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A.20 B.18 C.8 D. 4
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I am assuming there are addition signs between the terms. In that case, the answer is "B" (18)
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need help with my QRB 501 Week Six Assignment, statistics homework help
Signature Assignment oDue Jul 17, 6:00 PM (PST) oNot Submitted oPOINTS 10 Exercise no new messages Objectives ...
need help with my QRB 501 Week Six Assignment, statistics homework help
Signature Assignment oDue Jul 17, 6:00 PM (PST) oNot Submitted oPOINTS 10 Exercise no new messages Objectives: 6.2 6.4 View more »Expand viewInstructionsAssignment FilesGradingAbout Your Signature Assignment This signature assignment is designed to align with specific program student learning outcome(s) in your program. Program Student Learning Outcomes are broad statements that describe what students should know and be able to do upon completion of their degree. The signature assignment may be graded with an automated rubric that allows the University to collect data that can be aggregated across a location or college/school and used for program improvements. Purpose of Assignment The purpose of this assignment is for students to synthesize the concepts learned throughout the course, provide students an opportunity to build critical thinking skills, develop businesses and organizations, and solve problems that require data. Assignment Steps Case 1: Scenario: Cloud Data Services (CDS), headquartered in Memphis, provides information technology services, specifically application hosting services in the cloud for several clients in the southern United States. CDS hosts software applications on their network servers. While CDS has achieved great success and customers rate CDS's services highly, lately, some customers have been complaining about downtime on one of the primary network servers. The given dataset, found in the Signature Assignment Excel® Template, contains the downtime data for the month of November.Use the data analytics skills learned in Week 3 and analyze the downtime data.Make a short presentation to CDS's management including the following:1.Using used Microsoft® Excel® Pivot Tables, construct a frequency distribution showing the number of times during the month that the server was down for each downtime cause category.2.Develop a bar chart that displays the data from the frequency distribution in part 1.3.Develop a pie chart that breaks down the percentage of total downtime that is attributed to each downtime cause during the month.4.Evaluate the mean, median, standard deviation, and variance of the downtime minutes for the month of November.Case 2:Note: Although you will be studying the concept of CPI in more detail in your ECO/561 class; for the purpose of this case, you need to use the concepts of percentages, percentage increase/decrease, and creating and interpreting line charts to compute the inflation rate in the US economy and determine which time period experienced the highest inflation rate. Follow the steps below to complete this signature assignment: 1.Search for the Federal Reserve Bank of St. Louis (FRED).2.On the home page of the website, you will see a search box.3.Type in CPI- AUCSL in the search box and press the return key.4.The first result of the search will be "Consumer Price Index for All Urban Consumers: All Items." Click on this result link.5.Click on the Download link and download the data in Excel®.6.On the Excel® file, the second column gives you the CPI values for each period starting from 1947.7.Go to the last row and notice the last date and the CPI value. Go back 6 years from this last date. For example, if the last date is 2016-11-01, then the date 6 years ago would be 2010-11-01.8.Copy and paste this six years data into a separate Excel® tab.9.Using Excel®, calculate the percentage change in CPI from a year earlier for each observation, beginning with the observation one year later than the first observation. To make this calculation, click on the blank cell next to the observation corresponding to that date and then use Formula 1, located in the Signature Assignment Excel® Formulas document (note that in Excel®, the symbol for multiplication is *), where t-1 is the first observation and t is the observation one year later. For example, to find the percentage change in CPI from 2010-11-01 to 2010-10-01, refer to Formula 2 located in the Signature Assignment Excel® Formulas document. Convert this value to a percentage in Excel®. Repeat this process for the remaining observations (you can use the copy and paste functions to avoid having to retype the formula).10.This new column contains the national inflation rate.11.Create a line graph of the percentage changes (inflation rates) from a year earlier.12.Which period experienced the highest inflation rate? What was the inflation rate during that period?Format your assignment consistent with APA guidelines. Click the Assignment Files tab to submit your assignment.
Inferential Statistics MBA Program Paper
Scenario: Upon successful completion of the MBA program, imagine you work in the analytics department for a consulting com ...
Inferential Statistics MBA Program Paper
Scenario: Upon successful completion of the MBA program, imagine you work in the analytics department for a consulting company. Your assignment is to analyze one of the following databases:
Manufacturing
Hospital
Consumer Food
Financial
Select one of the databases based on the information in the Signature Assignment Options.
Provide a 1,600-word detailed, four part, statistical report with the following sections:
Part 1 - Preliminary Analysis
Part 2 - Examination of Descriptive Statistics
Part 3 - Examination of Inferential Statistics
Part 4 - Conclusion/Recommendations
Part 1 - Preliminary Analysis
Generally, as a statistics consultant, you will be given a problem and data. At times, you may have to gather additional data. For this assignment, assume all the data is already gathered for you.
State the objective:
What are the questions you are trying to address?
Describe the population in the study clearly and in sufficient detail:
What is the sample?
Discuss the types of data and variables:
Are the data quantitative or qualitative?
What are levels of measurement for the data?
Part 2 - Descriptive Statistics
Examine the given data.
Present the descriptive statistics (mean, median, mode, range, standard deviation, variance, CV, and five-number summary).
Identify any outliers in the data.
Present any graphs or charts you think are appropriate for the data.
Note: Ideally, we want to assess the conditions of normality too. However, for the purpose of this exercise, assume data is drawn from normal populations.
Part 3 - Inferential Statistics
Use the Part 3: Inferential Statistics document.
Create (formulate) hypotheses
Run formal hypothesis tests
Make decisions. Your decisions should be stated in non-technical terms.
Hint: A final conclusion saying "reject the null hypothesis" by itself without explanation is basically worthless to those who hired you. Similarly, stating the conclusion is false or rejected is not sufficient.
Part 4 - Conclusion and Recommendations
Include the following:
What are your conclusions?
What do you infer from the statistical analysis?
State the interpretations in non-technical terms. What information might lead to a different conclusion?
Are there any variables missing?
What additional information would be valuable to help draw a more certain conclusion?
Moore College of Art and Design Matched Pairs Statistics Worksheet
Learn by DoingMatched Pairs: In this lab you will learn how
to conduct a matched pairs T-test for a population mean using ...
Moore College of Art and Design Matched Pairs Statistics Worksheet
Learn by DoingMatched Pairs: In this lab you will learn how
to conduct a matched pairs T-test for a population mean using
StatCrunch. We will work with a data set that has historical
importance in the development of the T-test.Paired T hypothesis test:
μD = μ1 - μ2 : Mean of the
difference between Regular seed and Kiln-dried seed
H0 : μD = 0
HA : μD > 0
Hypothesis test results:
Difference
Mean
Std. Err.
DF
T-Stat
P-value
Regular seed - Kiln-dried seed
-33.727273
19.951346
10
-1.6904761
0.9391
Some features of this activity may not work well on a cell phone
or tablet. We highly recommend that you complete this activity on a
computer.Here are the directions, grading rubric, and definition of
high-quality feedback for the Learn by Doing
discussion board exercises.A list of StatCrunch directions is provided at the bottom of
this page.ContextGosset's Seed Plot DataWilliam S. Gosset was employed by the Guinness brewing company
of Dublin. Sample sizes available for experimentation in brewing
were necessarily small. At that time, Gosset contacted a famous
statistician Karl Pearson (1857-1936) and was told that there were
no techniques for developing probability models for small data
sets. Gosset studied under Pearson, and the outcome of his study
was perhaps the most famous paper in statistical literature, "The
Probable Error of a Mean" (1908), which introduced the
T-distribution.Since Gosset was employed by Guinness, any work he produced
would be owned by Guinness, so he published under a pseudonym,
"Student"; hence, the T-distribution is often referred to as
Student's T-distribution.To illustrate his analysis, Gosset used the results of seeding
11 different plots of land with two different types of seed:
regular and kiln-dried. He wanted to determine if drying seeds
before planting increased plant yield. Since different plots of
soil may be naturally more fertile, this confounding variable was
eliminated by using the matched pairs design and planting both
types of seed in all 11 plots.The resulting data (corn yield in pounds per acre) are as
follows.
Plot
Regular seed
Kiln-dried Seed
1
1903
2009
2
1935
1915
3
1910
2011
4
2496
2463
5
2108
2180
6
1961
1925
7
2060
2122
8
1444
1482
9
1612
1542
10
1316
1443
11
1511
1535
We use these data to test the hypothesis that kiln-dried seed
yields more corn than regular seed.Because of the nature of the experimental design (matched
pairs), we are testing the difference in yield.
Plot
Regular seed
Kiln-dried Seed
Difference
1
1903
2009
–106
2
1935
1915
20
3
1910
2011
–101
4
2496
2463
33
5
2108
2180
–72
6
1961
1925
36
7
2060
2122
–62
8
1444
1482
–38
9
1612
1542
70
10
1316
1443
–127
11
1511
1535
–24
Note that the differences were calculated:
regular −
kiln-dried.VariablesRegular seed: regular seeds that were traditionally
used for planting
kiln-dried: seed that were kiln-dried before plantingDataDownload the seed (Links to an external site.) data
file, and then upload the file into StatCrunch.PromptState the hypotheses and define the parameter.Checking conditions: Since Gosset invented the T-distribution,
we will assume that his sample meets the conditions and proceed
with the T-test. Regardless, answer these questions to demonstrate
your understanding of the conditions for use of the T-model.
But first you will need to review the dotplots for the data (opens
in a new tab).
Which graph is used to check conditions? Why?What do we look for in the graph to verify that conditions are
met?What else do we need to know about the sample of seeds before
using the T-test?
Use StatCrunch to find the T-score and the P-value. Hint: as
you work through the StatCrunch directions, keep in mind that we
want to calculate the differences as
regular −
kiln-dried . So you will choose
Regular seed for Sample 1 and kiln-dried seed for
Sample 2. (directions)
Copy and paste the information in the StatCrunch output window into
your initial post.State a conclusion based on the context of this scenario.EXAMPLE TO RIGHT ANSWER1. Ho: μ=0Ha: μ>0The average difference is -33.732. a) We use the graph of the differences because that is what
we are analyzing.b) We look to see if the graph is normally distributed, not
skewed, and doesn't have outliers.c) We don't know if the data is randomly selected.3.Paired T hypothesis test:
μD = μ1 - μ2 : Mean of the
difference between Regular seed and Kiln-dried seed
H0 : μD = 0
HA : μD > 0
Hypothesis test results:
Difference
Mean
Std. Err.
DF
T-Stat
P-value
Regular seed - Kiln-dried seed
-33.727273
19.951346
10
-1.6904761
0.9391
Differences stored in column, Differences.4. Based on the P-value of 0.9391, we do not have enough
evidence to reject the null hypothesis. There is no statistically
significant evidence to show that kiln-dried seeds yield more than
regular seeds.
QSO500 Southern New Hampshire Statistical Methods and Data Mining Paper
The final project for this course is the creation of a statistical analysis report. Each day, management professionals are ...
QSO500 Southern New Hampshire Statistical Methods and Data Mining Paper
The final project for this course is the creation of a statistical analysis report. Each day, management professionals are faced with multiple decisions affecting various aspects of the operation. The ability to use data to drive decisions is an essential skill that is useful in any facet of an operation. The dynamic environment offers daily challenges that require the talents of the operations manager; working in this field is exciting and rewarding. In Module Seven, you will submit your selection of statistical tools and data analysis, which are critical elements III and IV of your final project. You will submit a 3- to 4-page paper and a spreadsheet that provides justification for the appropriate statistical tools needed to analyze the company’s data. Specifically, the following critical elements must be addressed: Identifystatistical methods to collect data: Identify the appropriate statistical methods that you will use to perform your analysis. What are your statistical assumptions concerning the Justify why you chose these methods to analyze the data. Be sure to include how these methods will help predict the use of the data in driving Data-Driven Decisions to determine the appropriate decision for the identified problem: Outline the process needed to utilize your statistical analysis to reach a decision regarding the given problem. Explain how data mining is used to develop a solution to the case. In your response, consider the concepts of metadata. Explain if the problem is a structured or unstructured problem. Assess how the variables have potential for answering the problem. data that led you to selecting the methods? In other words, why did you select this method for statistical analysis? decisions using cited evidence. Guidelines for Submission: Your paper must be submitted as a 3- to 4-page Microsoft Word document and attached spreadsheet with double spacing, 12-point Times New Roman font, one-inch margins, and at least six sources cited in APA format.
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need help with my QRB 501 Week Six Assignment, statistics homework help
Signature Assignment oDue Jul 17, 6:00 PM (PST) oNot Submitted oPOINTS 10 Exercise no new messages Objectives ...
need help with my QRB 501 Week Six Assignment, statistics homework help
Signature Assignment oDue Jul 17, 6:00 PM (PST) oNot Submitted oPOINTS 10 Exercise no new messages Objectives: 6.2 6.4 View more »Expand viewInstructionsAssignment FilesGradingAbout Your Signature Assignment This signature assignment is designed to align with specific program student learning outcome(s) in your program. Program Student Learning Outcomes are broad statements that describe what students should know and be able to do upon completion of their degree. The signature assignment may be graded with an automated rubric that allows the University to collect data that can be aggregated across a location or college/school and used for program improvements. Purpose of Assignment The purpose of this assignment is for students to synthesize the concepts learned throughout the course, provide students an opportunity to build critical thinking skills, develop businesses and organizations, and solve problems that require data. Assignment Steps Case 1: Scenario: Cloud Data Services (CDS), headquartered in Memphis, provides information technology services, specifically application hosting services in the cloud for several clients in the southern United States. CDS hosts software applications on their network servers. While CDS has achieved great success and customers rate CDS's services highly, lately, some customers have been complaining about downtime on one of the primary network servers. The given dataset, found in the Signature Assignment Excel® Template, contains the downtime data for the month of November.Use the data analytics skills learned in Week 3 and analyze the downtime data.Make a short presentation to CDS's management including the following:1.Using used Microsoft® Excel® Pivot Tables, construct a frequency distribution showing the number of times during the month that the server was down for each downtime cause category.2.Develop a bar chart that displays the data from the frequency distribution in part 1.3.Develop a pie chart that breaks down the percentage of total downtime that is attributed to each downtime cause during the month.4.Evaluate the mean, median, standard deviation, and variance of the downtime minutes for the month of November.Case 2:Note: Although you will be studying the concept of CPI in more detail in your ECO/561 class; for the purpose of this case, you need to use the concepts of percentages, percentage increase/decrease, and creating and interpreting line charts to compute the inflation rate in the US economy and determine which time period experienced the highest inflation rate. Follow the steps below to complete this signature assignment: 1.Search for the Federal Reserve Bank of St. Louis (FRED).2.On the home page of the website, you will see a search box.3.Type in CPI- AUCSL in the search box and press the return key.4.The first result of the search will be "Consumer Price Index for All Urban Consumers: All Items." Click on this result link.5.Click on the Download link and download the data in Excel®.6.On the Excel® file, the second column gives you the CPI values for each period starting from 1947.7.Go to the last row and notice the last date and the CPI value. Go back 6 years from this last date. For example, if the last date is 2016-11-01, then the date 6 years ago would be 2010-11-01.8.Copy and paste this six years data into a separate Excel® tab.9.Using Excel®, calculate the percentage change in CPI from a year earlier for each observation, beginning with the observation one year later than the first observation. To make this calculation, click on the blank cell next to the observation corresponding to that date and then use Formula 1, located in the Signature Assignment Excel® Formulas document (note that in Excel®, the symbol for multiplication is *), where t-1 is the first observation and t is the observation one year later. For example, to find the percentage change in CPI from 2010-11-01 to 2010-10-01, refer to Formula 2 located in the Signature Assignment Excel® Formulas document. Convert this value to a percentage in Excel®. Repeat this process for the remaining observations (you can use the copy and paste functions to avoid having to retype the formula).10.This new column contains the national inflation rate.11.Create a line graph of the percentage changes (inflation rates) from a year earlier.12.Which period experienced the highest inflation rate? What was the inflation rate during that period?Format your assignment consistent with APA guidelines. Click the Assignment Files tab to submit your assignment.
Inferential Statistics MBA Program Paper
Scenario: Upon successful completion of the MBA program, imagine you work in the analytics department for a consulting com ...
Inferential Statistics MBA Program Paper
Scenario: Upon successful completion of the MBA program, imagine you work in the analytics department for a consulting company. Your assignment is to analyze one of the following databases:
Manufacturing
Hospital
Consumer Food
Financial
Select one of the databases based on the information in the Signature Assignment Options.
Provide a 1,600-word detailed, four part, statistical report with the following sections:
Part 1 - Preliminary Analysis
Part 2 - Examination of Descriptive Statistics
Part 3 - Examination of Inferential Statistics
Part 4 - Conclusion/Recommendations
Part 1 - Preliminary Analysis
Generally, as a statistics consultant, you will be given a problem and data. At times, you may have to gather additional data. For this assignment, assume all the data is already gathered for you.
State the objective:
What are the questions you are trying to address?
Describe the population in the study clearly and in sufficient detail:
What is the sample?
Discuss the types of data and variables:
Are the data quantitative or qualitative?
What are levels of measurement for the data?
Part 2 - Descriptive Statistics
Examine the given data.
Present the descriptive statistics (mean, median, mode, range, standard deviation, variance, CV, and five-number summary).
Identify any outliers in the data.
Present any graphs or charts you think are appropriate for the data.
Note: Ideally, we want to assess the conditions of normality too. However, for the purpose of this exercise, assume data is drawn from normal populations.
Part 3 - Inferential Statistics
Use the Part 3: Inferential Statistics document.
Create (formulate) hypotheses
Run formal hypothesis tests
Make decisions. Your decisions should be stated in non-technical terms.
Hint: A final conclusion saying "reject the null hypothesis" by itself without explanation is basically worthless to those who hired you. Similarly, stating the conclusion is false or rejected is not sufficient.
Part 4 - Conclusion and Recommendations
Include the following:
What are your conclusions?
What do you infer from the statistical analysis?
State the interpretations in non-technical terms. What information might lead to a different conclusion?
Are there any variables missing?
What additional information would be valuable to help draw a more certain conclusion?
Moore College of Art and Design Matched Pairs Statistics Worksheet
Learn by DoingMatched Pairs: In this lab you will learn how
to conduct a matched pairs T-test for a population mean using ...
Moore College of Art and Design Matched Pairs Statistics Worksheet
Learn by DoingMatched Pairs: In this lab you will learn how
to conduct a matched pairs T-test for a population mean using
StatCrunch. We will work with a data set that has historical
importance in the development of the T-test.Paired T hypothesis test:
μD = μ1 - μ2 : Mean of the
difference between Regular seed and Kiln-dried seed
H0 : μD = 0
HA : μD > 0
Hypothesis test results:
Difference
Mean
Std. Err.
DF
T-Stat
P-value
Regular seed - Kiln-dried seed
-33.727273
19.951346
10
-1.6904761
0.9391
Some features of this activity may not work well on a cell phone
or tablet. We highly recommend that you complete this activity on a
computer.Here are the directions, grading rubric, and definition of
high-quality feedback for the Learn by Doing
discussion board exercises.A list of StatCrunch directions is provided at the bottom of
this page.ContextGosset's Seed Plot DataWilliam S. Gosset was employed by the Guinness brewing company
of Dublin. Sample sizes available for experimentation in brewing
were necessarily small. At that time, Gosset contacted a famous
statistician Karl Pearson (1857-1936) and was told that there were
no techniques for developing probability models for small data
sets. Gosset studied under Pearson, and the outcome of his study
was perhaps the most famous paper in statistical literature, "The
Probable Error of a Mean" (1908), which introduced the
T-distribution.Since Gosset was employed by Guinness, any work he produced
would be owned by Guinness, so he published under a pseudonym,
"Student"; hence, the T-distribution is often referred to as
Student's T-distribution.To illustrate his analysis, Gosset used the results of seeding
11 different plots of land with two different types of seed:
regular and kiln-dried. He wanted to determine if drying seeds
before planting increased plant yield. Since different plots of
soil may be naturally more fertile, this confounding variable was
eliminated by using the matched pairs design and planting both
types of seed in all 11 plots.The resulting data (corn yield in pounds per acre) are as
follows.
Plot
Regular seed
Kiln-dried Seed
1
1903
2009
2
1935
1915
3
1910
2011
4
2496
2463
5
2108
2180
6
1961
1925
7
2060
2122
8
1444
1482
9
1612
1542
10
1316
1443
11
1511
1535
We use these data to test the hypothesis that kiln-dried seed
yields more corn than regular seed.Because of the nature of the experimental design (matched
pairs), we are testing the difference in yield.
Plot
Regular seed
Kiln-dried Seed
Difference
1
1903
2009
–106
2
1935
1915
20
3
1910
2011
–101
4
2496
2463
33
5
2108
2180
–72
6
1961
1925
36
7
2060
2122
–62
8
1444
1482
–38
9
1612
1542
70
10
1316
1443
–127
11
1511
1535
–24
Note that the differences were calculated:
regular −
kiln-dried.VariablesRegular seed: regular seeds that were traditionally
used for planting
kiln-dried: seed that were kiln-dried before plantingDataDownload the seed (Links to an external site.) data
file, and then upload the file into StatCrunch.PromptState the hypotheses and define the parameter.Checking conditions: Since Gosset invented the T-distribution,
we will assume that his sample meets the conditions and proceed
with the T-test. Regardless, answer these questions to demonstrate
your understanding of the conditions for use of the T-model.
But first you will need to review the dotplots for the data (opens
in a new tab).
Which graph is used to check conditions? Why?What do we look for in the graph to verify that conditions are
met?What else do we need to know about the sample of seeds before
using the T-test?
Use StatCrunch to find the T-score and the P-value. Hint: as
you work through the StatCrunch directions, keep in mind that we
want to calculate the differences as
regular −
kiln-dried . So you will choose
Regular seed for Sample 1 and kiln-dried seed for
Sample 2. (directions)
Copy and paste the information in the StatCrunch output window into
your initial post.State a conclusion based on the context of this scenario.EXAMPLE TO RIGHT ANSWER1. Ho: μ=0Ha: μ>0The average difference is -33.732. a) We use the graph of the differences because that is what
we are analyzing.b) We look to see if the graph is normally distributed, not
skewed, and doesn't have outliers.c) We don't know if the data is randomly selected.3.Paired T hypothesis test:
μD = μ1 - μ2 : Mean of the
difference between Regular seed and Kiln-dried seed
H0 : μD = 0
HA : μD > 0
Hypothesis test results:
Difference
Mean
Std. Err.
DF
T-Stat
P-value
Regular seed - Kiln-dried seed
-33.727273
19.951346
10
-1.6904761
0.9391
Differences stored in column, Differences.4. Based on the P-value of 0.9391, we do not have enough
evidence to reject the null hypothesis. There is no statistically
significant evidence to show that kiln-dried seeds yield more than
regular seeds.
QSO500 Southern New Hampshire Statistical Methods and Data Mining Paper
The final project for this course is the creation of a statistical analysis report. Each day, management professionals are ...
QSO500 Southern New Hampshire Statistical Methods and Data Mining Paper
The final project for this course is the creation of a statistical analysis report. Each day, management professionals are faced with multiple decisions affecting various aspects of the operation. The ability to use data to drive decisions is an essential skill that is useful in any facet of an operation. The dynamic environment offers daily challenges that require the talents of the operations manager; working in this field is exciting and rewarding. In Module Seven, you will submit your selection of statistical tools and data analysis, which are critical elements III and IV of your final project. You will submit a 3- to 4-page paper and a spreadsheet that provides justification for the appropriate statistical tools needed to analyze the company’s data. Specifically, the following critical elements must be addressed: Identifystatistical methods to collect data: Identify the appropriate statistical methods that you will use to perform your analysis. What are your statistical assumptions concerning the Justify why you chose these methods to analyze the data. Be sure to include how these methods will help predict the use of the data in driving Data-Driven Decisions to determine the appropriate decision for the identified problem: Outline the process needed to utilize your statistical analysis to reach a decision regarding the given problem. Explain how data mining is used to develop a solution to the case. In your response, consider the concepts of metadata. Explain if the problem is a structured or unstructured problem. Assess how the variables have potential for answering the problem. data that led you to selecting the methods? In other words, why did you select this method for statistical analysis? decisions using cited evidence. Guidelines for Submission: Your paper must be submitted as a 3- to 4-page Microsoft Word document and attached spreadsheet with double spacing, 12-point Times New Roman font, one-inch margins, and at least six sources cited in APA format.
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