every day. More importantly, though, participants will likely gain increased awareness of what we can do differently to maximize enterprise excellence through deliberate inclusion.
What is Enterprise Excellence?
Enterprise Excellence is a holistic approach that's aimed at achieving world-class performance across all aspects of the organization.
What might I learn?
A way to engage all in creating Inclusive Excellence. Lessons from the US military and their parallels to the story of Harry Potter. How belt systems and CI teams can destroy inclusive practices. How leadership language invites people to the party. There are three things leaders can do to engage everyone every day: maximizing psychological safety to create environments where folks learn, contribute, and challenge the status quo.
Who might benefit? Anyone and everyone leading folks from the shop floor to top floor.
Dr. William Harvey is a seasoned Operations Leader with extensive experience in chemical processing, manufacturing, and operations management. At Michelman, he currently oversees multiple sites, leading teams in strategic planning and coaching/practicing continuous improvement. William is set to start his eighth year of teaching at the University of Cincinnati where he teaches marketing, finance, and management. William holds various certifications in change management, quality, leadership, operational excellence, team building, and DiSC, among others.
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"𝐄𝐯𝐞𝐫𝐲 𝐞𝐯𝐞𝐧𝐭 𝐢𝐬 𝐚 𝐬𝐭𝐨𝐫𝐲, 𝐚 𝐬𝐩𝐞𝐜𝐢𝐚𝐥 𝐣𝐨𝐮𝐫𝐧𝐞𝐲. 𝐖𝐞 𝐚𝐥𝐰𝐚𝐲𝐬 𝐛𝐞𝐥𝐢𝐞𝐯𝐞 𝐭𝐡𝐚𝐭 𝐬𝐡𝐨𝐫𝐭𝐥𝐲 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐛𝐞 𝐚 𝐩𝐚𝐫𝐭 𝐨𝐟 𝐨𝐮𝐫 𝐬𝐭𝐨𝐫𝐢𝐞𝐬."
Business Valuation Principles for EntrepreneursBen Wann
This insightful presentation is designed to equip entrepreneurs with the essential knowledge and tools needed to accurately value their businesses. Understanding business valuation is crucial for making informed decisions, whether you're seeking investment, planning to sell, or simply want to gauge your company's worth.
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It is crucial for the taxpayers to understand about the TDS Return Filing Due Date, so that they can fulfill your TDS obligations efficiently. Taxpayers can avoid penalties by sticking to the deadlines and by accurate filing of TDS. Timely filing of TDS will make sure about the availability of tax credits. You can also seek the professional guidance of experts like Legal Pillers for timely filing of the TDS Return.
Memorandum Of Association Constitution of Company.pptseri bangash
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A Memorandum of Association (MOA) is a legal document that outlines the fundamental principles and objectives upon which a company operates. It serves as the company's charter or constitution and defines the scope of its activities. Here's a detailed note on the MOA:
Contents of Memorandum of Association:
Name Clause: This clause states the name of the company, which should end with words like "Limited" or "Ltd." for a public limited company and "Private Limited" or "Pvt. Ltd." for a private limited company.
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Registered Office Clause: It specifies the location where the company's registered office is situated. This office is where all official communications and notices are sent.
Objective Clause: This clause delineates the main objectives for which the company is formed. It's important to define these objectives clearly, as the company cannot undertake activities beyond those mentioned in this clause.
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Liability Clause: It outlines the extent of liability of the company's members. In the case of companies limited by shares, the liability of members is limited to the amount unpaid on their shares. For companies limited by guarantee, members' liability is limited to the amount they undertake to contribute if the company is wound up.
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Capital Clause: This clause specifies the authorized capital of the company, i.e., the maximum amount of share capital the company is authorized to issue. It also mentions the division of this capital into shares and their respective nominal value.
Association Clause: It simply states that the subscribers wish to form a company and agree to become members of it, in accordance with the terms of the MOA.
Importance of Memorandum of Association:
Legal Requirement: The MOA is a legal requirement for the formation of a company. It must be filed with the Registrar of Companies during the incorporation process.
Constitutional Document: It serves as the company's constitutional document, defining its scope, powers, and limitations.
Protection of Members: It protects the interests of the company's members by clearly defining the objectives and limiting their liability.
External Communication: It provides clarity to external parties, such as investors, creditors, and regulatory authorities, regarding the company's objectives and powers.
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Binding Authority: The company and its members are bound by the provisions of the MOA. Any action taken beyond its scope may be considered ultra vires (beyond the powers) of the company and therefore void.
Amendment of MOA:
While the MOA lays down the company's fundamental principles, it is not entirely immutable. It can be amended, but only under specific circumstances and in compliance with legal procedures. Amendments typically require shareholder
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Cracking the Workplace Discipline Code Main.pptxWorkforce Group
Cultivating and maintaining discipline within teams is a critical differentiator for successful organisations.
Forward-thinking leaders and business managers understand the impact that discipline has on organisational success. A disciplined workforce operates with clarity, focus, and a shared understanding of expectations, ultimately driving better results, optimising productivity, and facilitating seamless collaboration.
Although discipline is not a one-size-fits-all approach, it can help create a work environment that encourages personal growth and accountability rather than solely relying on punitive measures.
In this deck, you will learn the significance of workplace discipline for organisational success. You’ll also learn
• Four (4) workplace discipline methods you should consider
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EDU 8006-8 Assignment
1. Why do we care whether the assumptions required for statistical tests are met? (You mightalso wantto write your
answer on a note card you paste to your computer.)
2. Open the data setthat you corrected previouslyfor DownloadFestival.sav.You will use the following variables:
Day1, Day2, and Day3 (hygiene variable for all three days). Create a simple histogram for each variable.Choose to
displaythe normal curve (under ElementProperties) and title your charts.Copy these plots into your document.
3. Now create probability-probability(p-p) plots for each variable. This output will give you additional information.
Read over the Case Processing Summary.Notice that there is missing data for Days 2 and Day 3? Copy only the
Normal p-p Plots into your document(you do not need to copy the beginning outputnor the Detrended Normal p-p
Plots).
4. Examining the histograms and p-p plots describe the dataset,with particular attention toward the assumption of
normality.For each day, do you think the responses are reasonablynormallydistributed? (Justgive your impression
of the data.) Why or why not?
5. Using the same dataset,and the Frequency command,calculate the standard descriptive measures (mean,
median,mode,standard deviation,variance and range) as well as kurtosis and skew for all three hygiene variables.
Paste your outputinto your document(you do not need to paste the Frequency Table). What does the output tell
you? You will need to commenton:sample size,measures ofcentral tendency and dispersion and well as kurtosis
and skewness.You will need to either calculate z scores for skewness and kurtosis or use those given in the book to
provide a complete answer.Bottom line:is the assumption ofnormalitymetfor these three variables? Does this
match your visual observations from question #1?
6. Using the datasetSPSSExam.sav, and the Frequency command,calculate:the standard descriptive statistics
(mean,median,mode,standard deviation,variance and range) plus skew and kurtosis,and histograms with the
normal curve on the following variables:Computer,Exam,Lecture, and Numeracyfor the entire dataset.Complete
the same analysis using Universityas a grouping variable.Paste your output into your document(you do not need to
paste the Frequency Table). What do the results tell you with regard to whether the data is normallydistributed?
7. Using the datasetSPSSExam.sav, determine whether the scores on computer literacyand percentage of lectures
attended (with University as a grouping variable) meetthe assumption ofhomogeneityof variance (use Levene’s
test). You mustremember to unclick the “splitfile” option used above before doing this test.What does the output tell
you? (Be as specific as possible.)
8. Describe the assumptions ofnormalityand homogeneityof variance. When these assumptions are violated,what
are your options? Are there cases in which the assumptions maytechnicallybe violated, yet have no impacton your
intended analyses? Explain