Conceptual Framework

 

Murder Rate Data Analysis in RStudios

 

I selected the data set number 60, which is based on the murder rate in southern states and non-southern.  The variables in the data set are defined as followed;

Table 1

Murder Rate Variable Definition Table

                   (McManus, 1985)

For the Murder Rate data set that I selected, I examined the variables listed above, and then I conducted a multiple regression in R.  Below is the code that I ran in r;

#Linear Regression for the number of executions in the Murder Rate data set

MurderRates1 <- lm(MurderRates$rate ~ . + I(executions > 0), data = MurderRates)

summary(MurderRates1)

#Multiple Regression for the median time served, the median family income, non-Caucasian, Labor force participation rate, & Factor indicating region.

model <- I(MurderRates$executions > 0) ~ time + income + noncauc + lfp + southern

MurderRates2 <- lm(model, data = MurderRates)

summary(MurderRates2)

 

## Binomial models. Note: southern coefficient

MurderRates2_logit <- glm(model, data = MurderRates, family = binomial)

summary(MurderRates2_logit)

 

MurderRates2_logit2 <- glm(model, data = MurderRates, family = binomial,

                 control = list(epsilon = 1e-15, maxit = 50, trace = FALSE))

summary(MurderRates2_logit2)

 

MurderRates2_probit <- glm(model, data = MurderRates, family = binomial(link = "probit"))

summary(MurderRates2_probit)

 

MurderRates2_probit2 <- glm(model, data = MurderRates , family = binomial(link = "probit"),

                  control = list(epsilon = 1e-15, maxit = 50, trace = FALSE))

summary(MurderRates2_probit2)

 

## Explanation: quasi-complete separation

with(MurderRates, table(executions > 0, southern))

 

#residual plot check (multiple regression) for the correlation between

par(mfrow=c(2,2))

plot(MurderRates2_logit)

par(mfrow=c(1,1))

(Stokes, 2004)

Here is the output to the commands that were ran;

Figure 1

Output No.1

 

Figure 2

Output No.2

 

Figure 3

Output No.3

Figure 4

Output No.4

Figure 5

Output No.5

Figure 6

Output No.6

 

Figure 7

Output No.7

LOWESS stands for locally weighted scatterplot smoothing and is one of the many non-parametric regression techniques, but arguably the most flexible.  A smoothing function is a function that attempts to capture some sort of general pattern or relationship in the data set while trying to reduce the noise in the data set.  Typically, continuous data is used, so the greater the range of environmental conditions encompassed the better.  The LOWESS analysis is used to basically help visually assess the relationship between two variables when it can be hard to visualize the data.  The most suitable for large data sets, it helps to create a smooth line through a plot or scatter plot to help see the relationship visually.

 

The intent of LOWESS is to let the data set tell its own story and speak for itself.  LOWESS is also referred to as LOESS and it is non-parametric.  Therefore, the fitted curve is more focused on the shape of the curve because that is the most revealing of the data set. The disadvantage or drawback of using the LOWESS method is that it does not produce a linear regression equation that models the relationship of the data.  Therefore, you cannot reuse the model and apply it to another data set.

I used the following code to conduct a LOWESS analysis to adjust the data for a smoother span;

 

#Convert to a data frame

library(dplyr)

df <- as_tibble(MurderRates)

df

 

#create scatterplot

plot(df$rate, df$convictions)

 

#add lowess smoothing curves

lines(lowess(df$rate, df$convictions), col='red')

lines(lowess(df$rate, df$convictions, f=0.6), col='purple')

lines(lowess(df$rate, df$convictions, f=6), col='steelblue')

 

#add legend to plot

legend('topleft',

       col = c('red', 'purple', 'steelblue'),

       lwd = 2,

       c('Smoother = 1', 'Smoother = 0.6', 'Smoother = 6'))

You can also adjust the ‘f’ argument in the lowess() functions to decrease or increase the value that is used for a smoother span in the data set.  The large the value provided, the smoother the lowess curve will be.  See figure 8 for the output for the lowess function.

 

Figure 8

Output No.8

References

 

McManus, W.S. (1985). Estimates of the Deterrent Effect of Capital Punishment: The

Importance of the Researcher's Prior Beliefs. Journal of Political Economy, 93, 417–425.

 

Stokes, H. (2004). On the Advantage of Using Two or More Econometric Software Systems to

Solve the Same Problem. Journal of Economic and Social Measurement, 29, 307–320.

Dissertation Elevator Speech

 Introduction:  

Hello, my name is Shanel Crusoe and I am a second-year doctoral student at Colorado Technical University.  My doctoral concentration is a Doctor of Computer Science with a concentration in Big Data Analytics

My Contact information:

Shanel Crusoe

Shanel.Crusoe1@student.ctuonline.edu

 

 

My subject of research interest: 

Airplanes remain the safest form of transportation.   According to the International Air Transportation Association (IATA) flying is the safest form of transportation.   However, airplane accidents do occur, and one of the most common causes of aviation accidents other than mechanical failures is human error.  It is essential to determine the root cause of pilot errors, to prevent human-error-related airplane accidents from reoccurring.  Additionally, with a better understanding of human errors that occur during the take-off and landing phases of flight, better training programs can be implemented to eliminate or reduce human error.  Human error is inevitable, and it is important to collect reliable information on what causes human-related errors in airplane accidents.  

 

My Research Question:

Can real-time machine learning predict the causal factors that lead to human error aviation accidents during the take-off and landing phases of flight?

 

Relating Literature

Contextual Topics:

One of the contextual topics related to my subject is ‘Human Error in Aviation’, by authors Rene Amalberti and Liên Wioland.  This article focuses on aviation safety and the associated human factors.  Human error is recognized to be the primary cause of accidents.  There is an increased effort to primarily target this problem in efforts to reduce human errors that lead to accidents (Amalberti, & Wioland, 2020).

 

Another contextual topic related to my subject is ‘Human Error Perspectives in Aviation’, by authors Douglas A. Wiegmann &Scott A. Shappell.  This article focuses on how humans play a pivotal role in aviation accidents.  The article focuses on the human error framework and the accident investigation process, it further focuses on systematically organizing the errors that have theoretical similarities.  The aim is to enhance safety programs, that often rely primarily on personal experiences, the ultimate intent of the article is to provide safety practitioners with an overview of the human error perspectives in aviation (Wiegmann & Shappell, 2001)

 

Relating Theoretical Topics:

A theoretical topic that relates to my subject is ‘Applying Modern Error Theory to the Problem of Missed Injuries in Trauma’, by authors D. L. Clarke, J. Gouveia, S. R. Thomson & D. J. J. Muckart.  This article addresses the theory that if human error is accounted for during trauma surgeries, it can then reduce the number of missed injuries.   A missed injury was anything that escaped detection from the initial medical assessment.  These missed injuries were recorded, and the physician level was documented.  Although missing injuries are uncommon, they can be made by all grades of staff, and understanding these errors will develop error reduction strategies.  Similarly, in aviation accidents, human errors can occur at any phase of flight by either the pilot or the flight crew (Clarke, 2008).

 

References

Amalberti, R., & Wioland, L. I. E. N. (2020). Human error in aviation. In Aviation safety (pp. 91-

108). CRC Press.

 

Clarke, D. L., Gouveia, J., Thomson, S. R., & Muckart, D. J. J. (2008). Applying modern error 

theory to the problem of missed injuries in trauma. World journal of surgery, 32(6), 1176-1182. 

 

Wiegmann, D. A., & Shappell, S. A. (2001). Human error perspectives in aviation. The 

International Journal of Aviation Psychology, 11(4), 341-357.

Homeless Helper App Video

 

My video is based on my Homeless Helper Application.  The Homeless Helper is an application that is used to help homeless individuals and low-income families connect to available services and resources using the internet and mobile devices.  This application allows needy people to have access to resources right at their fingertips.  This application can communicate in any language, and it can be used anywhere at any time.  The application is meant to support people who might not otherwise have access to this information to be connected with various resources.  The Homeless Helper application also has built in technology that can provide healthcare assessments and then connect you directly with the correct healthcare provider.  Additionally, the application allows organizations that are doing community service events to list their events so that can add and manage their services through the application, and also allow needy individuals to see what services are being offered.  Organizations that have shelters that are offering beds for the homeless can list the number of beds that they have available.  The application provides a direct connection between service providers’ technology and the people that need to utilize their services.

The Homeless Helper connects needy people with the services that they need.  Some of the features that they have is that the application is offered on most mobile platforms, the internet, Google Play Store, and the Apple Store.  It offers a unique design; its simple design allows users to intuitively list their services and the software provides a map view of the location of the service that is being offered.  It is easy for users to find and connect to the service that they need.  Users can easily narrow down their searches by using the filtering capability.  This application can communicate in any language, and it can be used anywhere at any time.  It also has built-in technology to support homeless people who are blind and hearing impaired.  It does not matter where individuals are geographically located, the application communicates in any language and provides resources at any location, nationwide.   The application is meant to support people who might not otherwise have access to this information to be connected with various resources.  The Homeless Helper application also has built-in technology that can provide healthcare assessments and then connect you directly with the correct healthcare provider.  The application can use infrared technology to scan an individual’s vital signs to determine if they have any mental conditions or if they have any other healthcare concerns that need to be addressed.  After the individual’s vital scans are completed, they will be automatically referred to the nearest healthcare provider in their immediate area.  Additionally, the application has a built-in feature that provides emergency support, by providing crisis line contact information.  Users can easily connect with various national hotlines which can help users with emotional support.

I used the online video creation tool to create my 30-second video.  The video highlights the homeless helper’s features.  The video helps to demonstrate how intuitive it is and how easy it is to use the Homeless Helper application.   The video shows the easy-to-use layout, and further, it shows how the technology is connected with the users.   Here is the direct link to my video;

HH App Video

Learning From Failure & Having a Sociotechnical Plan

 

There is great wisdom in learning from our failures, yet there are not too many businesses that can actually do that well.  It is essential that businesses learn from their failures in an effort to improve future performance, however, many businesses do not seem to implement strategies for real change.  There are several businesses that had good strategies in place, but they failed to stay operational due to unforeseen circumstances.  For example, Tower Record was a music store that was primarily based in California.  Tower Records allowed its customers to access their favorite music by simply taking a trip to their record store.  Whenever you hear the name Tower Records you associated it with buying music.  At its peak, the company operated over 200 stores in 15 different countries.  The company reported a net income of $1 billion in 1999.  However, in 2006 the company filed for bankruptcy.  The company had expanded very quickly and had to compete with smaller record shops that were selling music at half price.  As online music purchases advanced with technology, more companies started selling music online.  MP3 players began to appear on the market, and then the iPod was introduced, and this completely changed the industry.  The evolution of online music sales also led to piracy and Tower Records was not spared, illegal music was being made available for free almost everywhere.  This drastically decreased their sale and ultimately led to the company’s demise.  Even businesses that start off with a well-thought-out plan can fail if they do not stay modernized and adapt to changing strategies.

            It is essential for businesses to forecast change and vision their future state and engage in scenario planning.  Businesses have to be prepared for the changes that lie ahead.  Businesses need to anticipate change and be prepared to handle it.  They must have a team that is consistently doing trade studies and continuously observing the market to stay on top of what the next hottest thing may be.  Therefore, making companies more adaptable will help companies to have continued success despite the impediments that they may be forced to encounter along the way.

            Customers contribute to the design of sociotechnical systems because they provide key feedback on how to implement continuous adaptations and system improvements in collaboration with the system developers (Carayon, 2006).  This is important because sociotechnical is about connecting society with technology.  Sociotechnical refers to the connection of social awareness and the technical aspects of an organization.  It is all about technical performance and the quality that it brings to people’s lives.

            There is always an economic impact that has to be considered as well.  In order to remain competitive and to compete with prices, companies will need to consider offering memberships and plans that will allow customers to receive enhanced perks and rewards with continued membership and a promise that the company will always offer the latest convenience in technology and the best social platform to deliver the service for their needs.  Another force to consider is political impacts, which can drive new policies and laws.  This is also what impacted Tower Records, at the time there were different laws on piracy and now those have changed, and music can be sold online and even shared. 

            It is important to ensure that your systems can interact and behave well with human interaction, businesses are implementing a more holistic approach.  It is important to evaluate the entire sociotechnical system to avoid any potential impacts which make it easier to evolve and respond to change.  Having a sociotechnical plan is an effective way to bring technology and people together while managing risk and improving the human experience with the latest technology.

References

Carayon, P. (2006). Human factors of complex sociotechnical systems. Applied

ergonomics, 37(4), 525-535.