# Stats homework using r Mathematics Assignment Help

Stats homework using r Mathematics Assignment Help. Stats homework using r Mathematics Assignment Help.

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STATS HOMEWORK USING R:

#Lab 6

#274-Wilcox (Fall 2019)

#Name:

#Student ID:

rm(list=ls())

source(‘Rallfun-v33.txt’)

#####

#Please name your variables according to the question, otherwise points will be deducted even if the command is correct

#####

#Submit any histogram/density plots

#####

#PART 1-Sampling t-distribution

#1.1) Use the scan( ) function to read in Lab6hw.txt into a variable called pop2. Create a histogram of the data AND describe the distribution.

#1.2) Use the function CI_var_known we wrote in class to get the 95% confidence interval.

#1.3) Use the function CI_var_unknown we wrote in class to get the 95% confidence interval.

#1.4) Draw 500 samples of size 25 from pop2 and put these into a matrix called tsams.

#1.5) Compute the T-scores for each sample and store these into a new vector called tscores.

#1.6) Using a histogram, plot the distribution of T-scores (tscores). Describe the distribution.

################################################

#PART 2-Simulated t-distribution

#2.1) Simulate a distribution of 500 t-scores with 24 degrees of freedom into a variable called tsim using the rt() function.

#2.2) Using a histogram, plot the distribution of simulated T-scores (tsim). Describe the distribution.

#2.3) Using an overlaid density plot, simultaneously show the distributions of the sampled t-scores (tscores) and simulated t-scores (tsim)

#2.4) Based on the plot in 2.3, how do the distributions compare

################################################

#PART 3-Implications for Confidence Intervals

#3.1)When computing confidence intervals, the critical quantile is always derived from the theoretical (simulated) t-distribution, which assumes normality.

# As you discovered in 2.4, the actual distribution of t-scores can be very different from the theoretical one, depending on the population distribution.

# If we ignore the fact that the population may be non-normal, and compute confidence intervals in the usual way,

# what will happen to the probability coverage (i.e. accuracy) of the confidence intervals?

—————————————————————————-

LECTURE NOTES FOR LAB 6

#Lab 6-Contents

#0. Quick review of What we’ve been doing

#1. Confidence Intervals when the Population Variance is Known

#2. Confidence Intervals when the Population Variance is Unknown

#3. Critical values and sample size

#4. Writing Functions to Calculate CI

#5. The T-Distribution

#———————————————————————————

# 0. Quick Review

#———————————————————————————

#By now, you have a bunch of tools in your toolbox to handle data

#Below is a listing of some of the functions we’ve used

#=======================#===============================#===========================================================================#

# function # Meaning # Example #

#=======================#===============================#===========================================================================#

# c(#,#,…)# Collection of Numbers# vect1=c(1,2,3,4)#

## Usually to assign to a vector # #

#———————–#——————————-#—————————————————————————#

# cbind()# Combine vectors by Columns # newC=cbind(vect1, vect2)#

# rbind() # Combine vectors by Rows# newR=rbind(vect1, vect2)#

#———————–#——————————-#—————————————————————————#

# dat[i,j]# Retrieving values from data # dat[3,25] #Row 3, Column 25#

## i=row, j=column# dat[ ,”bmi”] #All Rows, variable bmi#

#———————–#——————————-#—————————————————————————#

# fix()# Visualize data # fix(dat)

#———————–#——————————-#—————————————————————————#

# scan() # Read data into a vector

#———————–#——————————-#—————————————————————————#

# source()# Read in Dr. Wilcox Source Code# source(file.choose())#

#———————–#——————————-#—————————————————————————#

# dim()# Dimensions of Data (rows,cols)# dim(mydata)#

# names()# Variable names in Data# names(mydata)#

# str()# Data properties# str(mydata)#

# tail()# Show last 6 rows# tail(mydata)#

#———————–#——————————-#—————————————————————————#

# which()# Used to identify rows in a # rows=which(mydata\$bmi < 15)#

## data file. Often for recoding # mydata[rows, “bmi”] = NA#

#———————–#——————————-#—————————————————————————#

# min()# Min value in variable# min(mydata\$bmi, na.rm=T)#

# max()# Max value in variable# max(mydata\$bmi, na.rm=T)#

# range()# Min and Max value in variable # range(mydata\$bmi, na.rm=T)#

#———————–#——————————-#—————————————————————————#

# table()# Frequencies of variable# table(mydata\$race)#

#———————–#——————————-#—————————————————————————#

# mean()# mean of variable# mean(mydata\$bmi, na.rm=T)#

# median()# median of variable# median(mydata\$bmi, na.rm=T)#

# tmean()*# Trimmed Mean of Variable# tmean(mydata\$bmi, tr=0.2, na.rm=T)#

# sd()# Standard Deviation of Variable# sd(mydata\$bmi, na.rm=T)#

# var()# Variance of variable# var(mydata\$bmi, na.rm=T)#

#———————–#——————————-#—————————————————————————#

# boxplot()# Boxplot of Variable# boxplot(mydata\$bmi)#

# hist()# Histogram of Variable# hist(mydata\$bmi)#

# density()# Density plot# plot(density(mydata\$bmi, na.rm=T))#

#———————–#——————————-#—————————————————————————#

# dbinom(k,n,p)# Prob of EXACTLY k Successes # dbinom(5,20,0.45) #

### Prob of EXACTLY 5 success out of 20 given prob of 0.45#

# pbinom(k,n,p)# Prob of <= k Success# pbinom(5,20,0.45) #

###Prob of <= 5 success out of 20 given prob of 0.45#

#———————–#——————————-#—————————————————————————#

# pnorm(val,mean,sd) # Prob of < val on normal dist # pnorm(50,65,5) #Prob of scoring <50 on a norm dist of mean 65 and sd 5 #

# qnorm(p,mean,sd)# Value where the Prob of val=p # qnorm(0.2,65,5) #The score where the prob for < score is 0.20#

#———————–#——————————-#—————————————————————————#

# rnorm(N,mean,sd)# Simulate normal distribution # pop=rnorm(5000,0,1) #

###a random variable pop with N=5000 and a mean of 0, sd of 1 #

# runif(N)# Simulate uniform distribution # popunif=runif(5000) #

###a random variable popunif with N=5000 distributed uniformly#

# cnorm(n, epsilon, k)* # Contaminated Normal dist# cpop=cnorm(5000, 0.4, k=3) #

### A standard normal (mean=0, sd=1) for 1-epsilon (60%) of the data #

### A normal of mean=0 and sd=k (eg. 3) for epsilon (40%) of the data #

#———————–#——————————-#—————————————————————————#

# sample(data, size) # Take a random sample of data # x=sample(pop, size=50, replace=T) #

### Take a sample from pop of size 50 and store in x#

#———————–#——————————-#—————————————————————————#

# matrix(,ncol=#,nrow=#)# Create a blank matrix# mysams=matrix(, ncol=200, nrow=50) #

###Create a blank matrix called mysams of 200 columns and 50 rows#

#———————–#——————————-#—————————————————————————#

# for (i in START:END) {# For Loop to loop over numbers # for (i in 1:200) {#

# }## mysams[,i]=sample(pop, size=50, replace=T)#

### }#

#———————–#——————————-#—————————————————————————#

# apply(data,2,FUN)# Apply a FUNction to columns # sammeans=apply(mysams, 2, FUN=mean) #

###Store column means of mysams in variable called sammeans#

#———————–#——————————-#—————————————————————————#

#NOTE: Functions with a star (*) after are from Dr. Wilcox’s source code and must have the source code read in using the source() command first

#———————————————————————————

# 1. Confidence Intervals when the Population Variance is known.

#———————————————————————————

#The formula for a CI is:

# LB: mean – c * (sd/sqrt(N))

# UB: mean + c * (sd/sqrt(N))

#And we can compute c based upon the quantiles

#from a standard normal distribution where

# c = qnorm(1-(alpha/2)).

# Remember for an 80% CI, alpha is 1-0.8 = 0.2;

# for a 65% CI, alpha = 1-0.65 = 0.35 etc.

#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#

# EXERCISE 1-1: Compute a 95% confidence interval for mean=25,

# population variance=25, N=20

#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#

mean1=25

var1=25

N=20

sd1=sqrt(var1)

alpha=1-.95

c= qnorm(1-(alpha/2))

LB= mean1 – c * (sd1/sqrt(N));LB

UB= mean1 + c * (sd1/sqrt(N));UB

#??????????????????????????????????????????????????????????????#

#Thought Question 1: For the above, why do we use alpha/2 to

# find the quantile. Or rather, why am I using qnorm(0.975) to

# find c rather than qnorm(0.95)

#??????????????????????????????????????????????????????????????#

#———————————————————————————

# 2. Confidence Intervals when the Population Variance is UNKNOWN.

#———————————————————————————

#In reality, rarely do we know the population variance.

#Instead what we have is our sample variance.

#While the formula for a CI is the same as we used before:

# LB: mean – c * (sd/sqrt(N))

# UB: mean + c * (sd/sqrt(N))

#However, the way we calculate our critical value ‘c’ will now

# be from the quantiles of a T-distribution rather than a normal.

#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#

# T Dist Probability (T <= t): pt(q, df)

# T Dist Quantile:qt(p, df)

#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#

# The function pt() computes the probability that

# T is <= some quantile q given degrees of freedom (df)

# The function qt() computes the quantile for T at the probability

# value of p given degrees of freedom (df)

# What are degrees of freedom?

#For now, just know that df=N-1

#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#

#Exercise 2-1:

# A) Calculate the probabilty that T is <= 1 given 10 df

# B) Calculate the 0.975 quantile with 10 df

# C) What is the sample size for 10 df

#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#

#A)

pt(1,10)

#B)

qt(0.975,10)

#C)

N=11

#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#

#EXERCISE 2-2:

# A) Compute a 95% confidence interval for mean=25,

# SAMPLE variance=25, N=20

# B) How does the Lower and Upper Bounds compare to those

# found in Exercise 2-1 then the Population variance was known

#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#

#A)

mean2=25

var2=25

N2=20

sd2=sqrt(var2)

alpha2=1-.95

df=N2-1

c2= qt(1-(alpha2/2),df)

LB2= mean2 – c2 * (sd2/sqrt(N2));LB2

UB2= mean2 + c2 * (sd2/sqrt(N2));UB2

#B)

#For the normal

# LB=22.80869

#UB=27.19131

#For the T-dis we got

#LB2=22.65993

#UB2=27.34007

#———————————————————————————

# 3. Critical Values and Sample Size

#———————————————————————————

# Let’s think more about, why the Confidence interval was wider

# when the sample variance was unknown?

# We’ll start by computing the critical values for a variety

# of sample sizes for a 95% CI

# When the population variance is known, we use the qnorm() function

# to find the critical value

qnorm(0.975)

# Which is 1.959964. Notice that it doesn’t matter what sample size I have, the critical value is the same.

# I’m going to use a loop to loop over values of

# N=20, 50, 100, 200, 1000, and 10000

# to see what the critical values would be at various sample sizes

# when the population variance is unknown

for (ii in c(25,50,100,200,1000,10000)) {

qtval=qt(0.975, ii-1)

print(qtval)

}

#??????????????????????????????????????????????????????????????#

#Thought Question 2: What do you notice about the relationship

# between the critical values and sample size?

#??????????????????????????????????????????????????????????????#

#———————————————————————————

# 4. Writing Functions to Calcuate the Confidence Intervals

#———————————————————————————

#To calculate the confidence interval easier, we can write our own function which figures out the CI for us

#I’ll start by writing the function for the confidence interval when the population variance is Unknown

CI_var_unknown = function(data, ci) {

#data=c(1,2,3,4,5,6)

#ci=.95

mean=mean(data)

N=length(data)

df=N-1

sd=sd(data)

alpha=1-ci

c=qt(1-(alpha/2), df)

LB=mean – c * (sd/sqrt(N))

UB=mean + c * (sd/sqrt(N))

output=list(LB, UB)

names(output)=c(“Lower Bound”, “Upper Bound”)

print(output)

}

#So, now if I have data from a normal distribution (x) of mean=0 and sd=1:

x=rnorm(500, 0, 1)

# I can use my new function to compute the 95% confidence interval

CI_var_unknown(data=x, ci=0.95)

#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#

#Exercise 5-1:

# A) Write a function called CI_var_known for computing the confidence intervals when the population

# variance is known

# B) Compute the 90% CI for the data x using your new function.

#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#

#A)

CI_var_known = function(data, ci) {

#data=c(1,2,3,4,5,6)

#ci=.95

mean=mean(data)

N=length(data)

sd=sd(data)

alpha=1-ci

c=qnorm(1-(alpha/2))

LB=mean – c * (sd/sqrt(N))

UB=mean + c * (sd/sqrt(N))

output=list(LB, UB)

names(output)=c(“Lower Bound”, “Upper Bound”)

print(output)

}

#B)

CI_var_known(data=x, ci=0.90)

#———————————————————————————

# 5. The T-Distribution

#———————————————————————————

#Let’s start to understand the T distribution by reading data

# that we will use as our population

#The scan() function is used to read in external text files as vectors

pop=scan(‘Lab6.txt’)

#And a quick look at the data

hist(pop); mean(pop); sd(pop)

# There is a formula for a T-score such that:

# T = (SampleMean – PopMean)/(SampleSD/sqrt(SampleN))

#What we will do is take multiple samples from our population,

#compute the T score for each sample, and then compare

#to a random T distribution

#Let’s take 500 samples of size 20

sams = matrix(, ncol=500, nrow=20)

for (ii in 1:500) {

sams[,ii] = sample(pop, size=20, replace=TRUE)

}

#’sams’ now contains 500 random samples from the population,

#now I will use the T-score formula to compute a T score for each sample

#I will store my 500 T scores into a numeric vector called samtscores

samtscores=numeric(500)

for (jj in 1:500) {

samtscores[jj]= ( mean(sams[,jj]) – mean(pop) ) / ( sd(sams[,jj]) / sqrt(20) )

}

#Let’s take a quick look at the distribution of the T-scores

hist(samtscores)

#Lastly, I’d like to compare what we have to a simulated T -distribution

#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#

# Random T Score: rt(n, df)

#

#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#

# The function rt() will create n random T scores from a t-distribution

# with n-1 df

simt=rt(500, 19)

# We can graphically compare the two distributions by using an overlaid density plot

plot(density(samtscores))

lines(density(simt), col=”red”)

#??????????????????????????????????????????????????????????????#

#Thought Question 3: How does our simulated t scores compare

# to the actual t-scores from our samples?

#??????????????????????????????????????????????????????????????#

Stats homework using r Mathematics Assignment Help[supanova_question]

## Define ethics and address any attempts the U.S. government has made to legislate business ethics. Have the government’s attempts to legislate business ethics been effective? Why or why not? Business Finance Assignment Help

Your response to the discussion prompt should contain a minimum of 500 words. Your response should be formatted in APA style and reference the text book as well as one other authored source.

Follow the following writing requirements for all of your discussion prompt responses:

WRITING REQUIREMENTS FOR ALL ASSIGNMENTS

• References MUST be cited within your paper in APA format. Your reference page and citations must match 100%.
• Always include a cover page and reference page with all submissions
• Your paper must have headings in it. For discussion posts Introduction, Prompt/Question, and Conclusion will suffice as headings.
• Provide the EXACT web link for all online sources – do not provide just the home page, but the EXACT LINK – I check all sources
• No abbreviations, no contractions – write formally
• Write in the third person formal voice (no first or second person pronouns)
• Write MORE than the minimum requirement of the word count assigned
• As always, the word count is ONLY for the BODY of the paper – the cover page, reference page, and / or Appendix (if included) do not count towards the word count for the paper
• Indent the first line of each new paragraph five spaces
• Refer to the example APA paper in the getting started folder under the content tab if you need an example. Also, a power is provided under the information tab that addresses APA format.
• Use double-spacing / zero point line spacing, a running header, page numbers, and left justify the margins.

[supanova_question]

## Week 8; simple paper Business Finance Assignment Help

Instructions: Based on the research you have conducted in the course and the organization you chose in Week 4, create a consultancy report.

Considering your chosen organization, perform an analysis that identifies areas of concern.

Your consultancy report must include: your analysis findings, identified concerns and/or weaknesses, your recommendations, a proposed action plan.

Support your report with at least seven scholarly resources. In addition to these specified resources, other appropriate scholarly resources, including older articles, may be included. The paper should be thirteen pages, not including title and reference pages Your report should demonstrate thoughtful consideration of the ideas and concepts presented in the course by providing new thoughts and insights relating directly to this topic.

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## Stragetic Planning Business Finance Assignment Help

Read Case 20 on page 512 in the textbook. Answer the discussion questions listed below. Each question should be answered in paragraph form. (Not double spaced)

1.Outline the steps you would take to initiate a strategic planning process for improving the ED information system. How will you ensure that this plan is in alignment with the hospital’s and department’s overall strategic plans?

2. Multiple factors have contributed to the current state of the ED at Newcastle Hospital and are listed in the case. Which of these do you think will be the most difficult to overcome? Why?

3. The new CEO has good insights into the ED issues. Assuming that his assessment of the situation is accurate, discuss how his continued support could affect the outcome of any ED IS strategic plan.

4. Assume the CEO has appointed you to spearhead the ED IS strategic planning effort. What are the first steps you will take? Outline a general plan of action for the next three months. Indicate, by title, whom you would involve in the process. Explain your choices.

file:///C:/Users/kassa/Documents/HealthCareInformationSystems-Textbook09.23.19.pdf

[supanova_question]

## Instructions

Throughout this course, you have been tracking the financial performance of your organization in CAPSIM and you have been looking at performance reports such as the Balanced Scorecard and the financial information in the Courier and related financial statements.

For this Signature Assignment, you will write a paper where you analyze your company’s performance concerning the following:

1. Describe the industry conditions that existed when you started.
2. Compare the state of the company after the end of the eight rounds with the state of the company prior to starting. Is the company in better or worse shape? How so?
3. Examine your functional decisions in Finance, Human Resource, TQM, R&D, and Marketing, and then determine what you would change to improve each of those areas.
4. Discuss the basic strategy you used in your company (these are the Generic Strategies that you chose in Week 2). Did you adjust these strategies during the rounds, and if so, what adjustments did you make?
5. Summarize your SWOT and PESTEL analysis and indicate if you would add or subtract anything from the analysis given the performance of your company.
6. Consider the results of your SWOT and PESTEL analysis, and subsequent performance of your company. What, if anything would you have done differently if you had this company to run all over again?

Be specific in all answers – do not say “I had large profits in Round 1”. Indicate that you “had profits of \$4,565,000 in Round 1” or whatever the case may be.

Length: 10 – 12 pages, not including title page and references

Your presentation should demonstrate thoughtful consideration of the ideas and concepts presented in the course and provide new thoughts and insights relating directly to this topic. Your response should reflect scholarly writing and current APA standards. Be sure to adhere to Northcentral University’s Academic Integrity Policy.

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[supanova_question]

## Help with Theory of Computation Mathematics Assignment Help

Please completed the problems attached in a word document with analysis. Please find the attached text book as well.

Note: All homework and exam submissions must be typed and submitted in a Word Document (.DOC / .DOCX), including responses to any prompts or assignments. Hand-written or image submissions will not be accepted, except in the case for complex diagrams. In the case of complex diagrams, you may hand draw them and import them into your digital document (typically done by taking a picture, cropping it, and organizing your diagrams with the responses). You must write your name and date under the diagram as proof of their originality and your ownership. Any hand-drawn image or diagram that does not have your hand-written name will not be graded.

Chapter 9.1 Exercise – 2,3,4,5,6,7

Chapter 12.1 Exercise – 1,4,6

Chapter 12.2 Exercise – 1,2,3

Chapter 12.3 Exercise – 1,4,5

Help with Theory of Computation Mathematics Assignment Help[supanova_question]

## EMERGENCY!!! Assignment 1: Financial Statement/Audit Report Review Business Finance Assignment Help

Using the city of cary, north Carolina.

Write a three- to five-page paper in which you do the following:

1. Compare and contrast the Comprehensive Annual Financial Report (CAFR) of the selected local government entity with the government entity identified in the Week 1 homework. In your comparison, include the following:

1. The publication method of the CAFR;
2. Audit and budget information in the CAFR;
3. The type of audit report issued; and
4. The existence or nonexistence of an internal audit function within the government entity.
2. Prepare the analysis for the selected local government entity, including information on the introduction, financial section, and statistical section prepared in the Continuing Problem CAFR from Chapter 2.
3. Analyze the methods used by the selected local government entity in comparing the budget-to-actual reports. Your analysis should include an evaluation of the basis of accounting used for the budget and financial statements.
4. Analyze the sources of revenue for the selected local government. Your analysis should include information on both governmental and business-type activities of the government. In your report, be sure to examine the following:

1. Property taxes and how they are accounted for;
2. Other sources identified as primary revenue for the entity;
3. Deferred revenue;
4. Year-to-year variations in the tax levels of income;
5. Various management discussion and analysis items of note; and
6. Information about the general fund.

• This course requires use of new Strayer Writing Standards (SWS). The format is different than other Strayer University courses. Please take a moment to review the SWS documentation for details.
• Be typed, double-spaced, using Times New Roman font (size 12), with 1-inch margins on all sides; citations and references must follow SWS or school-specific format. Check with your professor for any additional instructions.
• Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required assignment page length.

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## Anatomical Analysis Chart Science Assignment Help

A.Anatomical analysis (in chart form for the outline*) (40%)

1.Joints-must include:

a) spine (cervical & thoracic/lumbar)

b) pelvic girdle

c) R &L shoulder girdle

d) R & L elbow

f) R & L wrist

g) R & L hip

h) R & L knee

i) R & L talocural

j) R & L subtalur

2.Starting and ending positions (events)

3.Joint motion (phases)

4.Segment moved

5.Plane and axis

6.Force producing motion (please refer to iLearn chapter #1)

7.Prime movers

8.Contraction type

*There is an example on how it should look and be formatted* *There is also a doc that will explain on the activity we are focusing on which is shooting a free throw no jumping* *The only thing you have to do is the anatomical analysis*

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## A Class Divided and the Invisible Knapsack Review Assignment Help

Imagine that your principal has come to you and stated that the
district is interested in hosting a professional development workshop
for educators to help them broaden their cultural competence, improve
their family-teacher relationships, and enhance educational
experiences for all students. The district is proposing using the
materials from “A Class Divided” and “White Privilege:
Unpacking the Invisible Knapsack” for the workshop. Both of these
resources are dated, but are often used in schools to introduce
faculty to social justice issues that affect educators.

essay either in support of or against the use of the material in these
resources. In your essay, include specific examples that discuss
whether the materials could be used to help individuals broaden their
cultural competence, build stronger relationships, and create more
relevant educational experiences. If you argue that the materials
should not be used, offer reputable alternatives for your principal to
consider. Be sure to include links and descriptions of the alternative resources.

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## Reflection Paper Writing Assignment Help

### Final Reflection Instructions (this is included as part of the survey)

The survey at the link in the assignment below is the actual assignment. The reflection will be included within the survey. This is simply an informational piece detailing what you should include in your reflection that is part of the survey.

Nearing the end of the semester, it is now time for students to reflect on the knowledge obtained in their course(s) and determine the effectiveness of incorporating real-world experience into our academic curriculum. Please complete the survey/reflection to the best of your ability.

Students should;

• Be able to apply knowledge and theory gained in their courses of study within current workplace or in their future employment.
• Be able demonstrate the application of theory to workplace in written form.
• Be able to identify the benefits of incorporating real-world experience into an academic program.

Courses taken: Organ leadership and legal research compliance, I work in the IT vertical of health care and manage technology as an analyst.

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Stats homework using r Mathematics Assignment Help

Stats homework using r Mathematics Assignment Help