Pepperdine University Criminological Theories & Neurological Theory Discussion Law Assignment Help

Pepperdine University Criminological Theories & Neurological Theory Discussion Law Assignment Help. Pepperdine University Criminological Theories & Neurological Theory Discussion Law Assignment Help.

I’m working on a criminal justice multi-part question and need a sample draft to help me study.
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) Complete chapters 6, then submit a 500-800 words minimum answering question 1, 2, and 4 of the listed questions in the “Critical Thinking” section found at the end of the chapter. When writing your section, do not quote directly from the reading, and please properly cite your sources using APA, MLA, or Chicago format. Please submit a PDF with your answers on Canvas using the assignment submission section.

1. Is there a “transitional” area in your town or city?
Does the crime rate remain constant there, regardless
of who moves in or out?
2. Is it possible that a distinct lower-class culture exists?
Do you know anyone who has the focal concerns
Miller talks about? Were there “focal concerns” in your
high school or college experience?
3. Have you ever perceived anomie? What causes
anomie? Is there more than one cause of strain?
4. How would Merton explain middle-class crime? How
would Agnew?
5. Could “relative deprivation” produce crime among
college-educated white-collar workers?

Pepperdine University Criminological Theories & Neurological Theory Discussion Law Assignment Help[supanova_question]

George Mason University What Role Does the Audit Committee Have Discussion Computer Science Assignment Help

I’m working on a networking question and need an explanation to help me learn.

A net class exam has two essay questions about Firewalls spreadsheets and ACLs

Create rules on a firewall to meet the following requirements:

  1. Allow all HTTP traffic to a web server with an IP of 192.168.1.25.
  2. Allow all HTTP and HTTPS traffic to a web server with an IP of 192.168.1.25.
  3. Allow DNS queries from any source to a computer with an IP of 192.168.1.10.
  4. Block DNS zone transfer traffic from any source to any destination.
  5. Block all DNS traffic from any source to any destination.
  6. Implement implicit deny.

How many rules would you create? What protocols would you use? What ports? create a table(similar to the below) to meet the above requirements:

Rule# permission(A/R) Protocol Source Destination port

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FIN 4453 The Payoff and Profit Curves for Alibaba Stock Options Discussion Paper Economics Assignment Help

I’m working on a finance question and need support to help me understand better.

assignment 7

In this assignment, follow the steps below and generate the payoff and profit curves for a stock’s options.

1. Pick one stock you are interested in to analyze.

2. Select one call option and one put option for this stock based on your preference on the strike prices and expiration dates. The two options do not need to have the same strike price or expiration date. Collect last price information from Yahoo! Finance for the options selected.

3. In Excel, generate the following charts:

a. A chart to show the payoffs and profits for a long position in the call option selected based on different possible terminal underlying stock price;

b. A chart to show the payoffs and profits for a short position in the call option selected based on different possible terminal underlying stock price;

c. A chart to show the payoffs and profits for a long position in the put option selected based on different possible terminal underlying stock price;

d. A chart to show the payoffs and profits for a short position in the put option selected based on different possible terminal underlying stock prices.

For all charts above, you need to include the calculation details in Excel as the basis to create those charts.

4. Write a brief report in a Word file (400 words minimium)and briefly describe the following items:

  • What is your estimation of the future price of the underlying stock on the expiration date of those options selected? Just give a guess with some brief reasoning according to your instincts. No analysis needs to be done for this estimation.
  • Based on your estimation of the underlying stock’s future price, use the charts created, draw a conclusion which option you would like to long or short.

check the link below to follow the instructors lectures in order to solve the assignment:

https://we.tl/t-rJgEZEcXog

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Miami Dade College Social Behavioral Learning Exercise Report Health Medical Assignment Help

I’m working on a health & medical writing question and need an explanation to help me study.

Drugs in the News: Distinguishing Fact from Opinion

Introduction

This assignment is designed to develop your critical thinking skills, particularly in distinguishing facts from opinions. The statement “Over one billion dollars was used to prohibit drug use and drug trafficking last year” is a fact that can be verified by checking the relevant research on the subject. The statement “The federal government has not spent enough money to stop drug abuse and drug trafficking” is an expressed opinion. Not all statements of fact are true, unfortunately, because some are based on false or inaccurate information. For this assignment, however, you should be concerned primarily with understanding the difference between those statements that appear to be factual and those statements that appear to be based upon opinion.

Learning Exercises

Select a brief report from the media, excluding the Internet (print form is best, but you can work from radio or television reports, if you have recorded them) regarding drugs, drug use, or drug use problems in a medical context. Advertisements cannot to be used for this assignment. An online article from a non-Internet-based organization, which produces news for outlets other than the Internet, will be accepted. For example, an article from USA Today online or CNN online is okay. An article from Yahoo or Huffington Post will not be accepted. I hope that this information is helpful.

oIdentify those statements that appear to be based upon facts and those that appear to be based upon opinion.

oReview the whole report, summarize its major statements, and comment on its overall value in covering the issue.

oWrite a paper (no more than two (2) pages in length using 12-point, Times New Roman font, 1.5 line spacing), giving an account of the above researched information.

oUpload the actual article or report (as a PDF document).

Grading

This assignment will be graded on neatness, relevance, meeting the objectives, and promptness.

Your score will be determined by the following formula:

70% content

20% grammar, syntax, readability, and flow

10% originality

This learning exercise represents a large component of your grade; a corresponding effort is expected and necessary in order to earn a satisfactory grade.

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ANT 215 Stony Brook Environmental Science Carbon Footprint Discussion Writing Assignment Help

I’m working on a environmental science writing question and need support to help me study.

In at least 500 words, please:

-share what you’ve learned about how aspects of your lifestyle affect your carbon footprint.

-compare your footprint to those of the U.S., European Union, and the entire world (you can even past the image from the calculator website if you like).

-describe what you think the best steps to take to reduce your carbon footprint would be. Do you think you could ever reach the worldwide target to combat climate change at 2 metric tons?

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AT Still University Rigor Mortis The Mortis Brothers Discussion Board Law Assignment Help

I’m working on a criminal justice question and need support to help me understand better.

Discussion #4: During this chapter, we have learned about death investigations, the decomposition of the human body, tools to aid us in developing information on a deceased body, and so on. Time of death is a critical component of a death investigation. Three times of death can be determined-Legal (time of death called by a Coroner or Doctor only), Physiological-the actual moment the human body ceases to function, and Estimated-created from the resulting evidence. Three pieces of evidence that can determine information on the Estimated time of death are what I like to call “the Mortis Brothers”. Discuss these three Mortises from the chapter and answer the following:

  1. Define each type of mortis
  2. Discuss what they mean with regards to time of death-when do they appear?
  3. Discuss what other pieces of information they can provide?
  4. Why do they work together?

Finally, discuss how you feel understanding the “mortis brothers” can help in death investigation cases, and why this information is necessary. Use examples from cases in the book or other sources to support your discussion.

Remember-READ the instructions for writing a Discussion Board. Your initial post MUST BE AT LEAST 300 WORDS IN LENGTH; if shorter, you will be marked down. There is no limit to the length. YOU MUST RESPOND TO TWO CLASSMATES’ POSTS. Click on the post, then click on Add a Comment to respond. EACH of these two posts must be AT LEAST 200 WORDS IN LENGTH! You must include at least one citation and reference in your post in APA 7 format. If any evidence or data is used in responses to classmates, these must also be cited and referenced in the response. If no citation(s) and reference(s) points will be lost.

AT Still University Rigor Mortis The Mortis Brothers Discussion Board Law Assignment Help[supanova_question]

SCSU Rethinking the Stanford Prison Experiment Discussion Writing Assignment Help

For this homework, we want you to listen to an episodes of the podcast “You’re Wrong About”, focused on unpacking the misunderstandings around the Stanford Prison Experiment. 

https://www.stitcher.com/show/youre-wrong-about/episode/the-stanford-prison-experiment-80302479

You can also read a transcript here:

https://www.buzzsprout.com/1112270/6933818-the-stanford-prison-experiment

For this assignment, focus on the material presented as it pertains to the study. We are not requesting that you review the podcast, or the hosts.

After listening to the podcast (or reading the transcript), please submit a discussion board post. You may respond to the posts of other students after that, if you choose, but you are not required to. 

In your discussion board response, please address the two points listed below. We do not request an essay – just a short, but clear and complete, response to each question, written clearly and with attention to proper spelling and grammar. A few clear, full, properly structured sentences in response to each prompt should be sufficient.

  1. Given what you heard, do you believe deindividuation was truly bring examined, or is there another social psychology phenomenon that could be at play in the antisocial behaviors observed? Please either briefly explain why you believe it was deindividuation OR identify the other phenomenon you think it was and briefly explain why you believe it was that instead.
  2. What is a particular problem with this study from the perspective of research design? Briefly outline why it is a flaw.

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Mesa College Meeting with Childrens Lively Minds Journal Response Writing Assignment Help

I’m working on a writing multi-part question and need an explanation to help me understand better.

1. Meeting with Children’s Lively Minds

2. Intentional Teacher
3. A Thinking Lens for Reflection and Inquiry

After your Reading

Please look over your notes from today’s observation and answer the following, please use details from the observation and the reading to support your ideas:

You are taking the role of the teacher. Use your observations to guide your thinking.

  1. What new thinking do you have about the children as a result of this observation?
  2. What are you excited to try next with the children after this observation?
  3. What changes in the environment would support and expand children’s Thinking?
  4. Identify new materials you can add to support specific explorations you observed.
  5. How will you share what happened during this play time with the children, families? and your co-workers?
  6. How has your thinking about observation shifted?

Rubric

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Harvard University GroupLens Research and Rating Distribution RStudio Project Programming Assignment Help

I’m working on a programming project and need support to help me study.

#Jennifer Young

#Movie Recommendation Project

#Recommendation systems are used more and more, as consumers expect suggestions based

#on their known likes so that they can discover new likes in products, movies, music

#and other interests. They assist users in finding what they might be interested in

#based on their preferences and previous interactions. In this report, a movie

#recommendation system using the MovieLens dataset from HarvardX’s Data Science

#Professional Certificate3 program will be covered. GroupLens Research is the

#organization that collected the data sets for this project from their site:

#(https://movielens.org).

## First specify the packages of interest

packages = c(“tidyverse”, “caret”,

“ggplot2”)

## Now load or install&load all

package.check <- lapply(

packages,

FUN = function(x) {

if (!require(x, character.only = TRUE)) {

install.packages(x, dependencies = TRUE)

library(x, character.only = TRUE)

}

}

)

library(tidyverse)

library(caret)

library(ggplot2)

dl <- tempfile()

download.file(“http://files.grouplens.org/datasets/movielens/ml-10m.zip”, dl)

ratings <- read.table(text = gsub(“::”, “t”, readLines(unzip(dl, “ml-10M100K/ratings.dat”))),

col.names = c(“userId”, “movieId”, “rating”, “timestamp”))

movies <- str_split_fixed(readLines(unzip(dl, “ml-10M100K/movies.dat”)), “\::”, 3)

colnames(movies) <- c(“movieId”, “title”, “genres”)

movies <- as.data.frame(movies) %>% mutate(movieId = as.integer(movieId),

title = as.character(title),

genres = as.character(genres))

movielens <- left_join(ratings, movies, by = “movieId”)

#Methods and Analysis

#There are five steps in the data analysis process that must be completed.

#In this case, the data must be prepared. The dataset from was downloaded from

#the MovieLens website and split into two subsets used for training and validation.

#In this case, we named the training set “edx” and the validation set “validation”.

#For training and testing, the edx set was split again into two subsets.

#The edx set is trained with the model when it reaches the RMSE goal and the

#validation set is used for final validation. During data exploration and

#visualization, charts are crated to understand the data and how it affects

#the outcome. We observe the mean of observed values, the distribution of ratings,#

#mean movie ratings, movie effect, user effect and number of ratings per movie.

#We improve the RMSE by including the user and movie effects and applying the

#regularization parameter for samples that have few ratings.

# The Validation subset will be 10% of the MovieLens data.

set.seed(1)

test_index <- createDataPartition(y = movielens$rating, times = 1, p = 0.1, list = FALSE)

edx <- movielens[-test_index,]

temp <- movielens[test_index,]

#Make sure userId and movieId in validation set are also in edx subset:

validation <- temp %>%

semi_join(edx, by = “movieId”) %>%

semi_join(edx, by = “userId”)

# Add rows removed from validation set back into edx set

removed <- anti_join(temp, validation)

edx <- rbind(edx, removed)

rm(dl, ratings, movies, test_index, temp, movielens, removed)

# lists six variables “userID”, “movieID”, “rating”, “timestamp”, “title”, and “genres” in data frame

head(edx) %>%

print.data.frame()

#Looking for missing values

summary(edx)

#unique movies and users in the edx subset

edx %>%

summarize(n_users = n_distinct(userId),

n_movies = n_distinct(movieId))

#distribution of ratings (histogram)

edx %>%

ggplot(aes(rating)) +

geom_histogram(binwidth = 0.25, color = “red”) +

scale_x_discrete(limits = c(seq(0.5,5,0.5))) +

scale_y_continuous(breaks = c(seq(0, 3000000, 500000))) +

ggtitle(“Rating distribution”)

#ratingspermovie (Histogram)

edx %>%

count(movieId) %>%

ggplot(aes(n)) +

geom_histogram(bins = 25, color = “yellow”) +

scale_x_log10() +

xlab(“Number of ratings”) +

ylab(“Number of movies”) +

ggtitle(“Number of ratings per movie”)

#movies rated once (chart)

edx %>%

group_by(movieId) %>%

summarize(count = n()) %>%

filter(count == 1) %>%

left_join(edx, by = “movieId”) %>%

group_by(title) %>%

summarize(rating = rating, n_rating = count) %>%

slice(1:20) %>%

knitr::kable()

#User ratings (Histogram)

edx %>%

count(userId) %>%

ggplot(aes(n)) +

geom_histogram(bins = 25, color = “green”) +

scale_x_log10() +

xlab(“Number of ratings”) +

ylab(“Number of users”) +

ggtitle(“Number of ratings given by users”)

#Mean user ratings

edx %>%

group_by(userId) %>%

filter(n() >= 100) %>%

summarize(b_u = mean(rating)) %>%

ggplot(aes(b_u)) +

geom_histogram(bins = 25, color = “white”) +

xlab(“Mean rating”) +

ylab(“Number of users”) +

ggtitle(“Mean movie ratings given by users”) +

scale_x_discrete(limits = c(seq(0.5,5,0.5))) +

theme_light()

#compute the RMSE

RMSE <- function(true_ratings, predicted_ratings){

sqrt(mean((true_ratings – predicted_ratings)^2))

}

#Average movie rating model

mu <- mean(edx$rating)

mu

naive_rmse <- RMSE(validation$rating, mu)

naive_rmse

rmse_results <- tibble(method = “Average movie rating model”, RMSE = naive_rmse)

rmse_results %>% knitr::kable()

#Movie effect model

movie_avgs <- edx %>%

group_by(movieId) %>%

summarize(b_i = mean(rating – mu))

movie_avgs %>% qplot(b_i, geom =”histogram”, bins = 10, data = ., color = I(“red”),

ylab = “Number of movies”, main = “Number of movies with the computed b_i”)

predicted_ratings <- mu + validation %>%

left_join(movie_avgs, by=’movieId’) %>%

pull(b_i)

model_1_rmse <- RMSE(predicted_ratings, validation$rating)

rmse_results <- bind_rows(rmse_results,

tibble(method=”Movie effect model”,

RMSE = model_1_rmse ))

rmse_results %>% knitr::kable()

#Movie and user effect model

user_avgs<- edx %>%

left_join(movie_avgs, by=’movieId’) %>%

group_by(userId) %>%

filter(n() >= 100) %>%

summarize(b_u = mean(rating – mu – b_i))

user_avgs%>% qplot(b_u, geom =”histogram”, bins = 25, data = ., color = I(“magenta”))

user_avgs <- edx %>%

left_join(movie_avgs, by=’movieId’) %>%

group_by(userId) %>%

summarize(b_u = mean(rating – mu – b_i))

predicted_ratings <- validation%>%

left_join(movie_avgs, by=’movieId’) %>%

left_join(user_avgs, by=’userId’) %>%

mutate(pred = mu + b_i + b_u) %>%

pull(pred)

model_2_rmse <- RMSE(predicted_ratings, validation$rating)

rmse_results <- bind_rows(rmse_results,

tibble(method=”Movie and user effect model”,

RMSE = model_2_rmse))

rmse_results %>% knitr::kable()

#Regularized movie and user effect model

lambdas <- seq(0, 10, 0.25)

rmses <- sapply(lambdas, function(l){

mu <- mean(edx$rating)

b_i <- edx %>%

group_by(movieId) %>%

summarize(b_i = sum(rating – mu)/(n()+l))

b_u <- edx %>%

left_join(b_i, by=”movieId”) %>%

group_by(userId) %>%

summarize(b_u = sum(rating – b_i – mu)/(n()+l))

predicted_ratings <-

validation %>%

left_join(b_i, by = “movieId”) %>%

left_join(b_u, by = “userId”) %>%

mutate(pred = mu + b_i + b_u) %>%

pull(pred)

return(RMSE(predicted_ratings, validation$rating))

})

qplot(lambdas, rmses)

lambda <- lambdas[which.min(rmses)]

lambda

rmse_results <- bind_rows(rmse_results,

tibble(method=”Regularized movie and user effect model”,

RMSE = min(rmses)))

rmse_results %>% knitr::kable()

#Results

#For the average movie rating model that we generated first, the result was 1.0606506.

#After accounting for movie effects, we lowered the average to .9437046. In order to lower

#the RMSE even more, we added both the movie and user effects with the result of .8655329.

#Finally, we used regularization to penalize samples with few ratings and got the

#final result of .8649857.

#Conclusion

#In conclusion, we downloaded the data set and prepared it for analysis.

#We looked for various insights and created a simple model from the mean of the

#observed ratings. After that, we added the movie and user effects in an attempt

#to model user behavior. Finally, we conducted regularization that added a

#penalty for the movies and users with the least number of ratings. We achieved

#a model with an RMSE of 0.8649857.

print(“Operating System:”)

version

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University of Calgary Cultural Biases and Struggle with Identity for Immigrants Paper Writing Assignment Help

I’m working on a literature writing question and need support to help me study.

Readings

Required:

Readings: Choose one of the following stories for your essay:

The Broadview Introduction to Literature:

“Squatter” by Rohinton Mistry pg. 163 – 184

Or

Discuss the ideas developed by Rohinton Mistry in the short story “Squatter” regarding the cultural biases and struggle with identity that an individual can have when faced with immigration.

Requirements

  1. The essay should be a maximum of 750 – 1000 words or 3 pages double-spaced.
  2. Format example:
    1. Introduction – 1 paragraph
    2. Body – 2 paragraphs
    3. Conclusion – 1 paragraph
  3. Include the following essay elements:
    1. Thesis statement
    2. Analysis/reflection: develop, connect, and support your ideas by referring to the text you selected.
    3. Include a Works Cited Page using proper MLA format.
    4. Ensure you have at least two in-text citations in proper MLA format.
  4. Here is the Essay Rubric that you will be graded on.

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Pepperdine University Criminological Theories & Neurological Theory Discussion Law Assignment Help

Pepperdine University Criminological Theories & Neurological Theory Discussion Law Assignment Help

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