Category: Data Analytics

I have an existing Lucid chart, which I want to convert in 2 different formats. The lucid chart is on “page 1”. I want it converted into page 2 and page 3 format (use the given format only).In addition to formatting, extend the NIR blue box to Trade compliance. remove the very first doc control swimlane ( keep the other doc control swimlane). Below is the link for the Lucid chart

Exerice 3 Phayton
In this exercise, you will explore the use of programming lang

Exerice 3 Phayton
In this exercise, you will explore the use of programming language to carry out statistical analysis
Carry out the statistics parts
There are two r files for this lecture
classPrt1stat.R. https://drive.google.com/file/d/1M7hy-o2myh-GCuQjd…
classPrt2stat.R. https://drive.google.com/file/d/1qrgkPaX5DgYPaF8A6…
Create two .rmd files for each and
the output from the knit for each.
Exercise 4 Exercise2 R coding
This assignment is for learning some of the basic operations in R
You will want to execute the following R script in R studio.
NOTE: There is a section for you to add your own code please do so.
Here is the r code https://drive.google.com/file/d/1Tpr_aTOwdL0iS2h6i…
Doc version attached below
note: you can run this by going to File in R Studio and choosing to open a file.
You will need to separate the code into Chunks for the rmd file. It is up to you as to how.
Deliverables:
Output from KNIT
.RMD file

Please don’t accept if you don’t have or are not familiar with R Studio
This pr

Please don’t accept if you don’t have or are not familiar with R Studio
This problem examines logistic regression. You will want to review the material on linear regression including the use of logistic regression in R Studio. The date needed is attached Below
Here is a video on how to handle parts a.i to a.iii

DataDeliverable:
RMD and the KNITed output
Run the code below and answer parts d and e.
Code
#NOTE: Prepared with R version 3.6.0
#set the working directory to appropriate folder on your machine, so as to access the data
#files.
#load the required librarie(s)/package(s) for this chapter
#Install the package(s) below once on your machine. To do so, uncomment the
#install.packages line(s) below.
NOTE: DO NOT INCLUDE INSTALL.PACKAGES IN .RMD FILE – YOU WILL GET AN ERROR WHEN YOU KNIT
#install.packages(“caret”)
#install.packages(“MASS”)
library(caret)
library(MASS)
## Financial Condition of Banks.
##The file Banks.csv includes data on a sample of 20 banks. The “Financial
##Condition” column records the judgment of an expert on the financial
##condition of each bank. This outcome variable takes one of two possible
##values-weak or strong-according to the financial condition of the bank. The
##predictors are two ratios used in the financial analysis of banks:
##TotLns&Lses/Assets is the ratio of total loans and leases to total assets
##and TotExp/Assets is the ratio of total expenses to total assets. The target
##is to use the two ratios for classifying the financial condition of a newbank.
##Run a logistic regression model (on the entire dataset) that models the
##status of a bank as a function of the two financial measures provided.
##Specify the success class as weak (this is similar to creating a dummy that
##is 1 for financially weak banks and 0 otherwise), and use the default cutoff
##value of 0.5.
#load the data
bank.df <- read.csv("banks.csv") head(bank.df) #fit logistic regression model and obtain the summary reg<-glm(Financial.Condition ~ TotExp.Assets + TotLns.Lses.Assets, data = bank.df, family = "binomial") summary(reg) reg$coefficients #Coefficients: # Estimate Std. Error z value Pr(>|z|)
#(Intercept) -14.721 6.675 -2.205 0.0274 *
#TotExp.Assets 89.834 47.781 1.880 0.0601 .
#TotLns.Lses.Assets 8.371 5.779 1.449 0.1474
##a Write the estimated equation that associates the financial condition
##of a bank with its two predictors in three formats:
NOTE: You may need to convert the formulas given here into a form that works in R
##a.i derive the logit values – The logit as a function of the predictors
#the function would be in this form
#logit = -14.721 + (89.834 * TotExp.Assets) + (8.371 * TotLns.Lses.Assets)
#PUT the above code in the correct form so that it will run
##a.ii The odds as a function of the predictors
#the function would be in this form
# Odds = e^(logit) = e^(-14.7207 + (89.8321 * TotExp/Assets) +
# (8.3712 * TotLns&Lses/Assets)
#NOTE: You may need to convert the formulas given here into a form that works in R
#for instance substitute “e^” above the R function for e which is exp()
#PUT the above code in the correct form so that it will run
##a.iii The probability as a function of the predictors
#the function would be in this form
# p = 1/(1 + Exp(-(-14.7207 + (89.8321 * TotExp/Assets) +
# (8.3712 * TotLns&Lses/Assets))))
#Convert the above code in the correct form so that it will run – you will need to change the names to match the
#column names in bank.df
#b Consider a new bank whose total loans and leases/assets ratio = 0.6
##and total expenses/assets ratio = 0.11.
#From your logistic regression model,
##estimate the following four quantities for this bank (use R to do all the
##intermediate calculations; show your final answers to four decimal places):
##the logit, the odds, the probability of being financially weak, and the
##classification of the bank (use cutoff = 0.5).
# new record logit value
# you can use matrix multiplication to determine the probability
#this will be same calculation as above
logit <- c(1, 0.11, 0.6) %*% reg$coefficients #or you can use the logit formula above replacing the variables with 1, 0.11, and 0.6 #in this form: logit = -14.721 + (89.834 * TotExp.Assets) + (8.371 * TotLns.Lses.Assets) odds <- exp(-logit) prob <- 1/(1+odds) prob #show your results #> prob
# [,1]
#[1,] 0.5457504
#probability that the new bank is 0.5457 and therefore the predicted class
#for this new bank is 1, or “financially week”.
##c The cutoff value of 0.5 can be used in conjunction with the probability of
##being financially weak. Compute the threshold that should be used if we want
##to make a classification based on the odds of being financially weak, and
##the threshold for the corresponding logit.
###Convert and RUN thhe following code using Cutoff value of p=0.5.
#first determine the based on the probability of 0.5 which is based on the cutoff value
#Odds = (p) / (1-p) = (0.5) / (1-0.5) = 1
(0.6)/(1-0.6)
#If odds > 1 then classify financial status as “weak” (otherwise classify as
#”strong”).
#now determine the Logit value which is the log of the odds
#Logit = ln (odds) = ln (1) = 0
#If Logit > 0 then classify financial status as “weak” (otherwise, classify it
#as “strong”)
#YOU SHOULD GET THIS CONCLUSION
#Therefore, a cutoff of 0.5 on the probability of being weak is equivalent to a
#threshold of 1 on the odds of being weak, and to a threshold of 0 on the logit.
##d Interpret the estimated coefficient for the total loans & leases to
##total assets ratio (TotLns&Lses/Assets) in terms of the odds of being
##financially weak.
#look at how we determine the odds How does the 8.3712 impact the odds?
#in other words assume we only have the (TotLns&Lses/Assets) ratio
#zero out the -14.7207 and the 89.8321
#this will tell the impact of (TotLns&Lses/Assets)
#so if (TotLns&Lses/Assets)= 1 then what is the effect on the odds?
# Odds = e^(logit) = e^(-14.7207 + (89.8321 * TotExp/Assets) +
# (8.3712 * TotLns&Lses/Assets)
#USING THE EQUATION DETERMINE THE EFFECT ON THE ODD
PUT your answer here
##e When a bank that is in poor financial condition is misclassified as
##financially strong, the misclassification cost is much higher than when a
##financially strong bank is misclassified as weak. To minimize the expected
##cost of misclassification, should the cutoff value for classification
##(which is currently at 0.5) be increased or decreased?
PUT your answer here.

Hands-on Exercise Questions:
Write a brief summary of the goals of the Route Vie

Hands-on Exercise Questions:
Write a brief summary of the goals of the Route Views project from the project home page at www.routeviews.orgLinks
What is the network ID of the network to which your university belongs? (In some states, university networks are part of larger state networks)
Show a screenshot (similar to the figure) of the BGPlay query results for your university’s network.
Pick a route originating from your university that passes through at least three autonomous systems. Write the route as a sequence of ASes (e.g., 2905 701 1239 19151 3851).
What are the network names of the ASes selected above?
Pause BGPlay during a route announcement. Show a screenshot as shown in the figure. What is the new route added?
Pause BGPlay during a route withdrawal. Show a screenshot as shown in the figure. What is the route withdrawn?

PLEASE FIRST SELECT PUBLIC COMPANY TO ANALYZE. USING YOUR WEB BROWSER, ANSWER AL

PLEASE FIRST SELECT PUBLIC COMPANY TO ANALYZE. USING YOUR WEB BROWSER, ANSWER ALL OF THE QUESTIONS THAT FOLLOW FOR THATPARTICULAR COMPANY. THE CHOICE OF COMPANY IS UP TO YOU. BUT, PLEASE EMAIL ME TO LET ME KNOW OF YOUR SELECTION AND MAKE SURE THAT YOU HAVE ENOUGH DATA TO ANSWER ALL OF THE QUESTIONS. COMPANY SELECTION IS FIRST COME FIRST SERVE. IT IS A GOOD IDEA TO KEEP THREE OR FOUR COMPANIES IN MIND BECAUSE YOU ARE TO FIND A COMPANY THAT HAS ENOUGH PUBLICALLY AVAILABLE DATA TO ANSWER EACH QUESTION. THERE IS NO NEED TO INCLUDE DATA OR SPECIFIC PROGRAMS USED TO ANSWER QUESTIONS, BUT, IN ALL YOUR RESPONSES, PLEASE INCLUDE EXPLANATIONS OF STEPS CARRIED OUT. IT IS OKAY TO WORK WITH OTHER PEOPLE, BUT YOUR EXPLANATIONS SHOULD BE IN YOUR OWN WORDS.

Please be clear and use as many pages in Power BI as needed to answer the questi

Please be clear and use as many pages in Power BI as needed to answer the questions. Provide a small analysis for each question with a text box. Using LAPD crime data,provide a data-informed understanding of the nature of the crime problem within the target areas and make initial recommendations for action directly connected to the results. Finally, the exercise asks you to make data-informed recommendations for action within a Problem-Oriented Policing context.
LAPD leadership has started a new Violence Reduction Initiative (VRI) focus on three target Divisions: 77th Street, Southwest and Newton. These Divisions experience some of the highest rates of homicide and firearm-related violence in the city. Neighborhood leaders and community organizations based within these areas have demanded action from the city and the VRI is its response. (Will need to download more excel data that needs to be uploaded to Power BI)

Please see the attached documents to complete the assignment.
Textbook (Not Requ

Please see the attached documents to complete the assignment.
Textbook (Not Required but recommended): Cost-Benefit Analysis: Concepts and Practice (4th Edition) by Anthony E. Boardman, David H. Greenberg, Aidan R. Vining, and David L. Weimer, Prentice Hall, Inc., Upper Saddle, NJ, 2011 (ISBN-10: 0137002696). Include additional resources.

Check attachments for instructions for data analysis 3. I have completed data an

Check attachments for instructions for data analysis 3. I have completed data analysis 1 and 2 if you might need what was done you can ask.

Please see the attached documents to complete the assignment.
Textbook (Not Requ

Please see the attached documents to complete the assignment.
Textbook (Not Required but recommended): Cost-Benefit Analysis: Concepts and Practice (4th Edition) by Anthony E. Boardman, David H. Greenberg, Aidan R. Vining, and David L. Weimer, Prentice Hall, Inc., Upper Saddle, NJ, 2011 (ISBN-10: 0137002696). Include additional resources.

Check attachments for instructions for data analysis 3. I have completed data an

Check attachments for instructions for data analysis 3. I have completed data analysis 1 and 2 if you might need what was done you can ask.