Project Management

 

Level 0Level 1Risk response matrixLevel 2Risk response matrixLevel 3Risk response matrixManchester United football clubRegistration for the season1.1Registration of the tem in the required league

Game time1.1.1

1.1.2Entry member showing

Maintain league regulation by limiting the number of players 1.1.1.1

1.1.1.2Amenities
1.2Football pitch

Seats for Fandom1.2.1

1.2.2Verify the quality of the field

Re-set Goal line Area

Re-set Corner Area

For dragging out enthusiasticMood1.2.1.1

1.2.1.2

1.2.1.3

1.2.2.1Winning Awards1.3Any trophy

Players rewards

Outstanding players for the tournament 1.3.1

1.3.2

1.3.3Most Valuable Player

Best Striker

Best goal keeper

Best performance in the midfield and defense1.3.3.1

1.3.3.2

1.3.3.3

1.3.3.4Sponsors1.4Company

Pre-match opening sequence1.4.1

1.4.2T-Shirts & football shoes

Advertising 1.4.1.1

1.4.1.2Match 1.5Match Referees

Match forecasting

Time keeping1.5.1

1.5.2

1.5.3unpredictable
Weather and climate1.5.2.1

Project Management
Level 0Level 1Level 2Level 3Manchester United football clubRegistration for the season 1.1Registration of the tem in the required league 1.1.1

Game time 1.1.2Entry member showing 1.1.1.1

Maintain league regulation by limiting the number of players 1.1.1.2Amenities 1.2Football pitch 1.2.1

Seats for Fandom 1.2.2Verify the quality of the field 1.2.1.1

Re-set Goal line Area 1.2.1.2

Re-set Corner Area 1.2.2.1
For creating an exciting mood 1.2.1.3
Winning Awards 1.3Any trophy 1.3.1

Players rewards 1.3.2

Outstanding players for the tournament 1.3.3Most Valuable Player 1.3.3.1

Best Striker 1.3.3.2

Best goal keeper 1.3.3.3

Best performance in the midfield and defense 1.3.3.4Sponsors 1.4Company 1.4.1

Pre-match opening sequence 1.4.2T-Shirts & football shoes 1.4.1.1

Advertising 1.4.1.2Match 1.5Match Referees 1.5.1

Match forecasting 1.5.2

Time keeping 1.5.3unpredictable
Weather and climate 1.5.2.1

Please chose one of the following tables that you think suits you best

Running head: PROJECT MANAGEMENT 1

Discussions

 

Part One
Q1. Obstacles of a Project-Oriented Organization
Project-based organizations are a range of organizational forms, which involves creation of some temporary systems with the aim of improving project tasks’ performance (Sydow et al 2004, p. 1475). In the modern turbulent markets, many firms seek strategic advantages over their rivals with most resulting to project-based organization (PBO). However, there loom the questions concerning how an organization will adopt it in creation of a synergy between the firm business mission, portfolio and project management, and strategy. Some firms lack the knowledge about how the PBO function practically and the benefits and value underlying adoption of PBO practice. Another challenge is the lack of ownership of a PBO approach by an organization’s top leadership and management (Ajmal 2009, p. 1).

Q2. Important things for Accomplishment of Transition
For more efficient and effective deployment of project-based management, organizations require engaging top management and leadership to recognize the new PBO approach of clinching best practices of project management. Thus, the organization must establish executive responsibility and ownership for the project-based management to succeed. Another important factor is identification of the responsibilities, roles, and function of each organizational player in a project-based organization to enhance ownership and contribution towards the project by the organizational members (Ajmal 2009, p. 1). These initiatives will facilitate the reaping of the full benefits and values of the new competitive and formidable strategic weapon and thus enhance continuous growth of business as well as ensuring the firm gains better survival opportunity in the modern highly competitive market. The firms thus require engaging the appropriate attitudes across its system (Cattani et al 2011, p. xxiii).

Part 2
Q1. Effectiveness of Developing Project Managers Using Project Management Competency Frameworks
As the project management portfolio continues maturing, the modern day project managers face an array of sometimes-confusing certifications and standards on job training, performance criteria, skills and knowledge expertise measurement. The standards and certifications make it easy for project managers to understand the skills they need to develop, the knowledge management areas they require understanding, as well as the means of measuring their performance. Certifications and standards developed excellent project management candidates, who succeed in project performance (Gupta 2011, p. 115). Thus, it is necessary to encourage familiarization of project management standards by the project managers, as these are sufficient in fostering improved performance among them.

Q2. Should Program and Portfolio Managers’ Development Follow a Similar Process?
If an organization is interested in establishing a list of vital skills of project management, it should look at the standards of project management relevant to its projects or programs (Gupta 2011, p. 115). As such, there is a great need of the development of the program and portfolio managers to follow a similar path to ensure their effectiveness in their roles. In addition, these are essential competency frameworks structured after sufficient review and research and thus are vital for the program and portfolio managers’ development.

Q3. Pros and Cons of Project Managers Certification by Professional Organizations
A project management certification entails the awarding of a certificate to an individual after proving a given amount of experience as well as passing a test. These certifications meet three closely linked requirements. First, firms want to ensure their project managers can benchmark their knowledge, experience, as well as expertise in project management. Secondly, clients regularly need certified project managers to work on their projects. Finally, the project managers desire the recognition of their expertise, knowledge, and professional experience (Gupta 2011, p. 115).

Pros:
Project-oriented firms can create a culture of project management within themselves by having certified project managers. They also help in the improvement of return on investment of organizational projects and promote stakeholders satisfaction. Certified professionals improve efficiency and reduce delays. They also boost the organizational confidence in selling the services of project management to their end customers (PMC 2010, p. 4).

Cons:
The certification process is expensive and organizations lack the motivation of enrolling their project managers as they can always leave them. The preparation for certification is also time-consuming making organizations shy away from enrolling their project managers citing wastage of essential time for organizational development. As such, organizations prefer hiring trained and certified professionals, which may not augur well with the organizational members (PMC 2010, p. 4).

References

Ajmal, MM 2009, Managing knowledge in project-based organizations: a cultural perspective, Vaasa, Universitas Wasaensis.
Cattani, G, Ferriani S, Frederiksen L, & Taube, F 2011, Project-based organizing and strategic management, Bingley, Emerald.
Gupta, B L 2011, Competency framework for human resources management, New Delhi, Concept Pub. Co.
Project Management Certification (PMC) 2010, viewed 3 June 2012, http://www.sts.ch/documents/english/pm_certif.pdf
Sydow, J, Lindkvist L, & DeFillipi, R 2004, Project-based organizations, embeddedness and repositories of knowledge: editorial, Organization Studies, Vol. 25, No. 9, pp. 1475-1489.

 

Discussions 4

 

Contract Management Negotiations within supply chains

 

TOPIC IS Contract Management SPECIFIED IN Negotiation
Some main points could be :
1-Minimized Contractual Risk.
2-Best Value.
3-Performance of the terms specified in the contract.
4-Financial implications and penalties
5-Discover the Supplier’s Agenda.
6-Profile the Supplier’s Negotiating Team Personnel.
7-Review the Supplier’s Performance History.
8-Select and Prepare Your Negotiation Team
You also can talk about how the factors bellow affect the negotiation.
9-Political Factors
10-Economic Factors
11-Social Factors
12-Technological Factors

YOU DON’T HAVE TO USE ANY OF THE 12 IDEAS I GAVE YOU IF YOU HAVE SOME THING BETTER IN MIND.

You are required to write a white paper or complete a project report on a current supply chain topic of interest to the students.

Your report/paper should address a specific question related to supply chain management.

You are expected to make yourself an “expert” in the topic you choose.

The white paper should try to present “state-of-the-art” knowledge and best practices about the selected topic.

This project provides students with the opportunity to conduct in-depth research on a supply chain topic of particular interest.

Groups are encouraged to use contacts from internships, prior jobs, future jobs, or other networks to find meaningful projects that require analysis of real data from real companies facing supply chain challenges. The project report should be original. Please write the content in your own words. Whenever something is taken from a source, this source must be referenced appropriately. Full citation information is to be provided in the bibliography.

The final project report should be concise and well presented. Please pay careful attention to the organization and presentation of the report. While there is no minimum and maximum page length requirement, I recommend that the report be between – 15 double spaced pages (excluding references, appendices, tables and figures).

For the PowerPoint I need easy reading slides, I’m going to read out of the slides not the paper. I need the main important ideas that you came up with in the 15 page paper to be in the 20 slides.
I need A1 work please this project is worth a lot of points.

Media Censorship

Introduction
Project Censored is an initiative that deals with media research, education and advocacy. It has been in existence at the Sonoma State University since 1976. It is involved with the publication of news and stories that have been left out by other media sources. It works in cooperation with many media groups to train students on media research, First Amendment issues and other factors that help to protect the freedom of the press in USA. It is a partnership between the faculty, students, and community to do research on the critical national news that have either been underreported or have been ignored, misrepresented or censored by most of the corporate media houses. Some external forces that have some interest in a specific piece of news may cause this ignorance.

The training provided by Project Censored Initiative prepares journalist for the challenging media environment that they are to face. The initiative ranks the top 25 censored stories and publishes them in its yearbook. The current edition is the largest to have been produced and includes stories such as the junk food news and news abuse, Universal Healthcare, media, and the 2008 presidential campaign. It is imperative to note that project censored provides more detailed information on some of the important news than any other media house. This means that their book becomes a must-read for everyone even those familiar with the issues in order to gather more information.

Project Censored Definition of Modern Censorship
It is the constant and sophisticated manipulation of reality in the media’s role as the guardians of power. Most of them serve their single-minded search of profit. It involves ignoring vital news stories or parts of news and therefore, end up holding the truth from the public. This is because of the political, economic and to some extent, the legal pressures which fundamentally violates the right to a free and open press. Most of the media houses are by CEOs and not journalists as it should be the case. This means that these CEOs serve their own interests and those of the advertisers but not the interest of the public. The public’s interest is to be served with nothing but the truth when it comes to news.

Media Censorship in Practice
In recent years, most governments especially the US government have been at the forefront in applying pressure on the corporate media as well as monitoring the circulation of news over the social media. The news may be concerning the secrets that surrounds the government. No one is to broadcast any information that will reveal the truth about what the government does behind the scenes. For example, in 2012, it was revealed that the US government was using some unconstitutional and highly disturbing torture tactics in its war against terrorism. The news had been censored for a long time by the government with the fear that the citizens would protest against the actions of the government. A video was on YouTube but was removed after some time with allegation that it portrayed nudity, an allegation that was not true. Protestants went to the streets to demand the truth which led to the site owners (Google) reinstating the video for the viewers to see. Essential news, for instance the one discussed above is out by media owners since they have the close monitoring of the government, the same government that hides the information from the eyes of the public.

Healthcare issues are another area that has experienced massive censorship. In 2010, FDA was accused of censoring relevant information on nutritional cures that can be used to cure most of the major diseases such as arthritis. This information was censored with the aim of ensuring that the public remain ignorant about nutritional cures, which have received scientific proof. This ensures that drug manufacturers continue earning a lot from the sales of those drugs they manufacture. The media kept a deaf year to stories such as these due to economic pressures from those companies that stand to lose profit if such information gets to the ears of the public.

Effects of Media Censorship
Media censorship has many effects on the way the news are to the people. The media protect the privacy of people who are doing wrong things, which means that the public does not have a way of knowing the wrong doers in the society. In most cases, the media avoid the use of graphic details and images, which mean the information that reaches the public will be from the original story. The corporate media also conceal security information from the eyes of the public. Most of the government related factors such as The US military, intelligence and diplomatic operations are with confidentiality compared to other news. Some media have bias in their reporting, which is a factor of political affiliations to a certain individual or political party. The most critical part of media censorship is the part where they advance corporate interests through advertising. This means that most of them are business oriented compared to serving the interests of the public. They are by CEOs who have their main agenda as improving the profit margins of the company.

Why Project Censored is Significant
Democracy requires media that is free, open and diverse. The elements are absent in the corporate-dominated systems because the competition is about making money and not doing what is logically the work of a media house. Improvements can only be made if more competent journalists, who are free from manipulation by the government and any other force that may apply pressure to the journalists. Project Censored and other private organizations offer a platform that the public can access information that is both well researched and true. If the world has to talk of having a free media system, the media censorship is something that must be removed completely from the system. This can be done using training platforms like the one offered by the Project Censored Initiative. In addition, free media plays a role in ensuring civilization and observance of human rights, especially in developing countries.

Conclusion
Nothing called free media exists in the world today. His is because much of what the media houses broadcast is by other factors such as the pressure from the government and the corporate world. Many facts are out of the process of relaying information to the public, which implies that the information reaching the public is diluted from the original version of the story. The public is not in apposition to keep track of important facts since the real facts are not. It is only in other means that the public can benefit from efficient news and information.

Surname 1

 

Tsai-Chun, Yao (Fiona) Econ 310Results paper Draft2013/05/22

Empirical Results:
For this writing project concerning the relationships between home prices and home size and other characteristics in order to provide information about what new employees can expect to pay for a home and help new employees make appropriate offers on homes that they may be interested in purchasing.

I estimated two different regression models for two separate dependent variables: 15 single-family homes for sale in Shoreline (98133) and 60 single-family homes for sale around the North Boeing Company including Everett (98203), Bothell (98011), Shoreline (98133), and Mukilteo (98275). For each dependent variable, I estimated two different specifications.

The independent variables used in these regression models are (1) Price: the rice for which the home is offered for sale, (2) Bedrooms: the number of bedrooms in the home, (3) Bathrooms: the number of bathrooms in the home, (4) House FT2: the square footage of the home, (5) Lot FT2: the square footage of the lot, (6) Built Age: the year of 2013 minus the year of the home was built. Table 1 is the Regression result for 15 single-family homes for sale in Shoreline (98133), and it only shows the “Reduce Mode”. The regression line is Y-hat: 43296.66+6671.295(X1)+4130.694(X2)+65.58285(X3)-19.5075(X4)+2093.672(X5).Table 2 is the Regression result for 60 single-family homes for sale around the North Boeing Company and I use Zip Code to be my Dummy variable for my 60 single-family homes for sale. This table contains the “Full Model” and “Reduced Model”, and the regression line for “Full Model” is Y-hat: 814046.9-38396.13(X1)+13247.349(X2)+202.18185(X3)+1.1502909(X4)-346.9233(X5)-141942.3(X6)-45482.96(X7)-48806.05(X8). Also, the regression for “Reduced Model” is Y-hat: 53438.99-49831.5(X1)+39328.79(X2)+189.9316(X3)+1.406978(X4)+366.3751(X5).Table 1: Regression Results for 15 single-family homes for sale in Shoreline (98133)
Reduced Model
Table 2: Regression Results for 60 single-family homes for sale around North BoeingFull Model Reduced Model

Writing Project: Factors Affecting the Prices of Homes in the North Boeing Area

Introduction
Trout Company employs many people from the United States and the whole world in general. Most of the employees being employed have to relocate in order to ensure that they perform their best. Over time, it has emerged that the problem of finding a new home is a lot of trouble to the new employees. Earlier this year, for example, the personnel director asked me to conduct a research on various factors that overlap during relocation of employees with critical information they need at this time, including important factors that they will consider when moving.

This will make the task of looking for a new home easier by giving these new employees of the Trout company at least the average cost for purchasing a house, how big is the house, the number of bedrooms and bathrooms the house has and many more characteristics that can ease the daunting task of searching for a new home. Another fact that emerged is that these new employees prefer buying single homes to renting apartments. The main aim of the research is to determine the effect of size of the house to its price and other characteristics.

There is a conflict of hypothesis of Realtors on what determines the price of a home or house. The conventional way of thinking is that only the size of the house determines the price of the house. The price of a house depends on its age; there are many other factors such as the lot. I obtained data of about 60 houses in 4 areas in the Puget Sound area from the Zillow realtors’ website. In addition, I have summarised the data in the following table 1. This table has the descriptive statistics of all the variables that I thought to be important and included them in the model. I collected data for seven variables to address this topic. The first is the PRICE, which is the offered price for sale. The second is BEDROOMS, which represents the number of bedrooms in house.

The third variable is BATHROOMS, which is the number of bathrooms. The fourth is HOUSE FT2, which is the square footage of the home. The fifth variable is LOT FT2, which is the square footage of the lot. The sixth is YR BUILT, which represents the year the home was built. The last variable is the ZIP CODE, which is the zip code where the home is located.
Table 1 below shows the summary statistics of the sample data from the four areas from the website (Zillow):
CharacteristicMeanMedianModeSDMinMaxPRICE467616.3392500385000230352.7150000120000BED3.5330.9477928BATH2.59582.530.98192516.25HSE SQFT2373.78322201560994.84149005645LOT SQFT15162.42100181045423861.88871148633AGE39.0531.56627.600103YR BUILT19731981194727.5019102013The columns give a summary of the mean, median, mode, standard deviation (SD), minimum (Min) and maximum (Max). The standard deviation tries to explain the extent of Variation from the mean of the data.
The first row of table 1 suggests that the average price of a house is $467,616 with most houses having a price of $385,000. Yet, the minimum price of purchasing a house was $150,000 in the North Boeing area and a maximum of $ 1,200,000 from the North Boeing area. The standard deviation of the price variable is 230352.7

The second row of the table 1 suggests that the average number of bedrooms in the sample number of houses is 3.5, with most houses having 3 bedrooms. Houses with a minimum number of bedrooms are 2 in the North Boeing area and a maximum number of 8 bedrooms from the North Boeing area. The standard deviation of the BED variable is 0.95
The third row of the table 1 suggests that the average number of bathrooms in the sample number of houses is 2.5 with most houses having 3 bathrooms. The houses with a minimum number of bathrooms had just 1 bathroom in the North Boeing area. In addition, a maximum number of bathrooms that a house could have were 6.5 from the North Boeing area. The standard deviation of the BATH variable is 0.95

The fourth row of the table 1 suggests that the mean area in the sample number of houses in the North Boeing area is 2373.783 square feet. However, most houses have 1560 square feet under the houses. The houses with a minimum square footage and 900 square footages in the North Boeing area and a maximum area under the house are 5645 square feet. The standard deviation of the HSE SQFT variable is 994.84

The fifth row of the table 1 suggests that the mean square footage of the lot in the sample number of houses in the North Boeing area is 15162.42. However, most houses had 10454 square footage of the lot. The houses with a minimum square feet of 871 square feet in the North Boeing area and a maximum area under the lot is 148 633 square feet from the North Boeing area. The standard deviation of the LOT SQFT variable is 23861.88

The seventh row of the table 1 suggests that the Mean year, in which most of the houses were built, was in the year 1973. Yet, most of the houses in the area were built in the year 1947, the earliest houses to be built in the area were built in the year 1910 and some were built early 2013. The thirteenth and fourteenth columns of the table 1 suggest that the mean age of the houses is about 66 years, and maximum age is 103 years. The standard deviation of the YR BUILT variable is 27.60
This summary statistics can give the new employees an idea of what they have to do in order to be prepared for whatever comes on their ways in preparation for their relocation to their new homes from wherever. This can also enable them plan and budget well. We will further do more analysis.

This project investigates the relationship between the price of house, size, age, number of bedrooms, the house square footage, and Lot square footage. The results of the data analysis will give new employees who intend to move to the area an insight of the prices of the houses in relation to the mentioned variables.
I estimated two different regression models for two separate dependent variables: 15 single-family houses for sale in Shoreline (98133) and 60 single-family houses for sale around the North Boeing Company including Everett (98203), Bothell (98011), Shoreline (98133), and Mukilteo (98275)
I came up with a number of regression models to try to establish the relationship between the prices of the home, as a dependent variable. Independent variable being include BEDROOMS: the number of bedrooms in the home, BATHROOMS: the number of bathrooms in the home, HOUSE FT2: the square footage of the home, LOT FT2: the square footage of the lot, YR BUILT: the year the home was built and ZIP CODE: the zip code where the home is located.
Graph 1: Residual plot of the price variable (Differences between the predicted price and the actual price)

Relationship between the prices of a home with respect to the other independent variables in the model as the following two tables suggest.
Table 2
Regression StatisticsMultiple R0.853609R Square0.728649Adjusted R Square0.69793Standard Error126603.9Observations60ANOVA dfSSMSFSignificance FRegression62.28E+123.8E+1123.719822.08E-13Residual538.5E+111.6E+10Total593.13E+12   Table 3
CoefficientsStandard Errort StatP-valueIntercept-563251817043365-0.330480.742339BED-51714.625117.57-2.05890.044433BATH41654.3432157.241.2953330.200819HSE SQ FT189.051528.007616.7500021.14E-08LOT SQ FT1.4072740.7982751.7628930.083684YR BUILT-423.46822.2479-0.5150.608692ZIP CODE66.61595175.76540.3790050.706198The table 2 and 3 shows the regression results for the prices of houses. First, I performed a regression analysis on the price of a home on all the six variables that I had stated prior.

With a moderate R-Squared Adjusted at about 0.6979. From the table above it can be noted, that the intercept has a coefficient of -5632518. Number of bedrooms of each house has a coefficient of -51714.6 implying a negative relationship between dependent and independent variables (number of bedrooms). Number of bathrooms in the house has a coefficient of 41654.34. The house square footage has a coefficient of 189.0515. While lot square footage has a coefficient of 1.407274. The year the house was built has a coefficient of -423.46 and lastly the zip code had a coefficient of 66.61595. I intended to eliminate variables with low t-ratios and high p-values as they will be insignificant to the model at a 5% significance level. In this specification, the variable with the highest t-ratio was the independent variable concerning ZIP CODE, with t-ratio of 0.379005 respectively, from this analysis it can be assumed that the given variable might be insignificant to the model.

To confirm this topic further, I did an F test to find out if the assumption is valid with the null hypothesis stating that the variable is insignificant to the model and the alternative hypothesis that the variable is significant to the model. It came about, that the F value was so low almost 0 thereby implying that the variable can be dropped if we are considering an accuracy of 95 per cent. Secondly, I decided to make the model more simple and efficient. I decided to run further a regression analysis without the zip code of the area variable. I observed that, the value of the Adjusted R square increased to 0.70272, which increases the effectiveness of the prediction model, as shown in table 4 below.

Table 4
SUMMARY OUTPUTRegression StatisticsMultiple R0.853178R Square0.727913Adjusted R Square0.70272Standard Error125596Observations60ANOVA dfSSMSFRegression52.28E+124.56E+1128.89325Residual548.52E+111.58E+10Total593.13E+12   CoefficientsStandard Errort StatP-valueIntercept799415.715608880.5121540.610631BED-49800.924408.98-2.040270.046226BATH39293.6731297.111.2555050.214701HSE SQFT190.027327.6676.8683746.74E-09LOT SQFT1.4058470.7919121.7752580.081491YR BUILT-370.809803.9756-0.461220.646494From the table above it can be noted, that the intercept has a coefficient of 799415.7. Number of bedrooms of each house has a coefficient of -49800.9, implying a negative relationship between the dependent variable and the independent variable (number of bedrooms). Number of bathrooms in the house has a coefficient of 39293.67.

The house square footage has a coefficient of 190.0273. Lot square footage had a coefficient of 1.405847. Lastly, the year the house was built has a coefficient of -370.809.With their low t-ratios and high p-values, this independent variable might be insignificant to the model at a 5% significance level. The date of house construction was the variable with the highest t-ratio of -0.46122. Thus, I assumed that the variable might be insignificant to the model and performed an f-test to validate the assumption. It came about, that the F value was so low, almost 0, thereby implying that the variable can be dropped if we are considering an accuracy of 95 per cent.

Again, from the analysis in the tables above, it can be seen that the number of bedrooms a house has in the North Boeing area have a very low t- value statistic of about -2.40 with a p – value of approximately zero. Therefore, we can drop this variable from the prediction model. I did a regression analysis once more without the inclusion of the number of bedrooms variable each of the sample houses have and came up with the following table.
Table 5
SUMMARY OUTPUTRegression StatisticsMultiple R0.840774R Square0.706901Adjusted R Square0.691199Standard Error128006.6Observations60ANOVA dfSSMSFRegression32.21E+127.38E+1145.02059Residual569.18E+111.64E+10Total593.13E+12   CoefficientsSEt StatP-valueIntercept-7278.6450191.69-0.145020.885218BATH18060.7126006.040.6944810.490252HSE SQFT169.154326.125896.4745862.54E-08LOT SQFT1.7462010.786812.2193430.030531From table 5 we can see that the magnitude of the adjusted R square has reduced to 0.69 though it has reduced it is still sufficient to enable us come up with the prediction model. The intercept has a coefficient of -7278.64, the number of bathrooms a house has have a coefficient of 18060.71, the square footage had a coefficient of 169.1543 and the lot square footage had a coefficient of 1.75. We assume that the model is an additive one and as such, we can represent it as follows:
A table showing the results of the Regression analysis

 First analysis (1) Second analysis (2) Third analysis (3)Intercept-5632518.8 (-0.38)799415.7 (0.51)-7278.64 (-0.15)BED-51714.6 (-2.06)-49860.9 (-2.04) _____BATH41654 (1.30)39293 (1.26)18060.71 (0.69)HSE SQFT189.05 (6.75)190.02 (6.87)169.15 (6.50)LOT SQFT1.407(1.76)1.41 (1.78)1.75 (2.20)YR BUILT-423.46 (-0.52)-370.81 (-0.46)ZIP CODE66.62 (0.38) ** The values in the brackets show the respective t – statistic at 95% confidence level
Full model is given as follows:
Price = -5632518 – 51714.6(BED) + 41654.34(BATH) + 189.0515(HSE SQFT) + 1.41(LOT SQ FT) -423.46 (YR BUILT) +66.62 (ZIP CODE)
In the above full model I have used the number of bedrooms in the house; the year the house was built and Zip code of the area that the house is located are used as dummy variables. The reduced model without the dummy variables is given below.

Reduced model:
Price = -7278.64 + 18060.7(BATH) + 169.15(HSE SQFT) + 1.75(LOT SQ FT)
This is the final reduced model that I came up with which can be used to predict the prices of homes even in future.
Part II
In the next step I aimed to model house price for a choice area from the four options, and on doing random selection, I chose Shoreline area. I performed a regression analysis on the price on the remaining 5 variables, since it would not be wise to include the ZIP CODE variable, as it will be insignificant to the use of our prediction model. The variables I included in the regression analysis are: 1. BEDROOMS: the number of bedrooms in the home, 2. BATHROOMS: the number of bathrooms in the home, 3. HOUSE FT2: the square footage of the home, 4. LOT FT2: the square footage of the lot, 5. YR BUILT: the year the home was built. After the regression analysis, I came up with the following table.

Table 6
SUMMARY OUTPUTRegression StatisticsMultiple R0.776108R Square0.602343Adjusted R Square0.381422SE79250.52Observations15ANOVA dfSSMSFRegression58.56E+101.71E+102.726513Residual95.65E+106.28E+09Total141.42E+11   CoefficientsSEt StatP-valueIntercept425785919466492.1872760.056501BED66751.355653.31.1994130.26099BATH4130.69481175.710.0508860.960528HSE SQFT65.5828586.234040.7605220.466399LOT-19.50758.223907-2.372040.041769YR BUILT-2093.671022.99-2.046620.071001From the table 6 it can be noted, that the value of the adjusted R square is so small, that some variables have to be removed from the model to ensure that the adjusted R square is increased. From the model it is noted, that the intercept has a coefficient of 4257859. The number of bedrooms of each house has a coefficient of 66751.3, implying a positive relationship between the dependent and independent variable (number of bedrooms). Number of bathrooms in the house has a coefficient of 4130.694, while the house square footage has a coefficient of 65.58. Lot square footage has a coefficient of -19.51. The year the house was built has a coefficient of -2093.67.

Looking at the t statistics and the p values, we can determine which variables to eliminate and which to retain. From table 6 above, we can see that the independent variables with the least t statistic values are the lot square footage and the year the houses were built. We can exclude them from the model of predicting the price of a home from the shoreline area. We can assume that their contribution towards the final price of a home is so small and as such, insignificant to the model. I performed an f test to see if the lot square footage and the year of construction. In the null hypothesis, I stated that these two and in the alternative hypothesis, that at least one of the variables is significant to the model. The f statistic generated was so low and as such, lot square footage and construction year are insignificant, hence, they are to be dropped from the final model. A regression analysis of the other independent variable yielded the results depicted in table 7 below.

Table 7
SUMMARY OUTPUTRegression StatisticsMultiple R0.449972R Square0.202474Adjusted R Square-0.01503SE101518.5Observations15
ANOVA dfSSMSFSignificance FRegression32.88E+109.59E+090.9308870.458269Residual111.13E+111.03E+10Total141.42E+11    CoefficientsSEt StatP-valueIntercept255913.2123534.42.0715950.0626BED16918.8567976.950.2488910.808031BATH-40447.275687.25-0.53440.603698HSE SQFT82.1837287.682060.9372920.368731From table 7, it can be noted that the value of the adjusted R square is so small that some variables have to be removed from the model to ensure that the adjusted R square is increased.

However, if we remove more variables, the probability that the adjusted R square is going to decrease further, is high. Thus, the final model ought to be left at this point. Again, from the model, it is noted that the intercept has a coefficient of 255913.2. The number of bedrooms of each house has a coefficient of 16918.85 implying a positive relationship between the dependent variable and the independent variable (number of bedrooms). The number of bathrooms in the house has a coefficient of -40447.2, while the house square footage has a coefficient of 82.18.
The following table shows the results after the regression analysis

 First analysis (1)Second analysis (2)Intercept425789 (2.19)255913.2 (2.07)BED66751.3 (1.20)16918 (0.25)BATH4130.69 (0.05)-40447.2 (-0.53)HSE SQFT65.58 (0.76)82.18 (0.94)LOT-19.51 (-2.37) _________________YR BUILT-2093.67 (-2.05) ____________________** The values in braces show the respective t values of the characteristic.
The full model is given below.
Price = 4257859 + 66751.3(BED) + 4130.69(BATH) + 65.58(HSE SQFT) – 19.51(LOTSQFT) -2093.67 (YR BUILT)
In the above full model, the variables used as dummy variables are the square footage of the lot and the year the house was built.
Therefore, the final reduced predictive model for the prices at the shoreline area is as below.
Reduced model
Price = 255913.2 + 16918.85(Bed) -40447(BATH) + 82.18(HSE SQFT)
From both the two regression models that we have come up with there are some similarities and some differences.

CONCLUSION
The research should also be extended to the whole country to get a clearer picture of what determines the price of a home in the whole country. This information helps new employees to know the attributes of the house before they place bid to buy, so that they make informed choices.
It can be seen, that there is a coefficient, which is significant in the model, the other factor to be considered, is the number of bedrooms a house has the number of bathrooms and the house square feet. The other factors that apparently due not contribute much to the price of a home in the regression analysis are, the area in which the home is located does not determine its price. In other words, the prices are more or less similar across all the areas.

The square footage of the lot also does not influence the price of a home. This implies that emphasis should not be placed on the size of the whole lot’s square footage. Importantly, the result of this analysis indicates that the year house construction is not a factor to be considered when determining the price of a home. Thus, the prospecting buyer will consider the maintenance of the house in addition to the other characteristics that were significant in the model.

PART 2
MEMO: HOUSE PRICES IN PUGET AREA
This memo of house prices in the Puget area in relation to certain variables that influence the pricing of houses. The results of the initial assessment performed show that the price of a home depends on a number of factors. All the results displayed above have a confidence interval of 95 per cent. This means that the value depicted may have an error of + or – 5 per cent of the value presented. This analysis will make it easy for the new employees to find homes without much difficulty, and save time. It also enables the employees to be prepared by giving them a hint on how much money they require to own buy a house and with which characteristics. The model for estimating the prices of a houses in Puget area as a factor of certain important variables is illustrated below:
Price = -7278.64 + 18060.7(Number of bathrooms in the home) + 169.15(HSE SQFT) + 1.75(LOT SQ FT)

Say a new employee requires a home with a square footage of 2000 square footage, he or she will have to pay the sum of $ 394,692 (in the Bothell area), and that of 2500 square footage will pay $ 486,913 (Mukilteo). Lastly, an employee requiring a home with a square footage of 3000square feet will pay $ 618, 789 (in the Everett area) in the Puget region. These estimates are made based on the assumption that the mean number of bedrooms and bathrooms are 3 and 2 respectively.
Briefly, this memo will give the new employees a rough estimate of the prices they should expect to be charged for houses with their desired characteristics. The price of a home depends mostly on the age of the home, lot square footage (LOT SQFT) and the number of bathrooms a house has.

Surname 9

Residuals

 

 

How does an understanding of how people learn affect projects audits and evaluations?

By understanding how people learn helps the auditors and evaluators to be able to know the various concepts that the team used when organizing and implementing the project. Various methods are used when appraising projects and it would therefore be vital if the people evaluating the projects knew these methods and concepts used so that they an apply it to avoid one method conflicting with another.

It is also vital to know how people lean because by doing so, one is able to know the criterion that the team used when assessing the criterion, be able to assess the efficiency and cost as well as the design, and the laid down structures to implement the said project. This being the case, it would be possible for the evaluator to know what to apply concerning a certain criteria that was used, as well as other options that could have been used to start or conclude an audit. Understanding how people learn helps the evaluator or auditor to know the loopholes that may lead to project incompletion.

Describe why it is important to properly terminate a project. What are some key advantages?
A project is an activity that has a start date as well as an end date or the termination stage. After all the requirements have been met and the project is finished, or after it has stalled, it is vital to properly terminate the project mainly because with termination marks the end of the project and the outcome of the project can be seen in terms of success or failure. The main advantages of terminating a project include the following reasons

Proper project termination is vital as it improves the morale and the confidence of the teams, which were working on the project. A successful project termination helps the team members to feel the sense of achievement in the accomplished work that was set before them. At the same time, on the side of the end-users, customer satisfaction is increased where they can rely on the project in the fulfillment of their needs.

Having a successful project termination helps a company gain experiences necessary to run such project in terms of cost and resources control and also helps the staff in gaining knowledge necessary to tackle future projects as well as helping the company continue improving their processes. Of importance to note is that in case of a project terminated due to performance inadequacy, the termination would be important, as it would help the donors to know where the problem was, as well as reschedule the projects and/ or increasing the finance or change the leadership if they are the causes of failure.

Surname 2

Disproportionate Minority Contact

How I would evaluate the outcome (s) of your applied research project
My applied research projects focuses on evaluating the efficacy of the interventions use to address disproportionate minority contact (DMC). I will address the matter by soliciting sponsorship for establishing schools and colleges in minority community neighborhoods and centers to allow for equitable access to education.
Explain how you would assess whether the original problem you identified had been solved/alleviated by your efforts

Prior to the initiation of the project, I will collect data from juvenile courts and criminal courts to establish the baseline statistics of cases involving youth of color. The data will capture the nature of the offense and the sentencing. After every month following the commencement of the project, I will visit various courts and prisons to collect data on the number of youth of color that have made contact with the juvenile justice system and compare it with the baseline and the past months progressively. Then I will analyze data using the SPSS software to evaluate the statistical significance of the results of my survey. After which, I will compute several graphs to capture the trend in DMC. I can use this data to convince policy makers on education to develop policies that motivate retention or inclusion of youth of color in education programs.

Present plans to compare at least five points of data collected from your program participants before the implementation of your program, with corresponding data collected from the same program participants after the implementation of your program
My participants of the program include juvenile courts, criminal courts, correctional facilities, prisons, and schools. The data I will collect from these participants include cases of juvenile delinquency associated with youth of color; the nature of verdict; the proportion of youth of color in correctional facilities; the ratio of youth of color incarcerated in adult prisons; the proportion of minority juvenile offenders referred to criminal courts. Other aspects that my data will address include the proportion of youth of minority enrolled in elementary schools and high schools, and the ratio of school dropout cases of youth of color. Lastly, the proportion of youth of color in colleges to capture the rate of advancement of education is another point my project will capture. Then I will conduct a prospective study to evaluate the proportion of cases related with juvenile of color in juvenile and criminal courts; the proportion of minority youth in correctional facilities, and prisons; the ratio of minority youth who dropout from schools.

What sets of data you would compare why they would be appropriate for use in your program evaluation how you would compare, interpret, and analyze the data to make your determinations regarding whether specific goals of your program had been successfully met
The research project will compare the number of cases related to juvenile of color with those of white juvenile delinquency, and with relevance to the proportion of the respective category in the overall community. Importantly, I will compare the number of juvenile cases referrals to criminal courts between respective majority and minority race or ethnicity. Similarly, the data will compare the number of juvenile youth of color incarcerated in corrective centers, and adult prisons compared to their white youth. Then a similar analysis will be conducted within three year on monthly basis. I will perform a t-test for the data using SPSS to establish the statistical significance of the differences, which will proof if the projects have achieved positive outcomes in the end.

Explain how you would reach an evaluative conclusion about the success of your program based on your comparison of these sets of data
To reach an evaluative conclusion, I will compare between the rates of the youth categories contact with juvenile justice system. In particular, I will conduct a t-test comparing the data related to youth of color and their white counterpart at the baseline. Then I will perform the same for progressive months between the two categories and with the baseline data. The t-test captures the statistical significance of the differences between contacts of juvenile offenders with the juvenile justice system between the white youth and color youth.

Expected outcome of the project
Specific sites support use strategy innovations to alleviate DMC through data collection and reporting, improved cultural competence, optional detention, and resources to support post-disposition juveniles. Other organizations collaborate with juvenile reforms projects to and focus completely on elimination of ethnic or racial bias in the justice system via planning and involvement at the community level. Initiatives, such as programs to assess local justice system and working at the community level to develop and improve techniques of alleviating DMC.

States as well as localities are progressing in terms of minimizing and eradicating DMC. These states and localities have applied various strategies to minimize contact of juvenile from minority groups with the juvenile justice system. First, application of the Juvenile Detention Alternatives Initiatives (JDAI) tool is associated with a significant drop between minority youth confined in detentions rather than in community options compared to white youth charged for the same offense being detained instead of being assigned in community work.

A second alternative is about collaborating with schools to promote school-based protocols of conflict resolutions and retention or inclusion of students. Decrease of disproportionate confinement of youth of minority for violation of procedural probation with a Sanction Supervision Program that provides exhaustive management of case as well as probation services to juveniles and their families. Lastly, initiating a reminder call program helps reduce detention of African American youth related to bench warrants for absconding court hearing.

DISPROPORTIONATE MINORITY CONTACT 2

Running Head: DISPROPORTIONATE MINORITY CONTACT

 

The project involved organizing groups of people in the rural and urban places and moving each group to the other setting to create new understanding of issues that are found in the setting and hopefully learn a few things that would seem relevant and helpful to the participants of the project. The leader had to identify the sample areas and set the example by visiting the chosen areas and sampling the response of the people on the ground. The leader had to mobilize the project members to search for willing participants for the project in a time frame that was acceptable for the project.

The researchers had to choose eligible candidates through carrying out oral interviews with random people in the urban and the rural places. The leader then had to organize means of transport to and from designated locations and the researchers had to spend the day with the chosen teams in the different setting.

The leader also had to explain to the group the aim of the project, the goals and objectives of carrying out the research. This ensured that every member involved in the project not only owned the process but also ensured that they were doing their best to meet the set goals. The aim of the project was to bring the chosen groups in unfamiliar territory and help them to understand issues that affect their counterparts in the setting chosen. The leader needed to involve other participants and people to carry out the research in order to achieve acceptable and effective results. Those chosen for the project needed to go to the ground and source for willing participants who would be transported to the various locations for the research to be carried out. The researchers started out with a random oral interview that established to the research team that the project was manageable.

Some challenges that members of the project faced were that some people ignored them, others had busy schedules that would not allow them to participate and others were simply not interested. However, the team managed to get a sizeable number that would participate in the project.
The team was well counseled and was prepared for the various reactions that they would encounter on the ground as they sourced for participants and during the process. This helped the project to be accomplished in the determined time frame. The leader cultivated cooperation within the project members which helped to reinforce and encourage the team.

This gave them the freedom to act in accordance to the project in the best way they thought was right and in a way that would bring guarantee positive results for all involved in the project. The project was successful since the project members found willing participants for the project, did their best to meet the set goals of the project, moreover, the participants enjoyed the experience and learned from it too. The members from the rural setting appreciated the urban environment and challenges that come with the environment and the urban team had the same experience. The leader made effective decisions that helped the goals of the project to be met with ease.

The leadership of the project was shared which caused every project member to own the project and deliver good work. If the project were to be repeated, both the leader and the researcher felt that they would do it the same way they had carried it out. The project had several volunteer workers from urban and rural areas. The volunteer workers were treated in a fair manner and there were no complaints. Transport to and from the various locations were provided and basic comforts offered. The team delivered more than was expected by the researcher and the participants. Allowing everyone to own the project ensured quality and friendly services. The participants were satisfied with how the staff handled different issues that arose within the project.

For any leader, trust and hope are two virtues that help them and the group to see the work through to the end. They are virtues that when possessed by both the leader and the members of the group helps in the realization of the set goals and effective achievement. Trust ensures that the researcher can rely and avoid checking up on fellow project members; it ensures that the members own the project which in turn ensures that the project is carried out with no hitches. Hope gives the group a fair chance to start and complete the project successfully.

When a leader possesses hope they are able to visualize the future and in turn help the group to have a vision of the expected future. When one is a leader, they need to posses these virtues since they help them to have peace of mind and give the members of the group a chance to be independent and carry out duties with minimum or no supervision. In addition, trust and hope are virtues that help the group to withstand challenges that are unforeseen and help them to forge ahead and complete the project.

(Name, 25-Feb-2013) 1

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(Name)
(25-Feb-2013)

What are the primary benefits of developing a comprehensive project scope analysis?

Many benefits are associated with the development of a comprehensive scope in project management. It should be noted that a comprehensive analysis of the project helps the company or organization in driving the scope of the project to a successful completion. This means that with a well laid out scope, the direction of the project can be monitored easily and better (efficiently and effectively).

The scope is important as it helps the project leaders to transform the project into a working concept. This is possible because the concept will guide the project team members in formulating the details and mapping them out. The scope also helps in setting out the steps and phases of the project and their activity level. A comprehensive scope will lead to the project being run on the original goals and objective. The scope also helps in running the project within the set budget and the time allocated. Finally a comprehensive project scope is beneficial as it helps the project team members to meet the required quality and specifications.

What is the logic behind developing a plan for project close-out prior to even beginning the project?
It is important to develop project close-out even before beginning a project because during the project operation, there may not be enough time to develop a plan. At the same time, documentation will be required by the management or the parties involved.

These documents may be used for legal purposes, or as training materials, or in other instances like auditing purposes. Early preparation of the close-out documentation helps in creating an accurate document that will be monitored as the project continues to ascertain whether the project runs as expected. At the same time, understanding the requirements of the final documentation will lead to proper records keeping and monitoring during the operations of the project. It should be noted that creating the project closeout after the completion of the project may be inaccurate and difficult.

Surname 2