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y=1.5x-4
y=-x
-x=1.5x-4
4=2.5x
x=1.6
y=-1.6
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Argosy University Probability and Distribution Worksheet
Answer to the following questions (APA format not needed):Please define each of the following terms:sampled population, ra ...
Argosy University Probability and Distribution Worksheet
Answer to the following questions (APA format not needed):Please define each of the following terms:sampled population, random sampling, convenient sampling, judgmental sampling, stratified random sampling, consistency in sampling, relative efficiency. Explain why a sample is of probabilistic nature.What is it meant by the term “parameter of a population”? Explain why a population can be represented by a random variable.What is a point estimate, and an unbiased point estimate? Explain howthe sample mean can bean unbiased estimate of the population mean.How do you justify that the sample variance is an unbiased estimate of the population variance? What is the sampling requirement in the latter case? Provide a numerical example of estimating the mean, the variance, and the standard deviation.Please define each of the following terms, discuss applicability and significance of each:sample statistic, standard error, sampling distribution, and central limit theorem. Include hypothetical examples for better clarity. What is the z statistic and what qualifies a statistic to be z statistic based on the central limit theorem and the basic properties of normal distributions?What are the limitations of the central limit theorem, and how some of these limitations are bypassed?For example, the z statistic as the sampling distribution in estimating a proportion.What is the sampling distribution in estimating the variance of a population? What are the properties of this distribution?What is the alternative of z statistic for normally distributed populations whicheliminates some limitations of the central limit theorem?How this sampling distribution is constructed as combination of a z distribution and a chi squared distribution? What are the properties of this distribution?
Harvard University Lab homework using R
#Lab 10#274-Wilcox (Fall 2019)#Name:#Student ID:rm(list=ls())source('Rallfun-v33.txt')#1) Import the dataset lab10hw1.txt ...
Harvard University Lab homework using R
#Lab 10#274-Wilcox (Fall 2019)#Name:#Student ID:rm(list=ls())source('Rallfun-v33.txt')#1) Import the dataset lab10hw1.txt in table form:#2) For this dataset, what is our dependent variable? #3) How many independent variables do we have? #4) How many levels does each independent variable have (use the function unique(x) to check)? #5) Make a boxplot for this set of data (submit the image). What problem do you see?#6) What is our null hypothesis?#7) Now use the classic method to analyze this dataset using the format aov(x~factor(g)). # Save this as an object called hw1.anova. #NOTE: MAKE SURE TO USE factor() AROUND YOUR GROUPING VARIABLE SO IT IS TREATED AS A FACTOR, NOT AS A NUMERIC VARIABLE. # Then summarize these results using summary(hw1.anova). #8) Do we reject or do we fail to reject the null hypothesis?#9) Now let's use the t1way() function, which is based on trimmed means and can deal with heteroscedasticity.#Hint 1: First, reorganize your data using fac2list(x, g). Save your new list as hw1.list.#Hint 2: You will need to have loaded in the source code to use the t1way function.#10) Do we reject or do we fail to reject the null hypothesis from 1.9?----------------------------------------------------------------------------------------------------------------------------------------------------------Lab 10 lecture notes:#Lab 10#Lab 10-Contents#1. One-Way Independent Groups ANOVA (Equal Variance)#2. One-Way Independent Groups ANOVA (Unequal Variance-Welch's Test)#---------------------------------------------------------------------------------# 1. One-Way Independent Groups ANOVA (Equal Variance)#--------------------------------------------------------------------------------- #Scenario for first exercise: # A professor is interested in the effect of visualization strategies#on test performance. In order to study this, he tells students in#his statistics class that they will have a 15 question exam in #two weeks. Then, he randomly assigns students to three groups. # # The first group is told to spend 15 min each day vizualizing #the outcome of getting an A on the test to vividly imagine #the exam with an "A" written on it and how great it will feel. # # The second group is a control group that does no visualization. ## The third group is told to spend 15 min each day visualizing#the process of studying for the exam: imagine the hours of studying,#reviewing their chapters, working through chapter problems, # quizzing themeselves, etc. # Two weeks later, the students take the exam and the professor # records how many questions the students answer correctly out of 15.#So, the groups are:#Group 1: Visualize Outcome (Grade)#Group 2: No visualization (Control)#Group 3: Visiualize Process (Studying)#######################################################Question: Are the groups here Independent?#######################################################We'll instroduce a few new terms: #Factor: A variable that consists of categories. #Levels: The categories of the Factor variable. #In our example above, the variable that contains#the groups is called "Group". #So, our factor is the variable "Group"#How many levels are there for the Group Factor?#Let's read in LAB10A.txtlab10a=read.table('LAB10A.txt', header=T)#While we can easily see the levels for the Group #factor we could also use a new command to figure out #the number of unique levels.#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Number of Unique Levels: unique(data$variable)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#unique(lab10a$Group) #As we can see, there are 3 levels. 1, 2, and 3#Look at boxplot of each group using #boxplot(y~group, data=data)par(mfrow=c(1,1))boxplot(Score~Group, data=lab10a)#Do you think the means will be different (statistically)#between the groups?#Before we begin to test for differences between #the means, let's wrtie out our NUll #and Alternative Hyhpotheses#H0: The means are equal (mu1=mu2=mu3)#HA: At least one mean is different. #(eg. mu1 != mu2 OR mu1 != mu3 OR mu2 != mu3 )#To test the Hypothesis we can use the ANOVA function aov():#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## One-Way ANOVA: aov(y~factor(g), data)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##The aov() function assumes that the #variance is the same within each of the groups.mod1=aov(Score ~ factor(Group), data=lab10a)summary(mod1)#A) If pval < alpha, then Reject the Null Hypothesis#B) If pval > alpha, then Fail to Reject the Null Hypothesis#Do we Reject or Fail to Reject the Null?#Reject 0.00129 < .05 then Reject H0#What does this tell us? That the groups are different?#If so, how do we know which groups?#P-value we just got is called the Omnibus P-value, #which tells us that there are differences somewhere#With this P-value we often use the term #"Main Effect" to say that there is an effect of the#factor on the outcome.#In this instance we'd say that there is a Main Effect #of Group on the Score.#To Answer which groups are different, we need to first#conver the data into List Mode (a different way #of storing the data). We can convert the factor Group #to a list using the function fac2list(y, g)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Convert Factors to List Data: fac2list(data$y, data$g)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#listA=fac2list(lab10a$Score, lab10a$Group)listA #Once the data is in List Mode we have to use the#lincon() command from Dr. Wilcox's source code.#The lincon() package is used to compare the groups while#controlling for the experimentwise Type 1 error rate.#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Compare Groups: lincon(list_name, tr=0.2)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##By default lincon() compares groups using 20% trimming. #We will set this to 0 for now:lincon(listA, tr=0) #result:# H0_1: mu1=mu2 --- p=0.32 ---Fail to reject# H0_2: mu1=mu3 --- p=0.0009 ---Reject# H0_3: mu2=mu3 --- p=0.008 ---Reject#---------------------------------------------------------------------------------# 2. One-Way Independent Groups ANOVA (Unequal Variance-Welch's Test)#--------------------------------------------------------------------------------- # We just learned how to conduct a One-Way ANOVA # when the variances are equal within each group. # Now, we will learn how to conduct a One-Way ANOVA #for then the variance is not equal.# Let's start by reading in the LAB10B.txt datafile.lab10b=read.table('LAB10B.txt', header=T)# Then examine a boxplot of all of it.boxplot(Score~factor(Group), data=lab10b)# What do we notice about this boxplot?#-----# Let's start by running the equal variance ANOVA#on the data (which of course is WRONG!)mod2=aov(Score ~ factor(Group), data=lab10b) #---DON'Tsummary(mod2)#A) If pval < alpha, then Reject the Null Hypothesis#B) If pval > alpha, then Fail to Reject the Null Hypothesis# Do we Reject or Fail to Reject the Null?#Fail to reject: p-value=0.0895 > .05 !!!INCORRECT----#----# Now let's try to run the correct test that assumes #unequal variance. #We call this the Welch's test (just like in the t-test)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Welch's One-Way ANOVA: t1way(list_name, tr=0.20)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##In order to use this t1way function, #we will first need to convert the data to #List Mode using fac2list()listB=fac2list(lab10b$Score, lab10b$Group)t1way(listB, tr=0.2)# Do we Reject or Fail to Reject the Null?#Reject: p-value:0.04966583 <.05#Again, we can use the lincon() command to #find out Where the group differences are.#This time we will use the 20% trimming.lincon(listB, tr=0.2)# G1 and G2: p-value=0.92210409 > .05 Fail to reject# G1 and G3: p-value=0.19451518 > .05 Fail to reject#G2 and G3: p-value=0.03227316 < .05 Reject#
Calculating a Final Grade
So I currently have a 98 in a class right now, and I have one week left in it. There's a project due that's worth 17.5% of ...
Calculating a Final Grade
So I currently have a 98 in a class right now, and I have one week left in it. There's a project due that's worth 17.5% of my grade. How would I calculate my final grade if, lets say, I didn't do it?
Kaplan Height Measurement Methods Comparison Paper
Case Study 1: Understanding Process Measurement Variation
For this assignment, you will need to conduct an experiment then ...
Kaplan Height Measurement Methods Comparison Paper
Case Study 1: Understanding Process Measurement Variation
For this assignment, you will need to conduct an experiment then create visuals that will be placed within a PowerPoint presentation to present your findings. Your presentation should be easy to read and have a consistent design theme throughout. Please view the first four chapters in the following LinkedIn Learning course on PowerPoint Essentials before creating your presentation:
PowerPoint2019 Essential Training.
Read the following experimental variation scenario:
To help you learn about measurement variation, try this experiential learning exercise. (We are indebted to Alan Goodman, DuPont Company, Wilmington, Delaware, for bringing this exercise to our attention.)
You have started a new business providing height measurements of humans. Your customers expect accurate and precise measurements. You offer two methods of measurement: a yardstick or a meter stick, and a tape measure. You need to test the two methods to evaluate their performance and provide the results to your customers.
For this experiment you will need the following tools:
A yardstick or meterstick.
A tape measure.
Access to an entrance door that is 6 feet or taller.
A group of 20 or more people (they do not have to be gathered at the same time, but it should be 20 different people that participate).
You will test Method 1 in this way:
Identify a group of 20 or more people.
Ask the group of 20 or more people to measure the height of the entrance door that is approximately 6 feet or taller. Be sure that the entrance door is the same for each member of the group.
Each person will measure the height of the door using the yardstick or the meterstick and will silently report the measurement to you or to someone you have designated as the data collector.
You will then tabulate the data and plot each measurement on a run or sequence chart. No deviation from the prescribed method is allowed.
In Method 2, you may use the same, or a different, group of 20 or more people and the same or a different entrance door.
This time, the group will use the tape measure in any way the group members desire.
Again, each person silently reports the measurement of the entrance door to you or a designated data collector, and you will tabulate and plot each data point.
You will then create a PowerPoint presentation that includes each of the following items:
Compare the accuracy and precision of the two methods using graphical and analytical methods. Which method was more accurate? Develop a flow chart for each method in which you specify the key problems that might be present. Develop the supplier, input, process steps, output, and customer (SIPOC) model to analyze the process of both methods. This can also be done in the flow chart. (Please reference these instructions on how to create a flow chart in Microsoft Word.)
Analyze the flow chart and SIPOC model to identify opportunity for improvement (OFI). Next, categorize whether the OFI are caused by special causes or common causes variations. Provide a rationale for your response. Which method of measurement would you recommend? Why? Should different methods be used under different circumstances? Consider the role of different customer segments.
Discuss the feelings the group(s) had when using the two methods. What were the differences between the two sets of feelings? Are these differences important?
Use Basic Search: Strayer University Online Library to identify at least two quality references to support your discussion.
Your assignment must follow these formatting requirements:
A PPT presentation with at least 8 slides that include the responses to numbers 1 through 3 above.
A references slide which follows APA format. Check with your professor for any additional instructions.
Formatting of the slides should be consistent and easy to read.
Cover slide containing the title of the assignment, the student's name, the professor's name, the course title, and the date.
Note: The cover and the reference slides are not included in the required assignment slides length.
1 page
Area Under The Normal Curve
The total area under a normal distribution curve is not infinite. To justify this position, consider The area under the no ...
Area Under The Normal Curve
The total area under a normal distribution curve is not infinite. To justify this position, consider The area under the normal distribution curve ...
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Argosy University Probability and Distribution Worksheet
Answer to the following questions (APA format not needed):Please define each of the following terms:sampled population, ra ...
Argosy University Probability and Distribution Worksheet
Answer to the following questions (APA format not needed):Please define each of the following terms:sampled population, random sampling, convenient sampling, judgmental sampling, stratified random sampling, consistency in sampling, relative efficiency. Explain why a sample is of probabilistic nature.What is it meant by the term “parameter of a population”? Explain why a population can be represented by a random variable.What is a point estimate, and an unbiased point estimate? Explain howthe sample mean can bean unbiased estimate of the population mean.How do you justify that the sample variance is an unbiased estimate of the population variance? What is the sampling requirement in the latter case? Provide a numerical example of estimating the mean, the variance, and the standard deviation.Please define each of the following terms, discuss applicability and significance of each:sample statistic, standard error, sampling distribution, and central limit theorem. Include hypothetical examples for better clarity. What is the z statistic and what qualifies a statistic to be z statistic based on the central limit theorem and the basic properties of normal distributions?What are the limitations of the central limit theorem, and how some of these limitations are bypassed?For example, the z statistic as the sampling distribution in estimating a proportion.What is the sampling distribution in estimating the variance of a population? What are the properties of this distribution?What is the alternative of z statistic for normally distributed populations whicheliminates some limitations of the central limit theorem?How this sampling distribution is constructed as combination of a z distribution and a chi squared distribution? What are the properties of this distribution?
Harvard University Lab homework using R
#Lab 10#274-Wilcox (Fall 2019)#Name:#Student ID:rm(list=ls())source('Rallfun-v33.txt')#1) Import the dataset lab10hw1.txt ...
Harvard University Lab homework using R
#Lab 10#274-Wilcox (Fall 2019)#Name:#Student ID:rm(list=ls())source('Rallfun-v33.txt')#1) Import the dataset lab10hw1.txt in table form:#2) For this dataset, what is our dependent variable? #3) How many independent variables do we have? #4) How many levels does each independent variable have (use the function unique(x) to check)? #5) Make a boxplot for this set of data (submit the image). What problem do you see?#6) What is our null hypothesis?#7) Now use the classic method to analyze this dataset using the format aov(x~factor(g)). # Save this as an object called hw1.anova. #NOTE: MAKE SURE TO USE factor() AROUND YOUR GROUPING VARIABLE SO IT IS TREATED AS A FACTOR, NOT AS A NUMERIC VARIABLE. # Then summarize these results using summary(hw1.anova). #8) Do we reject or do we fail to reject the null hypothesis?#9) Now let's use the t1way() function, which is based on trimmed means and can deal with heteroscedasticity.#Hint 1: First, reorganize your data using fac2list(x, g). Save your new list as hw1.list.#Hint 2: You will need to have loaded in the source code to use the t1way function.#10) Do we reject or do we fail to reject the null hypothesis from 1.9?----------------------------------------------------------------------------------------------------------------------------------------------------------Lab 10 lecture notes:#Lab 10#Lab 10-Contents#1. One-Way Independent Groups ANOVA (Equal Variance)#2. One-Way Independent Groups ANOVA (Unequal Variance-Welch's Test)#---------------------------------------------------------------------------------# 1. One-Way Independent Groups ANOVA (Equal Variance)#--------------------------------------------------------------------------------- #Scenario for first exercise: # A professor is interested in the effect of visualization strategies#on test performance. In order to study this, he tells students in#his statistics class that they will have a 15 question exam in #two weeks. Then, he randomly assigns students to three groups. # # The first group is told to spend 15 min each day vizualizing #the outcome of getting an A on the test to vividly imagine #the exam with an "A" written on it and how great it will feel. # # The second group is a control group that does no visualization. ## The third group is told to spend 15 min each day visualizing#the process of studying for the exam: imagine the hours of studying,#reviewing their chapters, working through chapter problems, # quizzing themeselves, etc. # Two weeks later, the students take the exam and the professor # records how many questions the students answer correctly out of 15.#So, the groups are:#Group 1: Visualize Outcome (Grade)#Group 2: No visualization (Control)#Group 3: Visiualize Process (Studying)#######################################################Question: Are the groups here Independent?#######################################################We'll instroduce a few new terms: #Factor: A variable that consists of categories. #Levels: The categories of the Factor variable. #In our example above, the variable that contains#the groups is called "Group". #So, our factor is the variable "Group"#How many levels are there for the Group Factor?#Let's read in LAB10A.txtlab10a=read.table('LAB10A.txt', header=T)#While we can easily see the levels for the Group #factor we could also use a new command to figure out #the number of unique levels.#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Number of Unique Levels: unique(data$variable)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#unique(lab10a$Group) #As we can see, there are 3 levels. 1, 2, and 3#Look at boxplot of each group using #boxplot(y~group, data=data)par(mfrow=c(1,1))boxplot(Score~Group, data=lab10a)#Do you think the means will be different (statistically)#between the groups?#Before we begin to test for differences between #the means, let's wrtie out our NUll #and Alternative Hyhpotheses#H0: The means are equal (mu1=mu2=mu3)#HA: At least one mean is different. #(eg. mu1 != mu2 OR mu1 != mu3 OR mu2 != mu3 )#To test the Hypothesis we can use the ANOVA function aov():#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## One-Way ANOVA: aov(y~factor(g), data)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##The aov() function assumes that the #variance is the same within each of the groups.mod1=aov(Score ~ factor(Group), data=lab10a)summary(mod1)#A) If pval < alpha, then Reject the Null Hypothesis#B) If pval > alpha, then Fail to Reject the Null Hypothesis#Do we Reject or Fail to Reject the Null?#Reject 0.00129 < .05 then Reject H0#What does this tell us? That the groups are different?#If so, how do we know which groups?#P-value we just got is called the Omnibus P-value, #which tells us that there are differences somewhere#With this P-value we often use the term #"Main Effect" to say that there is an effect of the#factor on the outcome.#In this instance we'd say that there is a Main Effect #of Group on the Score.#To Answer which groups are different, we need to first#conver the data into List Mode (a different way #of storing the data). We can convert the factor Group #to a list using the function fac2list(y, g)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Convert Factors to List Data: fac2list(data$y, data$g)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#listA=fac2list(lab10a$Score, lab10a$Group)listA #Once the data is in List Mode we have to use the#lincon() command from Dr. Wilcox's source code.#The lincon() package is used to compare the groups while#controlling for the experimentwise Type 1 error rate.#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Compare Groups: lincon(list_name, tr=0.2)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##By default lincon() compares groups using 20% trimming. #We will set this to 0 for now:lincon(listA, tr=0) #result:# H0_1: mu1=mu2 --- p=0.32 ---Fail to reject# H0_2: mu1=mu3 --- p=0.0009 ---Reject# H0_3: mu2=mu3 --- p=0.008 ---Reject#---------------------------------------------------------------------------------# 2. One-Way Independent Groups ANOVA (Unequal Variance-Welch's Test)#--------------------------------------------------------------------------------- # We just learned how to conduct a One-Way ANOVA # when the variances are equal within each group. # Now, we will learn how to conduct a One-Way ANOVA #for then the variance is not equal.# Let's start by reading in the LAB10B.txt datafile.lab10b=read.table('LAB10B.txt', header=T)# Then examine a boxplot of all of it.boxplot(Score~factor(Group), data=lab10b)# What do we notice about this boxplot?#-----# Let's start by running the equal variance ANOVA#on the data (which of course is WRONG!)mod2=aov(Score ~ factor(Group), data=lab10b) #---DON'Tsummary(mod2)#A) If pval < alpha, then Reject the Null Hypothesis#B) If pval > alpha, then Fail to Reject the Null Hypothesis# Do we Reject or Fail to Reject the Null?#Fail to reject: p-value=0.0895 > .05 !!!INCORRECT----#----# Now let's try to run the correct test that assumes #unequal variance. #We call this the Welch's test (just like in the t-test)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Welch's One-Way ANOVA: t1way(list_name, tr=0.20)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##In order to use this t1way function, #we will first need to convert the data to #List Mode using fac2list()listB=fac2list(lab10b$Score, lab10b$Group)t1way(listB, tr=0.2)# Do we Reject or Fail to Reject the Null?#Reject: p-value:0.04966583 <.05#Again, we can use the lincon() command to #find out Where the group differences are.#This time we will use the 20% trimming.lincon(listB, tr=0.2)# G1 and G2: p-value=0.92210409 > .05 Fail to reject# G1 and G3: p-value=0.19451518 > .05 Fail to reject#G2 and G3: p-value=0.03227316 < .05 Reject#
Calculating a Final Grade
So I currently have a 98 in a class right now, and I have one week left in it. There's a project due that's worth 17.5% of ...
Calculating a Final Grade
So I currently have a 98 in a class right now, and I have one week left in it. There's a project due that's worth 17.5% of my grade. How would I calculate my final grade if, lets say, I didn't do it?
Kaplan Height Measurement Methods Comparison Paper
Case Study 1: Understanding Process Measurement Variation
For this assignment, you will need to conduct an experiment then ...
Kaplan Height Measurement Methods Comparison Paper
Case Study 1: Understanding Process Measurement Variation
For this assignment, you will need to conduct an experiment then create visuals that will be placed within a PowerPoint presentation to present your findings. Your presentation should be easy to read and have a consistent design theme throughout. Please view the first four chapters in the following LinkedIn Learning course on PowerPoint Essentials before creating your presentation:
PowerPoint2019 Essential Training.
Read the following experimental variation scenario:
To help you learn about measurement variation, try this experiential learning exercise. (We are indebted to Alan Goodman, DuPont Company, Wilmington, Delaware, for bringing this exercise to our attention.)
You have started a new business providing height measurements of humans. Your customers expect accurate and precise measurements. You offer two methods of measurement: a yardstick or a meter stick, and a tape measure. You need to test the two methods to evaluate their performance and provide the results to your customers.
For this experiment you will need the following tools:
A yardstick or meterstick.
A tape measure.
Access to an entrance door that is 6 feet or taller.
A group of 20 or more people (they do not have to be gathered at the same time, but it should be 20 different people that participate).
You will test Method 1 in this way:
Identify a group of 20 or more people.
Ask the group of 20 or more people to measure the height of the entrance door that is approximately 6 feet or taller. Be sure that the entrance door is the same for each member of the group.
Each person will measure the height of the door using the yardstick or the meterstick and will silently report the measurement to you or to someone you have designated as the data collector.
You will then tabulate the data and plot each measurement on a run or sequence chart. No deviation from the prescribed method is allowed.
In Method 2, you may use the same, or a different, group of 20 or more people and the same or a different entrance door.
This time, the group will use the tape measure in any way the group members desire.
Again, each person silently reports the measurement of the entrance door to you or a designated data collector, and you will tabulate and plot each data point.
You will then create a PowerPoint presentation that includes each of the following items:
Compare the accuracy and precision of the two methods using graphical and analytical methods. Which method was more accurate? Develop a flow chart for each method in which you specify the key problems that might be present. Develop the supplier, input, process steps, output, and customer (SIPOC) model to analyze the process of both methods. This can also be done in the flow chart. (Please reference these instructions on how to create a flow chart in Microsoft Word.)
Analyze the flow chart and SIPOC model to identify opportunity for improvement (OFI). Next, categorize whether the OFI are caused by special causes or common causes variations. Provide a rationale for your response. Which method of measurement would you recommend? Why? Should different methods be used under different circumstances? Consider the role of different customer segments.
Discuss the feelings the group(s) had when using the two methods. What were the differences between the two sets of feelings? Are these differences important?
Use Basic Search: Strayer University Online Library to identify at least two quality references to support your discussion.
Your assignment must follow these formatting requirements:
A PPT presentation with at least 8 slides that include the responses to numbers 1 through 3 above.
A references slide which follows APA format. Check with your professor for any additional instructions.
Formatting of the slides should be consistent and easy to read.
Cover slide containing the title of the assignment, the student's name, the professor's name, the course title, and the date.
Note: The cover and the reference slides are not included in the required assignment slides length.
1 page
Area Under The Normal Curve
The total area under a normal distribution curve is not infinite. To justify this position, consider The area under the no ...
Area Under The Normal Curve
The total area under a normal distribution curve is not infinite. To justify this position, consider The area under the normal distribution curve ...
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