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Assume the world population is 7 Billion, 20% of all people live in China, and one-half of Chinese people are women. Then how many people are Chinese Women?
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Homework Assignment 2 5
I want the completed assignment handed in electronically through myCourses as a PDF document. You can upload the completed ...
Homework Assignment 2 5
I want the completed assignment handed in electronically through myCourses as a PDF document. You can upload the completed assignment through the
Week 8 Project
Write a minimum 4 page report explaining the results from your analysis of the ROI of Business Majors and Engineering Majo ...
Week 8 Project
Write a minimum 4 page report explaining the results from your analysis of the ROI of Business Majors and Engineering Majors. Use your results from each week’s assignment to make this report.This report should include:A detailed description of the data used in this analysis – specifically explain what the data in each column represents.A detailed explanation of any information learned about:School Type for each major; The Cost for each major;The 30 Year ROI for each major;The Annual % ROI for each major.Explanations should be supported with the results obtained from your work in previous weeks.Finally, answer the following questions and support your answer with your results.Does there appear to be a particular major that gives a better ROI? Why or Why not? Given that we are using statistical inference to make our conclusions, is it guaranteed that the major you choose that gave a better ROI for this sample will always have a better ROI than the other major? Explain your reasoning
Harvard Stats Homework Using R Lab 5 Help
stats homework using RHomework- lab 5:#The central limit theorem (CLT) states that as the sample size gets sufficiently la ...
Harvard Stats Homework Using R Lab 5 Help
stats homework using RHomework- lab 5:#The central limit theorem (CLT) states that as the sample size gets sufficiently large, the distribution of the sample means will be normally distributed.#In addition, the CLT has been used to justify the fact that for many of our statistics we rely upon computing the mean (not median or trimmed mean) of our samples#There are a few problems with the CLT. #1) How large of a sample is needed#2) It seems that our experiments with the contaminated normal may contradict this.#In this homework assignment you will investigate the CLT further. #PART 1 - The Central Limit Theorem under Normality.#1.1) Simulate a standard normal population of 1 million people called pop1 #1.2) Draw 5000 samples of size 20 and put these in sam20. Draw 5000 samples of size 50 and put these in sam50 .#1.3) Create variables called sam20means and sam50means that contains the means of the samples . Use a density plot to show the sampling distribution of the means for sam20means and sam50means together#1.4) Compare the Standard Error (SE) of the sampling distributions. Which sample size creates better estimates of the population mean (ie. has the lowest SE)? #PART 2 - The Central Limit Theorem under Non-Normality#2.1) Simulate a contaminated normal population using cnorm() of 1 million people called pop2 where 30% (epsilon=0.3) of the data have an SD of 30 (k=30) .#2.2) Draw 5000 samples of size 30 and put these in sam30. Draw 5000 samples of size 100 and put these in sam100.#2.3) Create variables called sam30means, sam30tmeans, sam100means, sam100tmeans that represent the means AND trimmed means for the samples. #2.4) Use a density plot to show the sampling distribution of the means and trimmed means for these variables.#2.5) Compare the Standard Error (SE) of the sampling distributions. #2.6) Which would be better here: a larger sample size using the mean as the location estimator OR a smaller sample using the trimmed mean? #2.7) Which location estimator performs the best, regardless of sample size?-------------------------------------------------------------------------------------------------------------------------------------------------Lab 5 lecture notes:#Lab 5-Contents# 1. Sampling Distribution of the Mean, # Median, and Trimmed Mean under Normality# 2. Sampling Distribution of the Mean, # Median, and Trimmed Mean under Non-Normality# 3. The Central Limit Theorem# Last week we saw that when we had a Normal or Uniform population, # that the means of random samples taken from that population #were normally distributed.#Today we are going to investigate the distributions of the mean,#median, and trimmed mean from samples coming from Normal # and non-normal populations.#---------------------------------------------------------------------------------# 1. Sampling Distribution of the Mean, Median, # and Trimmed Mean under Normality#--------------------------------------------------------------------------------- #Let's start by generating a standard normal distribution (mean=0, SD=1) for 1 million subjectspop1 = rnorm(1000000, mean=0, sd=1) #We will use this as our population from a normal distribution #*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*##EXERCISE 1-1: #A) Find the mean, median, trimmed mean (using tmean() ), and sd of pop1#B) Draw a density plot of pop1#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*# #A)mean(pop1); median(pop1); tmean(pop1); sd(pop1) #B) plot(density(pop1)) #Like we did last week, we are going to want to take random samples # from our population and then compute a measure of central tendency #(eg. mean, median, trimmed mean) for each sample and examine #the distribution of this measure. #We are going to take 5000 samples of 20 subjectssam1 = matrix(, ncol=5000, nrow=20)#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*## EXERCISE 1-2: Use a loop to draw 5000 samples of size 20 from pop1 # an place the samples in sam1#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#for (ii in 1:5000) {sam1[ , ii] = sample(pop1, 20, replace=TRUE)} # Now that we have our datafile containing all 5000 samples (ie. sam1) # we can begin to create variables for each of our location measures #I'll start us off with the meansam1means = apply(sam1, 2, mean) # number 2 = work in the columns rather than rows#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*## EXERCISE 1-3: Use the apply function to generate # the variables sam1meds (medians) and sam1tmeans (trimmed mean)#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#sam1meds = apply(sam1, 2, median)sam1tmeans = apply(sam1, 2, tmean) #Let's look at the distributions of each of these location estimatorsplot(density(sam1means))lines(density(sam1meds), col="red")lines(density(sam1tmeans), col="blue")abline(v = mean(pop1), lty=2) #Add in a line for the pop1 mean#??????????????????????????????????????????????????????????????##Thought Question 1: Which location estimator performs the best #for data coming from a normal population? Why?#??????????????????????????????????????????????????????????????# # One of the ways we can determine which location estimator # performs the best is by looking at the standard deviation # of the estimator accross all the samples. # The estimator with the lowest SD will have the least amount # of variability accross the samples. # A more common name for the standard deviation of the location # estimator is called the Standard Error or SE#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*## EXERCISE 1-4: Find the Standard Error of the sample means,# medians, and trimmed means. Based upon the SE, which # location estimator is the best for samples coming from# a normal population?#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*# sd(sam1means); sd(sam1meds); sd(sam1tmeans) #The mean performs the best.# In real life, we generally cannot go out an collect multiple samples # from a population, so we compute the Standard Error using a formula: # SE = sd(sample) / sqrt(sample N)#---------------------------------------------------------------------------------# 2. Sampling Distribution of the Mean, Median, # and Trimmed Mean under Non-Normality#---------------------------------------------------------------------------------# Normal distributions generally have very few outliers, # however when outliers begin to occur more frequently so of the # basic assumptions about normal distributions are no longer true # (as we are about to see). # One distribution that is like a normal distribution,# but with more outliers is called a mixed or contaminated # normal distribution and it is a result of two populations mixing together. #*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*# #EXAMPLE 1: "a" will be a mix of TWO populations 1: with SD=1 and 2: with SD=2 a=c(rnorm(5000, 0, 1), rnorm(5000, 0, 2)) #Let's compare this to b, which is from ONE population but with the same parameters of a b=rnorm(10000, mean(a), sd(a))plot(density(a))lines(density(b), col="red") #*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*##??????????????????????????????????????????????????????????????##Thought Question 2: How are a and b from Example 1 different?#??????????????????????????????????????????????????????????????##Thankfully, rather than having to create contaminated normal distributions the hard way, we can just use#a function provided to us by Dr. Wilcox called cnorm()#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##Contaminated/Mix Normal Distribution: cnorm(n, epsilon=0.1, k=10)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##Let's look at the options for the contaminated normal distribution:#cnorm() combines two normal distributions: #1) A standard normal (mean=0, sd=1) for 1-epsilon % of the data #2) A normal of mean=0 and sd=k for epsilon % of the data #If we were trying to re-create the variable a we made in example 1 we would have to do:z=cnorm(10000, epsilon=0.5, k=2)plot(density(a))lines(density(z), col="blue")#Which looks very very similar to a! #Let's create a second population called pop2 from a contaminated normal distributionpop2 = cnorm(1000000, epsilon=0.1, k=10) #The mean, sd, and plot of which are:mean(pop2); sd(pop2); plot(density(pop2))#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*##EXERCISE 2:#A) Create an empty matrix called sam2 to contain 5000 samples #of 20 observations each#B) Populate sam2 with 5000 random samples of size 20 from pop2#C) Compute the mean (sam2means), median (sam2meds), #and trimmed mean (sam2tmeans) for each sample#D) Create an overlaid density plot of each sample WITH the pop2 #mean as a verticle line#E) Find the SE of each location estimator#F) Based upon the SE, which location estimator is the best # for samples coming from a contaminated normal distribution#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*# #A) #B) #C) #D) #E) #F)#---------------------------------------------------------------------------------# 3. The Central Limit Theorem#--------------------------------------------------------------------------------- #We've discovered a few things today: #1) When a population comes from a normal distribution, # then mean will be the best location estimator of the samples#2) When a population comes from a mixed/contaminated normal distribution, # the trimmed mean is the best location estimator# These observations are related to the Central Limit Theorem (CLT)# that is discussed in Section 5.3 of the book (page 85)# The CLT states that as the sample size gets sufficiently large, # the distribution of the sample means will be normally distributed.# We saw a demonstration of this last week when we looked at the means # from the unifom distribution.# The CLT has been used to justify the fact that for many of our statistics# we rely upon computing the mean (not median or trimmed mean) of our samples#There are a few problems with the CLT. #1) how large of a sample do we need? #2) It seems that our experiements with the contaminated normal may contradict this.#In the homework you will investigate this further
Create Outline - Finer Diner Desert Consultant
Finer Diner Dessert ConsultantFiner Diner has hired you as a consultant to determine how to increase dessert sales.Data fr ...
Create Outline - Finer Diner Desert Consultant
Finer Diner Dessert ConsultantFiner Diner has hired you as a consultant to determine how to increase dessert sales.Data from Finer Diner, Kim’s Kitchen, and Pete’s Place was used to find out if there is a difference in the number of calories in their desserts. Set alpha to 0.05 and determine if there is a difference in the mean number of calories.RestaurantFiner DinerKim’s KitchenPete’s PlaceDessert 14201006880Dessert 2570852800Dessert 36361152508Dessert 43771255785Dessert 52581366702Use your findings to complete this exercise. Create an outline in which you:Identify ways you can market desserts for the Finer Diner to give them a comptetive edge over their competition,Illustrate potentional changes in the lineup of desserts at Finer Diner that could solicit more sales.Propose marketing ideas directed specifically at the competition’s customers in order to get them to try desserts at the Finer Diner.Format any references and citations in APA format.Submit assignment in a Microsoft®Word
ITEC 8437 WU Inferential Statistics One Sample T Test Male Professors Age Essay
A t test examines if two means (averages) are reliably different from each other; that is, whether any significant differe ...
ITEC 8437 WU Inferential Statistics One Sample T Test Male Professors Age Essay
A t test examines if two means (averages) are reliably different from each other; that is, whether any significant differences exist between the means of samples from different populations. For example, to what extent is the output of a local office which applied an IT solution different than the output of other local offices which did not apply the IT solution? Also, if you wanted to test whether the average IQ score of a group of students differs from 100, you may use a t test. A t test does more than just compare means; as an inferential statistic, t tests allow researchers to make inferences about the populations beyond their sample data.For this Assignment, you will use the IT Security database and conduct two t tests: (a) one-sample t test and (b) paired-samples t test and prepare an APA results write-up for each test....thank you
Ashford University Golf Club Manufacturing Worksheet
A golf club manufacturer is trying to determine how the price of a set of clubs affects the demand for clubs. The attached ...
Ashford University Golf Club Manufacturing Worksheet
A golf club manufacturer is trying to determine how the price of a set of clubs affects the demand for clubs. The attached file contains the price of a set of clubs and the monthly sales.
Assume the only factor influencing monthly sales is price. Fit the following three curves to these data: linear (Y = a + bX), exponential (Y = abX), and multiplicative (Y = aXb). Which equation fits the data best?
Interpret your best-fitting equation.
Using the best-fitting equation, predict sales during a month in which the price is $470.
Provide a short description of the tools used and why this was the "best-fitting equation"
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Homework Assignment 2 5
I want the completed assignment handed in electronically through myCourses as a PDF document. You can upload the completed ...
Homework Assignment 2 5
I want the completed assignment handed in electronically through myCourses as a PDF document. You can upload the completed assignment through the
Week 8 Project
Write a minimum 4 page report explaining the results from your analysis of the ROI of Business Majors and Engineering Majo ...
Week 8 Project
Write a minimum 4 page report explaining the results from your analysis of the ROI of Business Majors and Engineering Majors. Use your results from each week’s assignment to make this report.This report should include:A detailed description of the data used in this analysis – specifically explain what the data in each column represents.A detailed explanation of any information learned about:School Type for each major; The Cost for each major;The 30 Year ROI for each major;The Annual % ROI for each major.Explanations should be supported with the results obtained from your work in previous weeks.Finally, answer the following questions and support your answer with your results.Does there appear to be a particular major that gives a better ROI? Why or Why not? Given that we are using statistical inference to make our conclusions, is it guaranteed that the major you choose that gave a better ROI for this sample will always have a better ROI than the other major? Explain your reasoning
Harvard Stats Homework Using R Lab 5 Help
stats homework using RHomework- lab 5:#The central limit theorem (CLT) states that as the sample size gets sufficiently la ...
Harvard Stats Homework Using R Lab 5 Help
stats homework using RHomework- lab 5:#The central limit theorem (CLT) states that as the sample size gets sufficiently large, the distribution of the sample means will be normally distributed.#In addition, the CLT has been used to justify the fact that for many of our statistics we rely upon computing the mean (not median or trimmed mean) of our samples#There are a few problems with the CLT. #1) How large of a sample is needed#2) It seems that our experiments with the contaminated normal may contradict this.#In this homework assignment you will investigate the CLT further. #PART 1 - The Central Limit Theorem under Normality.#1.1) Simulate a standard normal population of 1 million people called pop1 #1.2) Draw 5000 samples of size 20 and put these in sam20. Draw 5000 samples of size 50 and put these in sam50 .#1.3) Create variables called sam20means and sam50means that contains the means of the samples . Use a density plot to show the sampling distribution of the means for sam20means and sam50means together#1.4) Compare the Standard Error (SE) of the sampling distributions. Which sample size creates better estimates of the population mean (ie. has the lowest SE)? #PART 2 - The Central Limit Theorem under Non-Normality#2.1) Simulate a contaminated normal population using cnorm() of 1 million people called pop2 where 30% (epsilon=0.3) of the data have an SD of 30 (k=30) .#2.2) Draw 5000 samples of size 30 and put these in sam30. Draw 5000 samples of size 100 and put these in sam100.#2.3) Create variables called sam30means, sam30tmeans, sam100means, sam100tmeans that represent the means AND trimmed means for the samples. #2.4) Use a density plot to show the sampling distribution of the means and trimmed means for these variables.#2.5) Compare the Standard Error (SE) of the sampling distributions. #2.6) Which would be better here: a larger sample size using the mean as the location estimator OR a smaller sample using the trimmed mean? #2.7) Which location estimator performs the best, regardless of sample size?-------------------------------------------------------------------------------------------------------------------------------------------------Lab 5 lecture notes:#Lab 5-Contents# 1. Sampling Distribution of the Mean, # Median, and Trimmed Mean under Normality# 2. Sampling Distribution of the Mean, # Median, and Trimmed Mean under Non-Normality# 3. The Central Limit Theorem# Last week we saw that when we had a Normal or Uniform population, # that the means of random samples taken from that population #were normally distributed.#Today we are going to investigate the distributions of the mean,#median, and trimmed mean from samples coming from Normal # and non-normal populations.#---------------------------------------------------------------------------------# 1. Sampling Distribution of the Mean, Median, # and Trimmed Mean under Normality#--------------------------------------------------------------------------------- #Let's start by generating a standard normal distribution (mean=0, SD=1) for 1 million subjectspop1 = rnorm(1000000, mean=0, sd=1) #We will use this as our population from a normal distribution #*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*##EXERCISE 1-1: #A) Find the mean, median, trimmed mean (using tmean() ), and sd of pop1#B) Draw a density plot of pop1#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*# #A)mean(pop1); median(pop1); tmean(pop1); sd(pop1) #B) plot(density(pop1)) #Like we did last week, we are going to want to take random samples # from our population and then compute a measure of central tendency #(eg. mean, median, trimmed mean) for each sample and examine #the distribution of this measure. #We are going to take 5000 samples of 20 subjectssam1 = matrix(, ncol=5000, nrow=20)#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*## EXERCISE 1-2: Use a loop to draw 5000 samples of size 20 from pop1 # an place the samples in sam1#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#for (ii in 1:5000) {sam1[ , ii] = sample(pop1, 20, replace=TRUE)} # Now that we have our datafile containing all 5000 samples (ie. sam1) # we can begin to create variables for each of our location measures #I'll start us off with the meansam1means = apply(sam1, 2, mean) # number 2 = work in the columns rather than rows#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*## EXERCISE 1-3: Use the apply function to generate # the variables sam1meds (medians) and sam1tmeans (trimmed mean)#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#sam1meds = apply(sam1, 2, median)sam1tmeans = apply(sam1, 2, tmean) #Let's look at the distributions of each of these location estimatorsplot(density(sam1means))lines(density(sam1meds), col="red")lines(density(sam1tmeans), col="blue")abline(v = mean(pop1), lty=2) #Add in a line for the pop1 mean#??????????????????????????????????????????????????????????????##Thought Question 1: Which location estimator performs the best #for data coming from a normal population? Why?#??????????????????????????????????????????????????????????????# # One of the ways we can determine which location estimator # performs the best is by looking at the standard deviation # of the estimator accross all the samples. # The estimator with the lowest SD will have the least amount # of variability accross the samples. # A more common name for the standard deviation of the location # estimator is called the Standard Error or SE#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*## EXERCISE 1-4: Find the Standard Error of the sample means,# medians, and trimmed means. Based upon the SE, which # location estimator is the best for samples coming from# a normal population?#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*# sd(sam1means); sd(sam1meds); sd(sam1tmeans) #The mean performs the best.# In real life, we generally cannot go out an collect multiple samples # from a population, so we compute the Standard Error using a formula: # SE = sd(sample) / sqrt(sample N)#---------------------------------------------------------------------------------# 2. Sampling Distribution of the Mean, Median, # and Trimmed Mean under Non-Normality#---------------------------------------------------------------------------------# Normal distributions generally have very few outliers, # however when outliers begin to occur more frequently so of the # basic assumptions about normal distributions are no longer true # (as we are about to see). # One distribution that is like a normal distribution,# but with more outliers is called a mixed or contaminated # normal distribution and it is a result of two populations mixing together. #*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*# #EXAMPLE 1: "a" will be a mix of TWO populations 1: with SD=1 and 2: with SD=2 a=c(rnorm(5000, 0, 1), rnorm(5000, 0, 2)) #Let's compare this to b, which is from ONE population but with the same parameters of a b=rnorm(10000, mean(a), sd(a))plot(density(a))lines(density(b), col="red") #*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*##??????????????????????????????????????????????????????????????##Thought Question 2: How are a and b from Example 1 different?#??????????????????????????????????????????????????????????????##Thankfully, rather than having to create contaminated normal distributions the hard way, we can just use#a function provided to us by Dr. Wilcox called cnorm()#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##Contaminated/Mix Normal Distribution: cnorm(n, epsilon=0.1, k=10)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##Let's look at the options for the contaminated normal distribution:#cnorm() combines two normal distributions: #1) A standard normal (mean=0, sd=1) for 1-epsilon % of the data #2) A normal of mean=0 and sd=k for epsilon % of the data #If we were trying to re-create the variable a we made in example 1 we would have to do:z=cnorm(10000, epsilon=0.5, k=2)plot(density(a))lines(density(z), col="blue")#Which looks very very similar to a! #Let's create a second population called pop2 from a contaminated normal distributionpop2 = cnorm(1000000, epsilon=0.1, k=10) #The mean, sd, and plot of which are:mean(pop2); sd(pop2); plot(density(pop2))#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*##EXERCISE 2:#A) Create an empty matrix called sam2 to contain 5000 samples #of 20 observations each#B) Populate sam2 with 5000 random samples of size 20 from pop2#C) Compute the mean (sam2means), median (sam2meds), #and trimmed mean (sam2tmeans) for each sample#D) Create an overlaid density plot of each sample WITH the pop2 #mean as a verticle line#E) Find the SE of each location estimator#F) Based upon the SE, which location estimator is the best # for samples coming from a contaminated normal distribution#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*# #A) #B) #C) #D) #E) #F)#---------------------------------------------------------------------------------# 3. The Central Limit Theorem#--------------------------------------------------------------------------------- #We've discovered a few things today: #1) When a population comes from a normal distribution, # then mean will be the best location estimator of the samples#2) When a population comes from a mixed/contaminated normal distribution, # the trimmed mean is the best location estimator# These observations are related to the Central Limit Theorem (CLT)# that is discussed in Section 5.3 of the book (page 85)# The CLT states that as the sample size gets sufficiently large, # the distribution of the sample means will be normally distributed.# We saw a demonstration of this last week when we looked at the means # from the unifom distribution.# The CLT has been used to justify the fact that for many of our statistics# we rely upon computing the mean (not median or trimmed mean) of our samples#There are a few problems with the CLT. #1) how large of a sample do we need? #2) It seems that our experiements with the contaminated normal may contradict this.#In the homework you will investigate this further
Create Outline - Finer Diner Desert Consultant
Finer Diner Dessert ConsultantFiner Diner has hired you as a consultant to determine how to increase dessert sales.Data fr ...
Create Outline - Finer Diner Desert Consultant
Finer Diner Dessert ConsultantFiner Diner has hired you as a consultant to determine how to increase dessert sales.Data from Finer Diner, Kim’s Kitchen, and Pete’s Place was used to find out if there is a difference in the number of calories in their desserts. Set alpha to 0.05 and determine if there is a difference in the mean number of calories.RestaurantFiner DinerKim’s KitchenPete’s PlaceDessert 14201006880Dessert 2570852800Dessert 36361152508Dessert 43771255785Dessert 52581366702Use your findings to complete this exercise. Create an outline in which you:Identify ways you can market desserts for the Finer Diner to give them a comptetive edge over their competition,Illustrate potentional changes in the lineup of desserts at Finer Diner that could solicit more sales.Propose marketing ideas directed specifically at the competition’s customers in order to get them to try desserts at the Finer Diner.Format any references and citations in APA format.Submit assignment in a Microsoft®Word
ITEC 8437 WU Inferential Statistics One Sample T Test Male Professors Age Essay
A t test examines if two means (averages) are reliably different from each other; that is, whether any significant differe ...
ITEC 8437 WU Inferential Statistics One Sample T Test Male Professors Age Essay
A t test examines if two means (averages) are reliably different from each other; that is, whether any significant differences exist between the means of samples from different populations. For example, to what extent is the output of a local office which applied an IT solution different than the output of other local offices which did not apply the IT solution? Also, if you wanted to test whether the average IQ score of a group of students differs from 100, you may use a t test. A t test does more than just compare means; as an inferential statistic, t tests allow researchers to make inferences about the populations beyond their sample data.For this Assignment, you will use the IT Security database and conduct two t tests: (a) one-sample t test and (b) paired-samples t test and prepare an APA results write-up for each test....thank you
Ashford University Golf Club Manufacturing Worksheet
A golf club manufacturer is trying to determine how the price of a set of clubs affects the demand for clubs. The attached ...
Ashford University Golf Club Manufacturing Worksheet
A golf club manufacturer is trying to determine how the price of a set of clubs affects the demand for clubs. The attached file contains the price of a set of clubs and the monthly sales.
Assume the only factor influencing monthly sales is price. Fit the following three curves to these data: linear (Y = a + bX), exponential (Y = abX), and multiplicative (Y = aXb). Which equation fits the data best?
Interpret your best-fitting equation.
Using the best-fitting equation, predict sales during a month in which the price is $470.
Provide a short description of the tools used and why this was the "best-fitting equation"
Earn money selling
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