This document contains a 3-part final exam for the course MAT 510, with 100 multiple choice questions total across the 3 sets. It provides the questions for Sets 1, 2, and 3 of the exam, covering topics like statistical thinking principles, process improvement frameworks, variation, control charts, and data analysis. The document also advertises a website for additional classes.
MAT 510 Exceptional Education - snaptutorial.comDavisMurphyB11
This document contains questions from three sets of a final exam for MAT 510. It includes 25 questions in each set covering topics like statistical thinking, process improvement frameworks, control charts, regression analysis, experimental design, and statistical analyses. The questions are multiple choice and assess understanding of key concepts and principles from the course material.
Mat 510 Enhance teaching / snaptutorial.comBaileya19
This document contains three sets of questions for the MAT 510 Final Exam. Each set contains 25 multiple choice questions covering topics in statistics, statistical process control, design of experiments, and statistical modeling. The questions assess understanding of concepts like variation, process capability, control charts, regression analysis, and experimental design.
MAT 510 Effective Communication - tutorialrank.comBartholomew46
For more course tutorials visit
www.tutorialrank.com
MAT 510 Final Exam Set 1
Question 1
Improvement is needed for an organization to survive because:
Question 2
For more course tutorials visit
www.newtonhelp.com
MAT 510 Final Exam Set 1
Question 1
Improvement is needed for an organization to survive because:
Question 2
The Statistical Thinking Strategy has significant commonality with the scientific method. Which of the following statistical thinking principles is NOT generally associated with the scientific method?
Question 3
MAT 510 RANK Education Planning--mat510rank.comWindyMiller24
This document contains 3 sets of questions for the MAT 510 Final Exam. Each set contains 25 multiple choice questions covering topics related to statistical process improvement, statistical thinking strategies, control charts, design of experiments, and regression analysis. Students are instructed to visit a website for additional course materials.
MAT 510 Exceptional Education - snaptutorial.comDavisMurphyB11
This document contains questions from three sets of a final exam for MAT 510. It includes 25 questions in each set covering topics like statistical thinking, process improvement frameworks, control charts, regression analysis, experimental design, and statistical analyses. The questions are multiple choice and assess understanding of key concepts and principles from the course material.
Mat 510 Enhance teaching / snaptutorial.comBaileya19
This document contains three sets of questions for the MAT 510 Final Exam. Each set contains 25 multiple choice questions covering topics in statistics, statistical process control, design of experiments, and statistical modeling. The questions assess understanding of concepts like variation, process capability, control charts, regression analysis, and experimental design.
MAT 510 Effective Communication - tutorialrank.comBartholomew46
For more course tutorials visit
www.tutorialrank.com
MAT 510 Final Exam Set 1
Question 1
Improvement is needed for an organization to survive because:
Question 2
For more course tutorials visit
www.newtonhelp.com
MAT 510 Final Exam Set 1
Question 1
Improvement is needed for an organization to survive because:
Question 2
The Statistical Thinking Strategy has significant commonality with the scientific method. Which of the following statistical thinking principles is NOT generally associated with the scientific method?
Question 3
MAT 510 RANK Education Planning--mat510rank.comWindyMiller24
This document contains 3 sets of questions for the MAT 510 Final Exam. Each set contains 25 multiple choice questions covering topics related to statistical process improvement, statistical thinking strategies, control charts, design of experiments, and regression analysis. Students are instructed to visit a website for additional course materials.
mat 300,strayer mat 300,mat 300 entire course new,mat 300 discussion questions,strayer mat 300 week 1,strayer mat 300 week 2,strayer mat 300 week 3,strayer mat 300 week 4,strayer mat 300 week 5,mat 300 case study,mat 300 discussion correlation and regression,mat 300 graphical representations,strayer mat 300 tutorials,strayer mat 300 assignments,mat 300 help
mat 510,stayer mat 510,stayer mat 510 complete course,stayer mat 510 entire course,stayer mat 510 week 1,stayer mat 510 week 2,stayer mat 510 week 3,stayer mat 510 week 4,stayer mat 510 week 6,stayer mat 510 week 7,stayer mat 510 week 8,stayer mat 510 week 9,mat 510 final exam new,mat 510 midterm exam new,mat 510 tutorials,mat 510 assignments,mat 510 help
Bruce Ingraham (Ingraham Consulting) gave a talk on Satisfaction and Loyalty at the SF Data Mining event: http://www.meetup.com/Data-Mining/events/68283282/
Quantitative Analysis For Management 13th Edition Render Test BankJescieer
This document contains 47 multiple choice questions about quantitative analysis and business analytics. It covers topics such as the quantitative analysis approach, business analytics categories, modeling, and developing quantitative analysis models. Several questions define key terms used in quantitative analysis like decision variables, parameters, algorithms, and sensitivity analysis. Other questions ask about the history and applications of quantitative analysis techniques.
This document provides an agenda and summaries of various statistical tests and analyses performed on call volume and customer satisfaction survey data. It includes:
1. A t-test and z-test finding no statistically significant difference between forecasted and actual daily call volumes.
2. A chi-square test finding a significant relationship between customer satisfaction survey results and whether calls originated from phone or email.
3. An individuals control chart finding two holidays caused unusually low call volumes but otherwise volumes were generally stable with no trends or patterns.
4. A binary logistic regression modeling customer satisfaction as predicted by ticket age, source, status reason, and resolution method, finding ticket age and status reason were significant predictors.
Analytical Hierarchical Process has been used as a useful methodology for multi-criteria decision making environments with substantial applications in recent years. But the weakness of the traditional AHP method lies in the use of subjective judgement based assessment and standardized scale for pairwise comparison matrix creation. The paper proposes a Condorcet Voting Theory based AHP method to solve multi criteria decision making problems where Analytical Hierarchy Process (AHP) is combined with Condorcet theory based preferential voting technique followed by a quantitative ratio method for framing the comparison matrix instead of the standard importance scale in traditional AHP approach. The consistency ratio (CR) is calculated for both the approaches to determine and compare the consistency of both the methods. The results reveal Condorcet- AHP method to be superior generating lower consistency ratio and more accurate ranking of the criterion for solving MCDM problems.
PROVIDING A METHOD FOR DETERMINING THE INDEX OF CUSTOMER CHURN IN INDUSTRYIJITCA Journal
Churn customer, one of the most important issues in customer relationship management and marketing is especially in industries such as telecommunications, the financial and insurance. In recent decades much
research has been done in this area. In this research, the index set for the reasons set reason churn customers for our customers is of particular importance. In this study we are intended to provide a formula for the index churn customers, the better to understand the reasons for customers to provide churn. Therefore, in order to evaluate the formula provided through six Classification methods (Decision tree QUEST, Decision tree C5.0, Decision tree CHAID, Decision trees CART, Bayesian network, Neural network) to evaluate the formula will be involved with individual indicators
Application of the analytic hierarchy process (AHP) for selection of forecast...Gurdal Ertek
In this paper, we described an application of the Analytic Hierarchy Process (AHP) for the ranking and selection of forecasting software. AHP is a multi-criteria decision making (MCDM) approach, which is based on the pair-wise comparison
of elements of a given set with respect to multiple criteria. Even though there are applications of the AHP to software selection problems, we have not encountered a study that involves forecasting software. We started our analysis by filtering
among forecasting software that were found on the Internet by undergraduate students as a part of a course project. Then we processed a second filtering step, where we reduced the number of software to be examined even further. Finally we
constructed the comparison matrices based upon the evaluations of three “semiexperts”, and obtained a ranking of forecasting software of the selected software using the Expert Choice software. We report our findings and our insights, together with the results of a sensitivity analysis.
http://research.sabanciuniv.edu.
This document provides instruction on using the 1 variance test for hypothesis testing. It begins with an overview of why hypothesis testing is needed to build a transfer function model. It then reviews the 4-step process for hypothesis testing and provides a decision tree to help select the appropriate statistical test based on data type and characteristics. The document demonstrates how to perform a 1 variance test using Minitab through examples comparing standard deviation to a target value. It concludes by prompting the reader to apply the 1 variance test to factors identified in a previous lesson and consider how the results could influence organizational decisions and goals.
This document discusses causal inference techniques for machine learning, including:
- Correlation does not imply causation, and observational data can be biased by confounding variables. Randomization and counterfactual modeling are introduced as alternatives.
- Inverse propensity scoring is presented as a method for estimating treatment effects from observational data by reweighting samples based on their propensity to receive treatment.
- Instrumental variable regression is discussed as another technique, using variables that influence the treatment but not the outcome except through treatment. Scalable methods for instrumental variable regression on large datasets are proposed.
- Challenges with weak instruments are noted, as instrumental variable estimates can become more biased than purely correlational models when instruments are weak
The document summarizes a student group project on forecasting. It thanks their professor and lists group members. It then outlines topics covered, including introduction to forecasting, qualitative and quantitative forecasting methods, and pricing policies. Forecasting methods discussed include Delphi method, market research, product life cycle, and expert judgment. Quantitative methods include trend extrapolation, consensus, simulation/analogy, and cross impact analysis.
This document summarizes a study that used machine learning algorithms to predict happiness based on survey data from over 4,600 users. The researchers analyzed data from 100+ survey questions along with demographics to predict if users identified as happy or unhappy. They tested multiple algorithms including naive Bayes, decision trees, random forests, gradient boosting, and support vector machines. Gradient boosting achieved the highest AUC score of 0.68 on test data, outperforming other algorithms. The researchers concluded machine learning can effectively predict happiness from subjective survey responses, with ensemble methods like gradient boosting and random forests performing best.
Using Investigative Analytics to Speed New Drugs to MarketCognizant
Investigative analytics can help speed up clinical trials and bring new drugs to market faster. It does this by improving data quality monitoring during trials. Exploratory data analysis and inferential statistics are two types of analysis that can be used to identify data quality issues. Exploratory analysis uses techniques like outlier analysis and repeated value analysis to detect anomalies in the data. Inferential statistics helps confirm findings and identify which sites require auditing. Together these methods provide a more cost-effective way to ensure data integrity and compliance during clinical trials.
Assessment of Strategies in the Diffusion Simulation GameRod Myers
This document summarizes research analyzing gameplay data from a simulation game designed to teach diffusion of innovations theory. The researchers assessed which strategies were most successful in the game by comparing patterns of frequent activities, sequences, and joint occurrences between groups that successfully or unsuccessfully adopted all innovations. While some predicted strategies from diffusion theory were more common in successful games, others were not. The researchers suggest future work to collect more granular data and address misconceptions through instructional strategies like debriefing.
Hypothesis Testing: Central Tendency – Non-Normal (Compare 1:Standard)Matt Hansen
An extension on hypothesis testing, this lesson reviews the 1 Sample Sign & Wilcoxon tests as central tendency measurements for non-normal distributions.
mat 510,stayer mat 510,stayer mat 510 complete course,stayer mat 510 entire course,stayer mat 510 week 1,stayer mat 510 week 2,stayer mat 510 week 3,stayer mat 510 week 4,stayer mat 510 week 6,stayer mat 510 week 7,stayer mat 510 week 8,stayer mat 510 week 9,mat 510 final exam new,mat 510 midterm exam new,mat 510 tutorials,mat 510 assignments,mat 510 help
This document provides a list of 25 questions that appear to be from a final exam for the MAT 510 statistics course. It includes questions about statistical concepts like control charts, regression analysis, experimental design, hypothesis testing, and intervals. The document also provides links to resources for accessing tutorials and more exam questions on the topic.
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mat 300,strayer mat 300,mat 300 entire course new,mat 300 discussion questions,strayer mat 300 week 1,strayer mat 300 week 2,strayer mat 300 week 3,strayer mat 300 week 4,strayer mat 300 week 5,mat 300 case study,mat 300 discussion correlation and regression,mat 300 graphical representations,strayer mat 300 tutorials,strayer mat 300 assignments,mat 300 help
mat 510,stayer mat 510,stayer mat 510 complete course,stayer mat 510 entire course,stayer mat 510 week 1,stayer mat 510 week 2,stayer mat 510 week 3,stayer mat 510 week 4,stayer mat 510 week 6,stayer mat 510 week 7,stayer mat 510 week 8,stayer mat 510 week 9,mat 510 final exam new,mat 510 midterm exam new,mat 510 tutorials,mat 510 assignments,mat 510 help
Bruce Ingraham (Ingraham Consulting) gave a talk on Satisfaction and Loyalty at the SF Data Mining event: http://www.meetup.com/Data-Mining/events/68283282/
Quantitative Analysis For Management 13th Edition Render Test BankJescieer
This document contains 47 multiple choice questions about quantitative analysis and business analytics. It covers topics such as the quantitative analysis approach, business analytics categories, modeling, and developing quantitative analysis models. Several questions define key terms used in quantitative analysis like decision variables, parameters, algorithms, and sensitivity analysis. Other questions ask about the history and applications of quantitative analysis techniques.
This document provides an agenda and summaries of various statistical tests and analyses performed on call volume and customer satisfaction survey data. It includes:
1. A t-test and z-test finding no statistically significant difference between forecasted and actual daily call volumes.
2. A chi-square test finding a significant relationship between customer satisfaction survey results and whether calls originated from phone or email.
3. An individuals control chart finding two holidays caused unusually low call volumes but otherwise volumes were generally stable with no trends or patterns.
4. A binary logistic regression modeling customer satisfaction as predicted by ticket age, source, status reason, and resolution method, finding ticket age and status reason were significant predictors.
Analytical Hierarchical Process has been used as a useful methodology for multi-criteria decision making environments with substantial applications in recent years. But the weakness of the traditional AHP method lies in the use of subjective judgement based assessment and standardized scale for pairwise comparison matrix creation. The paper proposes a Condorcet Voting Theory based AHP method to solve multi criteria decision making problems where Analytical Hierarchy Process (AHP) is combined with Condorcet theory based preferential voting technique followed by a quantitative ratio method for framing the comparison matrix instead of the standard importance scale in traditional AHP approach. The consistency ratio (CR) is calculated for both the approaches to determine and compare the consistency of both the methods. The results reveal Condorcet- AHP method to be superior generating lower consistency ratio and more accurate ranking of the criterion for solving MCDM problems.
PROVIDING A METHOD FOR DETERMINING THE INDEX OF CUSTOMER CHURN IN INDUSTRYIJITCA Journal
Churn customer, one of the most important issues in customer relationship management and marketing is especially in industries such as telecommunications, the financial and insurance. In recent decades much
research has been done in this area. In this research, the index set for the reasons set reason churn customers for our customers is of particular importance. In this study we are intended to provide a formula for the index churn customers, the better to understand the reasons for customers to provide churn. Therefore, in order to evaluate the formula provided through six Classification methods (Decision tree QUEST, Decision tree C5.0, Decision tree CHAID, Decision trees CART, Bayesian network, Neural network) to evaluate the formula will be involved with individual indicators
Application of the analytic hierarchy process (AHP) for selection of forecast...Gurdal Ertek
In this paper, we described an application of the Analytic Hierarchy Process (AHP) for the ranking and selection of forecasting software. AHP is a multi-criteria decision making (MCDM) approach, which is based on the pair-wise comparison
of elements of a given set with respect to multiple criteria. Even though there are applications of the AHP to software selection problems, we have not encountered a study that involves forecasting software. We started our analysis by filtering
among forecasting software that were found on the Internet by undergraduate students as a part of a course project. Then we processed a second filtering step, where we reduced the number of software to be examined even further. Finally we
constructed the comparison matrices based upon the evaluations of three “semiexperts”, and obtained a ranking of forecasting software of the selected software using the Expert Choice software. We report our findings and our insights, together with the results of a sensitivity analysis.
http://research.sabanciuniv.edu.
This document provides instruction on using the 1 variance test for hypothesis testing. It begins with an overview of why hypothesis testing is needed to build a transfer function model. It then reviews the 4-step process for hypothesis testing and provides a decision tree to help select the appropriate statistical test based on data type and characteristics. The document demonstrates how to perform a 1 variance test using Minitab through examples comparing standard deviation to a target value. It concludes by prompting the reader to apply the 1 variance test to factors identified in a previous lesson and consider how the results could influence organizational decisions and goals.
This document discusses causal inference techniques for machine learning, including:
- Correlation does not imply causation, and observational data can be biased by confounding variables. Randomization and counterfactual modeling are introduced as alternatives.
- Inverse propensity scoring is presented as a method for estimating treatment effects from observational data by reweighting samples based on their propensity to receive treatment.
- Instrumental variable regression is discussed as another technique, using variables that influence the treatment but not the outcome except through treatment. Scalable methods for instrumental variable regression on large datasets are proposed.
- Challenges with weak instruments are noted, as instrumental variable estimates can become more biased than purely correlational models when instruments are weak
The document summarizes a student group project on forecasting. It thanks their professor and lists group members. It then outlines topics covered, including introduction to forecasting, qualitative and quantitative forecasting methods, and pricing policies. Forecasting methods discussed include Delphi method, market research, product life cycle, and expert judgment. Quantitative methods include trend extrapolation, consensus, simulation/analogy, and cross impact analysis.
This document summarizes a study that used machine learning algorithms to predict happiness based on survey data from over 4,600 users. The researchers analyzed data from 100+ survey questions along with demographics to predict if users identified as happy or unhappy. They tested multiple algorithms including naive Bayes, decision trees, random forests, gradient boosting, and support vector machines. Gradient boosting achieved the highest AUC score of 0.68 on test data, outperforming other algorithms. The researchers concluded machine learning can effectively predict happiness from subjective survey responses, with ensemble methods like gradient boosting and random forests performing best.
Using Investigative Analytics to Speed New Drugs to MarketCognizant
Investigative analytics can help speed up clinical trials and bring new drugs to market faster. It does this by improving data quality monitoring during trials. Exploratory data analysis and inferential statistics are two types of analysis that can be used to identify data quality issues. Exploratory analysis uses techniques like outlier analysis and repeated value analysis to detect anomalies in the data. Inferential statistics helps confirm findings and identify which sites require auditing. Together these methods provide a more cost-effective way to ensure data integrity and compliance during clinical trials.
Assessment of Strategies in the Diffusion Simulation GameRod Myers
This document summarizes research analyzing gameplay data from a simulation game designed to teach diffusion of innovations theory. The researchers assessed which strategies were most successful in the game by comparing patterns of frequent activities, sequences, and joint occurrences between groups that successfully or unsuccessfully adopted all innovations. While some predicted strategies from diffusion theory were more common in successful games, others were not. The researchers suggest future work to collect more granular data and address misconceptions through instructional strategies like debriefing.
Hypothesis Testing: Central Tendency – Non-Normal (Compare 1:Standard)Matt Hansen
An extension on hypothesis testing, this lesson reviews the 1 Sample Sign & Wilcoxon tests as central tendency measurements for non-normal distributions.
mat 510,stayer mat 510,stayer mat 510 complete course,stayer mat 510 entire course,stayer mat 510 week 1,stayer mat 510 week 2,stayer mat 510 week 3,stayer mat 510 week 4,stayer mat 510 week 6,stayer mat 510 week 7,stayer mat 510 week 8,stayer mat 510 week 9,mat 510 final exam new,mat 510 midterm exam new,mat 510 tutorials,mat 510 assignments,mat 510 help
This document provides a list of 25 questions that appear to be from a final exam for the MAT 510 statistics course. It includes questions about statistical concepts like control charts, regression analysis, experimental design, hypothesis testing, and intervals. The document also provides links to resources for accessing tutorials and more exam questions on the topic.
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mat 510,stayer mat 510,stayer mat 510 complete course,stayer mat 510 entire course,stayer mat 510 week 1,stayer mat 510 week 2,stayer mat 510 week 3,stayer mat 510 week 4,stayer mat 510 week 6,stayer mat 510 week 7,stayer mat 510 week 8,stayer mat 510 week 9,mat 510 final exam new,mat 510 midterm exam new,mat 510 tutorials,mat 510 assignments,mat 510 help
mat 510,stayer mat 510,stayer mat 510 complete course,stayer mat 510 entire course,stayer mat 510 week 1,stayer mat 510 week 2,stayer mat 510 week 3,stayer mat 510 week 4,stayer mat 510 week 6,stayer mat 510 week 7,stayer mat 510 week 8,stayer mat 510 week 9,mat 510 final exam new,mat 510 midterm exam new,mat 510 tutorials,mat 510 assignments,mat 510 help
mat 510,stayer mat 510,stayer mat 510 complete course,stayer mat 510 entire course,stayer mat 510 week 1,stayer mat 510 week 2,stayer mat 510 week 3,stayer mat 510 week 4,stayer mat 510 week 6,stayer mat 510 week 7,stayer mat 510 week 8,stayer mat 510 week 9,mat 510 final exam new,mat 510 midterm exam new,mat 510 tutorials,mat 510 assignments,mat 510 help
1. What type of research uses numeric measurement data (Points .docxpaynetawnya
1. What type of research uses numeric measurement data? (Points : 3)
2. What type of research uses a research hypothesis? (Points : 3)
3. What type of research does not use statistical data analysis? (Points : 3)
4. What type of research preempts all other types of research endeavors? (Points : 3)
5. Business research is a type of ________________ inquiry. (Points : 3)
6. What are the three main types of non-probability sampling used in business research? (Points : 3)
7. In a situation where in a confidence level .01 what percent of the measurement results are left to chance? (Points : 3)
8. What is the most important ingredient in a statistical testing procedure? (Points : 3)
9. If a production manager wanted to determine whether or not the first shift was processing more widgets than the second shift, what type of statistical process would be used? (Points : 3)
10. What type of t test seeks to determine whether or not a relationship exists in one sample over two conditions? _______________ (Points : 3)
11. Which of the examples below represent the Ratio level of scaling?
A) A high temperature of 83 degrees Fahrenheit
B) A survey result that 24 students work full time, 36, part time.
C) Bill is consistently rated most effective communicator of his group.
D) Gallup says that 60% of the voters support the incumbent.
E) Pick up three pounds of ground beef please.
F) Patty acts as expected based on her first-born family position.
G) Seattle at an altitude of 67 feet is higher than Death Valley at an altitude of – 120.
H) The door is 37 inches wide, the door frame is 36 inches wide.
(Points : 3)
B, C and F
D, E and G
C, F and H
A, E and H
12. Select those issues that only relate to selecting a specific statistical test. (Do not select items common to all tests or not applying to statistical tests.) (Points : 3)
The distribution (shape) of the population (e.g., normal, skewed, flat, etc.)
The measurement scale/nature of the data being evaluated (nominal, ordinal, interval or ratio)
The size of the population (assuming it is much larger than any samples)
The level of significance (_) you wish to place on the test results
Whether you have matched/related or unmatched/unrelated samples
The degrees of freedom (sample size) associated with your sample(s)
The statement of the null and research hypotheses
Whether the sample was stratified or not
13. Which of the following apply to Populations?
A) parameter
B) “Roman” letters, i.e.: x, s
C) A bounded, defined complete group (people, objects, etc.) having something in common to be described in its totality
D) “Greek” letters, i.e.: μ, σ
E) One or more subsets of a larger defined group, used to represent the larger group
F) 170 Republicans selected randomly from King County voter records
G) All Democrats in the state of Washington (totality)
(Points : 3)
A, B ...
1. What type of research uses numeric measurement data (Points .docxjackiewalcutt
1. What type of research uses numeric measurement data? (Points : 3)
2. What type of research uses a research hypothesis? (Points : 3)
3. What type of research does not use statistical data analysis? (Points : 3)
4. What type of research preempts all other types of research endeavors? (Points : 3)
5. Business research is a type of ________________ inquiry. (Points : 3)
6. What are the three main types of non-probability sampling used in business research? (Points : 3)
7. In a situation where in a confidence level .01 what percent of the measurement results are left to chance? (Points : 3)
8. What is the most important ingredient in a statistical testing procedure? (Points : 3)
9. If a production manager wanted to determine whether or not the first shift was processing more widgets than the second shift, what type of statistical process would be used? (Points : 3)
10. What type of t test seeks to determine whether or not a relationship exists in one sample over two conditions? _______________ (Points : 3)
11.
Which of the examples below represent the Ratio level of scaling?
A) A high temperature of 83 degrees Fahrenheit
B) A survey result that 24 students work full time, 36, part time.
C) Bill is consistently rated most effective communicator of his group.
D) Gallup says that 60% of the voters support the incumbent.
E) Pick up three pounds of ground beef please.
F) Patty acts as expected based on her first-born family position.
G) Seattle at an altitude of 67 feet is higher than Death Valley at an altitude of – 120.
H) The door is 37 inches wide, the door frame is 36 inches wide.
(Points : 3)
B, C and F
D, E and G
C, F and H
A, E and H
12. Select those issues that only relate to selecting a specific statistical test. (Do not select items common to all tests or not applying to statistical tests.) (Points : 3)
The distribution (shape) of the population (e.g., normal, skewed, flat, etc.)
The measurement scale/nature of the data being evaluated (nominal, ordinal, interval or ratio)
The size of the population (assuming it is much larger than any samples)
The level of significance (_) you wish to place on the test results
Whether you have matched/related or unmatched/unrelated samples
The degrees of freedom (sample size) associated with your sample(s)
The statement of the null and research hypotheses
Whether the sample was stratified or not
13.
Which of the following apply to Populations?
A) parameter
B) “Roman” letters, i.e.: x, s
C) A bounded, defined complete group (people, objects, etc.) having something in common to be described in its totality
D) “Greek” letters, i.e.: μ, σ
E) One or more subsets of a larger defined group, used to represent the larger group
F) 170 Republicans selected randomly from King County voter records
G) All Democrats in the state of Washington (totality)
(Points : 3)
A, B, ...
Measuring effectiveness of machine learning systemsAmit Sharma
Many online systems, such as recommender systems or ad systems, are increasingly being used in societally critical domains such as education, healthcare, finance and governance. A natural question to ask is about their effectiveness, which is often measured using observational metrics. However, these metrics hide cause-and-effect processes between these systems, people's behavior and outcomes. I will present a causal framework that allows us to tackle questions about the effects of algorithmic systems and demonstrate its usage through evaluation of Amazon's recommender system and a major search engine. I will also discuss how such evaluations can lead to metrics for designing better systems.
What type of research uses numeric measurement data (Points 3.docxalanfhall8953
What type of research uses numeric measurement data? (Points : 3)
Quantitative research uses numeric measurement data.
Question 2. 2. What type of research uses a research hypothesis? (Points : 3)
Statistics and Probability
Question 3. 3. What type of research does not use statistical data analysis? (Points : 3)
Question 4. 4. What type of research preempts all other types of research endeavors? (Points : 3)
Statistical Research
Question 5. 5. Business research is a type of ________________ inquiry. (Points : 3)
Question 6. 6. What are the three main types of non-probability sampling used in business research? (Points : 3)
Question 7. 7. In a situation where in a confidence level .01 what percent of the measurement results are left to chance? (Points : 3)
Question 8. 8. What is the most important ingredient in a statistical testing procedure? (Points : 3)
Question 9. 9. If a production manager wanted to determine whether or not the first shift was processing more widgets than the second shift, what type of statistical process would be used? (Points : 3)
Question 10. 10. What type of t test seeks to determine whether or not a relationship exists in one sample over two conditions? _______________ (Points : 3)
Question 11. 11.
Which of the examples below represent the Ratio level of scaling?
A) A high temperature of 83 degrees Fahrenheit
B) A survey result that 24 students work full time, 36, part time.
C) Bill is consistently rated most effective communicator of his group.
D) Gallup says that 60% of the voters support the incumbent.
E) Pick up three pounds of ground beef please.
F) Patty acts as expected based on her first-born family position.
G) Seattle at an altitude of 67 feet is higher than Death Valley at an altitude of – 120.
H) The door is 37 inches wide, the door frame is 36 inches wide.
(Points : 3)
B, C and F
D, E and G
C, F and H
A, E and H
Question 12. 12. Select those issues that only relate to selecting a specific statistical test. (Do not select items common to all tests or not applying to statistical tests.) (Points : 3)
The distribution (shape) of the population (e.g., normal, skewed, flat, etc.)
The measurement scale/nature of the data being evaluated (nominal, ordinal, interval or ratio)
The size of the population (assuming it is much larger than any samples)
The level of significance (_) you wish to place on the test results
Whether you have matched/related or unmatched/unrelated samples
The degrees of freedom (sample size) associated with your sample(s)
The statement of the null and research hypotheses
Whether the sample was stratified or not
Question 13. 13.
Which of the following apply to Populations?
A) parameter
B) “Roman” letters, i.e.: x, s
C) A bounded, defined.
Descriptive Statistics and Interpretation Grading GuideQNT5.docxtheodorelove43763
This document outlines a sampling and data collection plan to test whether implementing a Total Quality Management (TQM) system will increase product quality at PhoenixSolar. The target population includes production workers, managers, engineers, technicians, and customers who will provide insights through focus groups and surveys. A sample size of 385 is needed for a 95% confidence level. Internal employees will participate in exploratory focus groups, while external groups like technicians and customers will complete paper, email, and installation surveys. Validity, reliability, and privacy protocols are defined. The plan is to analyze responses over six months to determine if TQM increases quality and customer satisfaction at PhoenixSolar.
This document discusses research methods and data collection techniques. It provides examples of a descriptive study design being used to explore employee empowerment policies and strategies. It also describes various quantitative and qualitative data collection methods, noting their advantages like collecting information from many people quickly, and disadvantages like not being able to determine causation. Cross-sectional studies are described as collecting data at a single point in time, making them less costly but unable to examine changes over time.
RES 351 Massive Success / snaptutorial.com351McdonaldRyan0
Differentiate between the scientific method and applied research. Which one is most often used in business? Provide an example of either that might be appropriate from your current or previous place of employment. You are the manager of a hotel. There have been several complaints from guests relating to employee attitude. Provide a description of three different types of research that might be appropriate for this situation.
For more course tutorials visit
www.newtonhelp.com
QNT 565 Week 1 Individual Assignment Business Research Case Study
QNT 565 Week 1 DQ 1
QNT 565 Week 1 DQ 2
QNT 565 Week 2 Learning Team Assignment Research Proposal Part I
For more course tutorials visit
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QNT 565 Week 1 Individual Assignment Business Research Case Study
QNT 565 Week 1 DQ 1
QNT 565 Week 1 DQ 2
QNT 565 Week 2 Learning Team Assignment Research Proposal Part I
The document discusses research design and its components. It defines exploratory, descriptive, and causal research designs. Exploratory research aims to gain insights and is flexible, while descriptive research describes characteristics and causal research determines causes and effects through experiments. The case study describes Citicorp using exploratory research like focus groups to understand senior citizens' needs. Descriptive surveys tested product features, and causal test marketing in branches determined if the product launched nationally.
Midterm Exam The purpose of this examination is tsimisterchristen
Midterm Exam
The purpose of this examination is to practice using SPSS to analyze the data. You should attempt each of the questions detailed on the following pages. You will need to have read through the provided materials in the class
first to familiarize yourself with its contents and revise the various statistical procedures covered.
There are a number of parts to this exercise.
· In Part A, you are asked to create your own data file and to perform analyses on this data file.
· In Part B, you will be asked to interpret some output generated by SPSS.
· In Part C, you will be using the data file attached in mid-term dropbox. Full details of this data file is also included in the dropbox, and you should read through this section thoroughly before beginning. This is a real data file that is condensed from data collected by Postgraduate Diploma of Psychology students.
· In Part D, you will estimate sample size by using G*power.
The following rules apply for this assignment:
· If applicable, answer the question under three headings.
1. SPSS syntax
2. Necessary SPSS output
3. Interpretation and/or conclusion of your analysis
· Organize your work in a reasonably neat and coherent way. Work scattered all over the page without a clear ordering will receive very little credit.
· Only necessary output from the SPSS software should be in the submitted exam.
· You are required to work INDIVIDUALLY.
· Justification does not include hundreds of pages of computer output with hopes you covered all aspects. Justification is a well thought out and well-articulated rationale for what you do!
Do not JUST SUBMIT SPSS OUTPUTS or CODES
Part A
Sample questionnaire
1. Sex
___ Male ___ Female
2. Age in years __________
3. Education level (please indicate the highest level of schooling that you completed)
_____ Year 10 _____ Year 12 ______ University or College
4. Are you currently on a diet to lose weight?
____ Yes ____ No
Mastery Scale
Please indicate how much you either agree or disagree with each of the following statements.
Write a number from 1 to 4 on the line next to each statement.
strongly disagree 1 2 3 4 strongly agree
1. ______ I have little control over the things that happen to me
2. ______ I can do just about anything I really set my mind to
3. ______ There is really no way I can solve some of the problems I have
4. ______ There is little I can do to change many of the important things in my life
5. ______ What happens to me in the future mostly depends on me
6. ______ I often feel helpless in dealing with the problems of life
7. ______ Sometimes I feel that I'm being pushed around in life
1. Prepare a codebook for this questionnaire, detailing each of the variable names and codes to be used to prepare the data for entry into SPSS
. (3 ...
This document discusses defining the problem statement in research methodology. It explains that the first steps are to identify the broad problem area through preliminary research, then define the specific research problem. A good problem statement includes a research objective that explains why the research is being conducted and research questions that specify what is to be learned. It provides examples of exploratory, descriptive, and causal research questions. Finally, it outlines the key components of a research proposal such as the background, problem statement, research design, timeframe and budget.
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A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
1. MAT 510 Final Exam (3 Set, 100% Score)
For more classes visit
www.snaptutorial.com
MAT 510 Final Exam Set 1
Question 1
Improvement is needed for an organization to survive because:
Question 2
The Statistical Thinking Strategy has significant commonality
with the scientific method. Which of the following statistical thinking
principles is NOT generally associated with the scientific method?
Question 3
Viewing a business as a system is important because:
Question 4
Improving the quality of process measurements is:
Question 5
Suppose the Problem Solving Framework were used to attack
a problem where the process in question was inherently stable. Which
of the following would be a likely result of this effort?
Question 6
2. The histogram below showing ages of credit card holders
displays what type of distribution?
Question 7
A main purpose of a control chart is to:
Question 8
The histogram has which of the following limitations?
Question 9
Non-random and unpredictable patterns on a control chart
indicate:
Question 10
A comparison between the Cp and Cpk for a process would
find which of the following to be true?
Question 11
A statistic that is used to indicate too much correlation
between the predictors in a regression analysis is called the:
Question 12
An Adjusted R-square value is a correlation coefficient that
has been modified to account for:
Question 13
3. Tips for building useful models include:
Question 14
The model residuals are examined to:
Question 15
Results of regression analysis are often abused in the
following ways:
Question 16
The basic reason for randomness of sampling in design of
experiments is:
Question 17
In every experiment there is experimental error. Which of the
following statements is true?
Question 18
An aspect of good experimental strategy is to study the effects
of the variables
(Xs) over a wide range. This strategy increases the chances that
effects will be found because:
Question 19
This technique is used to eliminate the effects of nuisance
factors (e.g., machines, day of week, and season of year) from an
experiment.
Question 20
A 32 experiment means that we are experimenting with:
4. Question 21
We ran a taste-test to see which soft drink employees in our
company prefer. We had 100 employees, selected as randomly as
possible, taste test two brands and determine which they preferred.
Which of the following would be a reasonable statistical analysis to
determine if there is a clear preference among our employees?
Question 22
We are testing the null hypothesis that the average monthly
revenue between four insurance offices is the same. We obtained a p-
value of .07. Which of the following would be an appropriate
conclusion about the population?
Question 23
For which of the following scenarios am I most likely to
utilize a Chi-squared test?
Question 24
Which of the following could be considered a prediction
interval?
Question 25
I have made a mistake in copying data into my computer to
perform a large regression model with 20 independent variables.
Basically, I ended up with random data in each of the columns.
However, I accidentally ran the regression anyway. Making
reasonable assumptions, which of the following outcomes is most
likely?
MAT 510 Final Exam Set 2
5. • Question 1
In every experiment there is experimental error. Which of the
following statements is true?
• Question 2
This technique is used to eliminate the effects of nuisance
factors (e.g., machines, day of week, and season of year) from an
experiment.
• Question 3
An aspect of good experimental strategy is to study the effects
of the variables
(Xs) over a wide range. This strategy increases the chances that
effects will be found because:
• Question 4
A 32 experiment means that we are experimenting with:
• Question 5
Given the plot below, what might you suspect about factors A
and B?
• Question 6
Which type of variation was critical to resolving the realized
revenue case study?
• Question 7
6. Figure 2.15 displays a model of the statistics discipline,
showing the relationship between statistical thinking, statistical
engineering, and statistical methods and tools. Which of the following
is a principle illustrated by this model?
• Question 8
The model residuals are examined to:
• Question 9
A statistic that is used to indicate too much correlation
between the predictors in a regression analysis is called the:
• Question 10
The best way to evaluate the validity of a statistical model is:
• Question 11
George Box tells us ―all models are wrong but some are
useful‖. By this comment he means:
• Question 12
Models based on subject matter fundamentals (theory) are
generally better than statistical models for:
• Question 13
We are testing the null hypothesis that the average monthly
revenue between four insurance offices is the same. We obtained a p-
value of .07. Which of the following would be an appropriate
conclusion about the population?
• Question 14
7. I have calculated an interval to document the uncertainty in
my estimate of the long-term standard deviation I will experience in
time to pay invoices in my business going forward. Baaed on this
information, this should be considered what type of interval?
• Question 15
After running an ANOVA comparing the average years of
experience between five different job classifications, we obtained a p
value of .02. Which of the following would be a reasonable
conclusion concerning the population in this case?
• Question 16
For which of the following scenarios am I most likely to
utilize a Chi-squared test?
• Question 17
Which of the following could be considered a prediction
interval?
• Question 18
Improvement is needed for an organization to survive because:
• Question 19
Control limits were originally defined at the three-sigma level
because:
• Question 20
If my histogram appears bimodal, what tool might help me
evaluate why?
8. • Question 21
In evaluating data on our process outputs, four characteristics
we might investigate are: central tendency, variation, shape of
distribution, and stability. Which of the following tools would be
most helpful to determine stability of the process?
• Question 22
The histogram has which of the following limitations?
• Question 23
Non-random and unpredictable patterns on a control chart
indicate:
• Question 24
Refer to Figure 3.21 and choose the correct statement for the
measurements
• Question 25
Processes are important because:
MAT 510 Final Exam Set 3
• Question 1
Lean manufacturing, Six Sigma, Total Quality Management
are some of the new systems for managing and improving an
organization. What is a common theme running through these
approaches?
• Question 2
9. Figure 2.15 displays a model of the statistics discipline,
showing the relationship between statistical thinking, statistical
engineering, and statistical methods and tools. Which of the following
is a principle illustrated by this model?
• Question 3
The Hidden Factory is:
• Question 4
Sub-optimization occurs when:
• Question 5
Suppose the Process Improvement Framework were used to
attack a problem where the process in question was inherently
unstable. Which of the following would be a likely result of this
effort?
• Question 6
The histogram has which of the following limitations?
• Question 7
Control limits were originally defined at the three-sigma level
because:
• Question 8
In evaluating data on our process outputs, four characteristics
we might investigate are: central tendency, variation, shape of
distribution, and stability. Which of the following tools would be
most helpful to determine the shape of the distribution?
• Question 9
10. If my histogram appears bimodal, what tool might help me
evaluate why?
• Question 10
A process is said to be capable if:
• Question 11
Results of regression analysis are often abused in the
following ways:
• Question 12
The model residuals are examined to:
• Question 13
The best way to evaluate the validity of a statistical model is:
• Question 14
Process models are used to:
• Question 15
An Adjusted R-square value is a correlation coefficient that
has been modified to account for:
• Question 16
The basic reason for randomness of sampling in design of
experiments is:
• Question 17
11. Identify the assumption that is NOT made when conducting an
experiment:
• Question 18
In the experimental layout below, which are the most
important factors?
Factor A Factor B Factor C Y
-1 -1 -1 150
1 -1 -1 148
-1 1 -1 156
1 1 -1 158
-1 -1 1 137
1 -1 1 132
-1 1 1 145
1 1 1 147
***************************
&
MAT 510 Midterm Exam (3 Set, 100% Score)
For more classes visit
www.snaptutorial.com
MAT 510 Midterm Exam Set 1
MAT 510 MIDTERM First Attempt
• Question 1
Improvement is needed for an organization to survive because:
12. • Question 2
Why is it so difficult for a person or an organization to improve?
• Question 3
A SIPOC map is:
• Question 4
Lean manufacturing, Six Sigma, Total Quality Management are some
of the new systems for managing and improving an organization.
What is a common theme running through these approaches?
• Question 5
Problem solving activities typically include:
• Question 6
The fact that processes tend to be dynamic, rather than static, is a key
principle of statistical thinking. Which of the following is a natural
consequence of this fact?
• Question 7
The Statistical Thinking Strategy has significant commonality with
the scientific method. Which of the following statistical thinking
principles is NOT generally associated with the scientific method?
• Question 8
Figure 2.16 shows a chart of the Dow Jones Industrial Average from
1990 through 2011. This plot reveals some obvious change points,
such as the 2008 financial collapse, and some unexplainable short-
13. term variation. Assuming that these are the only sources of variation
in this plot, what source of variation is NOT visible in the plot?
• Question 9
In Figure 2.11, Coach Hau presented data showing where goals were
scored on crosses. This produced unexpected results, in terms of
where the goals came from, and helped his team determine an
effective strategy on free kicks. This is an example of what principle
of statistical thinking?
• Question 10
The Statistical Thinking Strategy illustrated in Figure 2.14 provides a
graphic of the overall approach to driving improvement through
statistical thinking. Which of the following is a key principle
illustrated in this specific graph?
• Question 11
In the soccer case study, Coach Hau developed a flowchart of the
steps one goes through to properly ―head‖ the ball, and then
developed unique drills to develop the team’s skills in each of these
steps. This approach is an example of what statistical thinking
principle?
• Question 12
A manufacturing process has been experiencing problems. The
operators charting the process data have identified the cause to be due
to an unanticipated change in incoming raw materials. This problem
should be considered:
• Question 13
Process measurements are:
14. • Question 14
Process complexity is important because complex processes:
• Question 15
Service and non-manufacturing processes:
• Question 16
The Hidden Factory is:
• Question 17
The primary goal of process mapping is to:
• Question 18
Service processes are different from manufacturing processes in that:
• Question 19
Viewing a business as a system is important because:
• Question 20
Use of subject matter knowledge is a key aspect of the Statistical
Thinking Strategy discussed in Chapter 2. For which of the following
case studies in Chapter 4 was this principle NOT clearly applied?
• Question 21
Which of the following are valid statements about the DMAIC
framework?
15. • Question 22
We learned in Chapter 3 that the measurement process is always
important and worthy of our attention. However, in Chapter 4 we
found that measurement can be absolutely critical to solving the
problem. For which of the case studies in Chapter 4 was measurement
NOT discussed as a critical issue?
• Question 23
Which of the following are accurate statements about the relationship
between the Process Improvement Framework (PIF) and the Problem
Solving Framework (PSF)?
• Question 24
The frameworks discussed in Chapter 4 could be considered more
specific examples of:
• Question 25
If you are thinking creatively about how to take existing tools and link
and sequence them to develop a novel approach to solve important
problems, this would be an example of:
MAT 510 MIDTERM 2ND Attempt
• Question 1
Understanding variation is important because variation:
• Question 2
A SIPOC map is:
16. • Question 3
Improvement is needed for an organization to survive because:
• Question 4
Why is it so difficult for a person or an organization to improve?
• Question 5
W. E. Deming commented ―You don’t have to make these changes,
survival is not mandatory.‖ His message is:
• Question 6
The fact that processes tend to be dynamic, rather than static, is a key
principle of statistical thinking. Which of the following is a natural
consequence of this fact?
• Question 7
In the advertising case study, the unexpected ―V‖ shaped pattern in
the plot of advertising dollars versus sales indicated what?
• Question 8
Box, Hunter, and Hunter are quoted in this chapter as stating: ―Data
have no meaning in themselves; they are meaningful only in relation
to a conceptual model of the phenomenon studied.‖ This critical point
is related to which of the following principles of statistical thinking?
• Question 9
Analysts have noticed that October tends to be a bad month in the
collections department – our collections on past due accounts tend to
be consistently lower in October. This issue should be considered:
17. • Question 10
It is, unfortunately, common in business for managers to demand an
explanation for variation that is, based on statistics, typical and
expected variation, i.e., common cause variation. Alternatively, there
may be obvious special causes in the data that may be shrugged off as
―typical business fluctuations‖. Which of the following statements
about interpretation of variation are true?
• Question 11
The Statistical Thinking Strategy illustrated in Figure 2.14 provides a
graphic of the overall approach to driving improvement through
statistical thinking. Which of the following is a key principle
illustrated in this specific graph?
• Question 12
In Figure 2.11, Coach Hau presented data showing where goals were
scored on crosses. This produced unexpected results, in terms of
where the goals came from, and helped his team determine an
effective strategy on free kicks. This is an example of what principle
of statistical thinking?
• Question 13
The Hidden Factory is:
• Question 14
The primary goal of process mapping is to:
• Question 15
Viewing a business as a system is important because:
• Question 16
18. The SIPOC model for a business helps everyone in the company see
the business from an overall perspective by:
I. Keeping a focus on customer needs
II. Identifying inputs and outputs for each step of the process
III. Displaying cross-function activities in simple terms
IV. Helping maintain the big business picture
• Question 17
Process complexity is important because complex processes:
• Question 18
Service processes are different from manufacturing processes in that:
• Question 19
Processes are important because:
• Question 20
If you are thinking creatively about how to take existing tools and link
and sequence them to develop a novel approach to solve important
problems, this would be an example of:
• Question 21
Which type of variation was critical to resolving the realized revenue
case study?
• Question 22
Suppose the Process Improvement Framework were used to attack a
problem where the process in question was inherently unstable.
Which of the following would be a likely result of this effort?
19. • Question 23
The frameworks discussed in Chapter 4 could be considered more
specific examples of:
• Question 24
The Problem Solving Framework begins with a ―Document the
Problem‖ phase. The Process Improvement Framework does not
begin this way, but rather begins with an ―Understand the Process
phase.‖ Which of the following are valid reasons for this difference?
• Question 25
Which of the following are valid statements about the DMAIC
framework?
MAT 510 Midterm Exam Set 2
• Question 1
W. E. Deming commented ―You don’t have to make these changes,
survival is not mandatory.‖ His message is:
• Question 2
The principal drivers of the rapid change in the global economy are:
• Question 3
Process average or mean:
• Question 4
Which of the following are principles of Statistical Thinking?
20. • Question 5
A SIPOC map is:
• Question 6
Figure 2.16 shows a chart of the Dow Jones Industrial Average from
1990 through 2011. This plot reveals some obvious change points,
such as the 2008 financial collapse, and some unexplainable short-
term variation. Assuming that these are the only sources of variation
in this plot, what source of variation is NOT visible in the plot?
• Question 7
Figure 2.15 displays a model of the statistics discipline, showing the
relationship between statistical thinking, statistical engineering, and
statistical methods and tools. Which of the following is a principle
illustrated by this model?
• Question 8
In the soccer case study, Coach Hau developed a flowchart of the
steps one goes through to properly ―head‖ the ball, and then
developed unique drills to develop the team’s skills in each of these
steps. This approach is an example of what statistical thinking
principle?
• Question 9
Analysts have noticed that October tends to be a bad month in the
collections department – our collections on past due accounts tend to
be consistently lower in October. This issue should be considered:
• Question 10
What is tool that can be used to detect the structure variation?
21. • Question 11
In Figure 2.11, Coach Hau presented data showing where goals were
scored on crosses. This produced unexpected results, in terms of
where the goals came from, and helped his team determine an
effective strategy on free kicks. This is an example of what principle
of statistical thinking?
• Question 12
The Statistical Thinking Strategy illustrated in Figure 2.14 provides a
graphic of the overall approach to driving improvement through
statistical thinking. Which of the following is a key principle
illustrated in this specific graph?
• Question 13
Viewing a business as a system is important because:
• Question 14
Processes are important because:
• Question 15
The Hidden Factory is:
• Question 16
Refer to Figure 3.21 and choose the correct statement for the
measurements
• Question 17
Service processes are different from manufacturing processes in that:
22. • Question 18
The SIPOC model for a business helps everyone in the company see
the business from an overall perspective by:
I. Keeping a focus on customer needs
II. Identifying inputs and outputs for each step of the process
III. Displaying cross-function activities in simple terms
IV. Helping maintain the big business picture
• Question 19
Sub-optimization occurs when:
• Question 20
Which of the following are valid statements about the DMAIC
framework?
• Question 21
Suppose the Process Improvement Framework were used to attack a
problem where the process in question was inherently unstable.
Which of the following would be a likely result of this effort?
• Question 22
The Problem Solving Framework begins with a ―Document the
Problem‖ phase. The Process Improvement Framework does not
begin this way, but rather begins with an ―Understand the Process
phase.‖ Which of the following are valid reasons for this difference?
• Question 23
Which type of variation was critical to resolving the realized revenue
case study?
• Question 24
23. Which of the following are accurate statements about the relationship
between the Process Improvement Framework (PIF) and the Problem
Solving Framework (PSF)?
• Question 25
If you are thinking creatively about how to take existing tools and link
and sequence them to develop a novel approach to solve important
problems, this would be an example of:
MAT 510 Midterm Exam Set 3
Question 1
The standard deviation is:
Question 2
Understanding variation is important because variation:
Question 3
Process improvement activities typically include:
Question 4
Process average or mean:
Question 5
Which of the following are principles of Statistical Thinking?
Question 6
In Figure 2.11, Coach Hau presented data showing where goals were
scored on crosses. This produced unexpected results, in terms of
where the goals came from, and helped his team determine an
24. effective strategy on free kicks. This is an example of what principle
of statistical thinking?
Question 7
It is, unfortunately, common in business for managers to demand an
explanation for variation that is, based on statistics, typical and
expected variation, i.e., common cause variation. Alternatively, there
may be obvious special causes in the data that may be shrugged off as
―typical business fluctuations‖. Which of the following statements
about interpretation of variation are true?
Question 8
The Statistical Thinking Strategy illustrated in Figure 2.14 provides a
graphic of the overall approach to driving improvement through
statistical thinking. Which of the following is a key principle
illustrated in this specific graph?
Question 9
In the soccer case study, Coach Hau developed a flowchart of the
steps one goes through to properly ―head‖ the ball, and then
developed unique drills to develop the team’s skills in each of these
steps. This approach is an example of what statistical thinking
principle?
Question 10
The Statistical Thinking Strategy has significant commonality with
the scientific method. Which of the following statistical thinking
principles is NOT generally associated with the scientific method?
Question 11
Figure 2.15 displays a model of the statistics discipline, showing the
relationship between statistical thinking, statistical engineering, and
25. statistical methods and tools. Which of the following is a principle
illustrated by this model?
Question 12
A manufacturing process has been experiencing problems. The
operators charting the process data have identified the cause to be due
to an unanticipated change in incoming raw materials. This problem
should be considered:
Question 13
Process complexity is important because complex processes:
Question 14
Service and non-manufacturing processes:
Question 15
The Hidden Factory is:
Question 16
The primary goal of process mapping is to:
Question 17
The SIPOC model for a business helps everyone in the company see
the business from an overall perspective by:
I. Keeping a focus on customer needs
II. Identifying inputs and outputs for each step of the process
III. Displaying cross-function activities in simple terms
IV. Helping maintain the big business picture
Question 18
26. Refer to Figure 3.21 and choose the correct statement for the
measurements
Question 19
Processes are important because:
Question 20
Which type of variation was critical to resolving the realized revenue
case study?
Question 21
Suppose a DMAIC Framework were used to attack a problem where
the process in question was inherently stable. Which of the following
would be a likely result of this effort?
Question 22
We learned in Chapter 3 that the measurement process is always
important and worthy of our attention. However, in Chapter 4 we
found that measurement can be absolutely critical to solving the
problem. For which of the case studies in Chapter 4 was measurement
NOT discussed as a critical issue?
Question 23
Which of the following are accurate statements about the relationship
between the Process Improvement Framework (PIF) and the Problem
Solving Framework (PSF)?
Question 24
The frameworks discussed in Chapter 4 could be considered more
specific examples of:
Question 25
27. If you are thinking creatively about how to take existing tools and link
and sequence them to develop a novel approach to solve important
problems, this would be an example of:
***************************
MAT 510 Week 1 Homework Assignment 1
For more classes visit
www.snaptutorial.com
Homework Assignment 1
Due in Week 1 and worth 30 points
Describe a work task, a hobby, or another activity that you regularly
do, and sequentially list the various actions you take in order to
complete this activity. Consider the complexity of your list and the
amount of steps required to complete the activity.
Answer the following questions in the space provided below:
1. 1. Differentiate the main actions between doing and
improving your activities.
2. 2. Determine the overall manner in which variation has
affected your activities.
***************************
MAT 510 Week 2 Homework Assignment 2
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Homework Assignment 2
Due in Week 2 and worth 30 points
28. Answer the following questions in the space provided below:
Explain the importance of variation to health-care organizations and
answer the following questions.
1. a. What might be the key processes for health-care
organizations?
2. b. key processes of health-care organizations What are the
potential common causes of variation that would have an impact on
the?
3. c. What special causes might be more important than the
others?
4. d. How might health-care organizations’ business
environment be dynamic and change over time?
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MAT 510 Week 3 Homework Assignment 3
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Homework Assignment 3
Due in Week 3 and worth 30 points
The following data consists of the actual time used and potential (the
best time possible for this review process) to complete each step in
the review process. The actual times are based on the review of 30
projects. The potential times are subjective engineering judgment
estimates.
Table: Basic Data Review for Construction Project Equipment
Arrangement
Cycle Time (hours)
Step
Description
Actual
29. Potential
Difference
1
Read basic data package
4
4
—
2
Write, type, proof, sign, copy, and distribute cover letter
21.9
0.5
21.4
3
Queue
40
0
40
4
Lead engineer calls key people to schedule meeting
4
0.25
3.75
5
Write, type, proof, sign, copy, and distribute confirmation letter
25.4
2.1
23.3
6
Hold meeting; develop path forward and concerns
4
4
—
7
Project leader and specialist develop missing information
12
12
—
30. 8
Determine plant preferred vendors
12
12
—
9
Review notes from meeting
12
12
—
10
Resolve open issues
106
104
2
11
Write, type, proof, sign, copy, and distribute basic data acceptance
letter
26.5
0.25
26.25
Totals
267.8
151.1
116.7
Use the data in the table above and answer the following questions in
the space provided below:
1. 1. What are the sources of value-added and non-value-added
work in this process?
2. 2. Where are the main opportunities to improve the cycle time
of this process, with respect to both actual time used and the potential
best times? What strategy would you use?
3. 3. Step 10: Resolve Open Issues required 104 hours
(potential) versus 106 hours (actual). Is there an OFI here? Why or
why not? If so, how would you attack it?
31. 4. 4. What do you think are the most difficult critical issues to
deal with when designing a sound cycle time study such as this one?
Type your answers below and submit this file in Week 3 of the online
course shell:
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MAT 510 Week 4 Case Study 1 Statistical
Thinking in Health Care (2 Papers)
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This Tutorial contains 2 Papers
Case Study 1: Statistical Thinking in Health Care
Due Week 4 and worth 150 points
Read the following case study.
Ben Davis had just completed an intensive course in Statistical
Thinking for Business Improvement, which was offered to all
employees of a large health maintenance organization. There was no
time to celebrate, however, because he was already under a lot of
pressure. Ben works as a pharmacist's assistant in the HMO's
pharmacy, and his manager, Juan de Pacotilla, was about to be fired.
Juan's dismissal appeared to be imminent due to numerous complaints
and even a few lawsuits over inaccurate prescriptions. Juan now was
asking Ben for his assistance in trying to resolve the problem,
preferably yesterday!
"Ben, I really need your help! If I can't show some major
improvement or at least a solid plan by next month, I'm history."
"I'll be glad to help, Juan, but what can I do? I'm just a pharmacist's
assistant."
32. "I don't care what your job title is; I think you're just the person who
can get this done. I realize I've been too far removed from day-to-day
operations in the pharmacy, but you work there every day. You're in a
much better position to find out how to fix the problem. Just tell me
what to do, and I'll do it."
"But what about the statistical consultant you hired to analyze the data
on inaccurate prescriptions?"
"Ben, to be honest, I'm really disappointed with that guy. He has spent
two weeks trying to come up with a new modeling approach to
predict weekly inaccurate prescriptions. I tried to explain to him that I
don't want to predict the mistakes, I want to eliminate them! I don't
think I got through, however, because he said we need a month of
additional data to verify the model, and then he can apply a new
method he just read about in a journal to identify 'change points in the
time series,' whatever that means. But get this, he will only identify
the change points and send me a list; he says it's my job to figure out
what they mean and how to respond. I don't know much about
statistics -- the only thing I remember from my course in college is
that it was the worst course I ever took-- but I'm becoming convinced
that it actually doesn't have much to offer in solving real problems.
You've just gone through this statistical thinking course, though, so
maybe you can see something I can't. To me, statistical thinking
sounds like an oxymoron. I realize it's a long shot, but I was hoping
you could use this as the project you need to officially complete the
course."
"I see your point, Juan. I felt the same way, too. This course was
interesting, though, because it didn't focus on crunching numbers. I
have some ideas about how we can approach making improvements
in prescription accuracy, and I think this would be a great project. We
may not be able to solve it ourselves, however. As you know, there is
a lot of finger-pointing going on; the pharmacists blame sloppy
handwriting and incomplete instructions from doctors for the
problem; doctors blame pharmacy assistants like me who actually do
most of the computer entry of the prescriptions, claiming that we are
incompetent; and the assistants tend to blame the pharmacists for
33. assuming too much about our knowledge of medical terminology,
brand names, known drug interactions, and so on."
"It sounds like there's no hope, Ben!"
"I wouldn't say that at all, Juan. It's just that there may be no quick fix
we can do by ourselves in the pharmacy. Let me explain how I'm
thinking about this and how I would propose attacking the problem
using what I just learned in the statistical thinking course."
Source: G. C. Britz, D. W. Emerling, L. B. Hare, R. W. Hoerl, & J. E.
Shade. "How to Teach Others to Apply Statistical Thinking." Quality
Progress (June 1997): 67--80.
Assuming the role of Ben Davis, write a three to four (3-4) page paper
in which you apply the approach discussed in the textbook to this
problem. You'll have to make some assumptions about the processes
used by the HMO pharmacy. Also, please use the Internet and / or
Strayer LRC to research articles on common problems or errors that
pharmacies face. Your paper should address the following points:
1.Develop a process map about the prescription filling process for
HMO's pharmacy, in which you specify the key problems that the
HMO's pharmacy might be experiencing. Next, use the supplier,
input, process steps, output, and customer (SIPOC) model to analyze
the HMO pharmacy's business process.
2.Analyze the process map and SIPOC model to identify possible
main root causes of the problems. Next, categorize whether the main
root causes of the problem are special causes or common causes.
Provide a rationale for your response.
3.Suggest the main tools that you would use and the data that you
would collect in order to analyze the business process and correct the
problem. Justify your response.
4.Propose one (1) solution to the HMO pharmacy's on-going
problem(s) and propose one (1) strategy to measure the
aforementioned solution. Provide a rationale for your response.
34. 5.Use at least two (2) quality references. Note: Wikipedia and other
Websites do not qualify as academic resources.
Your assignment must follow these formatting requirements:
•Be typed, double spaced, using Times New Roman font (size 12),
with one-inch margins on all sides; citations and references must
follow APA 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.
The specific course learning outcomes associated with this
assignment are:
•Describe how organizations use statistical thinking to be more
competitive.
•Apply the basic principles of statistical thinking to business
processes.
•Apply the SIPOC model to identify OFIs in business processes.
•Use technology and information resources to research issues in
business process improvement.
•Write clearly and concisely about business process improvement
using proper writing mechanics.
***************************
MAT 510 Week 4 Homework Assignment 4
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Homework Assignment 4
Due in Week 4 and worth 30 points
Discuss one (1) project where you used a problem-solving approach
to address what turned out to be common-cause variation, or where
35. you used a process improvement approach to deal with a special
cause.
If you do not have a personal experience that echoes either of these
situations, you may use Internet to search for a case that reflects either
of these situations.
Examples:
· one’s personal investment strategy since 2008
· reducing waiting times at the local hospital or emergency room
· reducing difficulties trying to connect to a Wi-Fi Internet
provider
Answer the following questions in the space provided below:
1. 1. Describe the experience in the project.
2. 2. What were the solutions used to address the problem?
3. 3. Was the case you described a special-cause or common-
cause?
4. 4. Do you feel the solution or approach used appropriate for
the cause?
5. 5. What would you do if you could do it again?
6. 6. What conclusions can you draw from the problem-solving
or process-improvement techniques?
Note: You may create and / or make all necessary assumptions needed
for the completion of this assignment. In your original work, you may
use aspects of existing processes from either your current or a former
place of employment. However, you must remove any and all
identifying information that would enable someone to discern the
organization(s) that you have used.
***************************
MAT 510 Week 6 Homework Assignment 5
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Homework Assignment 5
36. Due in Week 6 and worth 30 points
The data in below table lists country code and the order to remittance
(OTR) time for hardware / software installations for the last 76
installations (from first to last). OTR is the time it takes from an order
being placed until the system is installed and we receive payment
(remittance). Because this company does business internationally, it
also notes the country of installation using a country code. This code
is listed in the first column.
Use the date in table above and answer the following questions in the
space provided below:
1. Does the OTR time appear to be stable? Why or why not?
2. If you were to use a control chart to evaluate stability, which
chart would you use? Why?
3. What can you learn about the distribution of the installation
process?
4. Does it appear that the country has an impact on installation
time? Why or why not?
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MAT 510 Week 6 Homework Assignment 6
For more classes visit
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Due in Week 8 and worth 30 points
The experiment data in below table was to evaluate the effects of
three variables on invoice errors for a company. Invoice errors had
been a major contributor to lengthening the time that customers took
to pay their invoices and increasing the accounts receivables for a
major chemical company. It was conjectured that the errors might be
due to the size of the customer (larger customers have more complex
orders), the customer location (foreign orders are more complicated),
37. and the type of product. A subset of the data is summarized in the
following Table.
Table: Invoice Experiment Error Customer Size Customer
Location Product Type Number of Errors - - - 15 + -
- 18 - + - 6 + + - 2 - - +
19 + - + 23 - + + 16 + + + 21
Customer Size: Small (-), Large (+) Customer Location: Foreign (-),
Domestic (+) Product Type: Commodity (-), Specialty (=)
Reference: Moen, Nolan, and Provost (R. D. Moen, T. W. Nolan and
L. P. Provost. Improving Quality through Planned Experimentation.
New York: McGraw-Hill, 1991)
Use the date in table above and answer the following questions in the
space provided below:
1. 1. What was the average effect of the process change? Did the
process average increase or decrease and by how much?
2. 2. Analyze the data using the regression model y = b0 + b1x,
where y = time to approve and mail a claim (weekly average), x = 0
for the old process, and x = 1 for the new process.
3. 3. How does this model measure the effect of the process
change?
4. 4. How much did the process performance change on the
average? (Hint: Compare the values of b1 and the average of new
process performance minus the average of the performance of the old
process.)
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MAT 510 Week 7 Homework Assignment 6
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Homework Assignment 6
38. Due in Week 7 and worth 30 points
The data in the table below is from a study conducted by an insurance
company to determine the effect of changing the process by which
insurance claims are approved. The goal was to improve policyholder
satisfaction by speeding up the process and eliminating some non-
value-added approval steps in the process. The response measured
was the average time required to approve and mail all claims initiated
in a week. The new procedure was tested for 12 weeks, and the results
were compared to the process performance for the 12 weeks prior to
instituting the change.
Table: Insurance Claim Approval Times (days)
Old Process
New Process
Week
Elapsed Time
Week
Elapsed Time
1
31.7
13
24
2
27
14
25.8
3
33.8
40. 11
38.6
23
24.5
12
39.3
24
23.5
Use the date in table above and answer the following questions in the
space provided below:
1. What was the average effect of the process change? Did the
process average increase or decrease and by how much?
2. Analyze the data using the regression model y = b0 + b1x,
where y = time to approve and mail a claim (weekly average), x = 0
for the old process, and x = 1 for the new process.
3. How does this model measure the effect of the process change?
4. How much did the process performance change on the average?
(Hint: Compare the values of b1 and the average of new process
performance minus the average of the performance of the old
process.)
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MAT 510 Week 8 Case Study 2 Improving E-
Mail Marketing Response (2 Papers)
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This Tutorial contains 2 Papers
Case Study 2: Improving E-Mail Marketing Response
41. Due Week 8 and worth 160 points
Read the following case study.
Students, please view the "Submit a Clickable Rubric Assignment" in
the Student Center.
Instructors, training on how to grade is within the Instructor Center.
A company wishes to improve its e-mail marketing process, as
measured by an increase in the response rate to e-mail advertisements.
The company has decided to study the process by evaluating all
combinations of two (2) options of the three (3) key factors: E-Mail
Heading (Detailed, Generic); Email Open (No, Yes); and E-Mail
Body (Text, HTML). Each of the combinations in the design was
repeated on two (2) different occasions. The factors studied and the
measured response rates are summarized in the following table.
Write a two to three (2-3) page paper in which you:
1. Use the data shown in the table to conduct a design of
experiment (DOE) in order to test cause-and-effect relationships in
business processes for the company.
2. Determine the graphical display tool (e.g., Interaction Effects
Chart, Scatter Chart, etc.) that you would use to present the results of
the DOE that you conducted in Question 1. Provide a rationale for
your response.
3. Recommend the main actions that the company could take in
order to increase the response rate of its e-mail advertising. Provide a
rationale for your response.
4. Propose one (1) overall strategy for developing a process model
for this company that will increase the response rate of its e-mail
advertising and obtain effective business process. Provide a rationale
for your response.
Your assignment must follow these formatting requirements:
· Be typed, double spaced, using Times New Roman font (size
12), with one-inch margins on all sides; citations and references must
follow APA 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.
42. The specific course learning outcomes associated with this
assignment are:
· Build regression models for improving business processes.
· Design experiments to test cause-and-effect relationships in
business processes.
· Use technology and information resources to research issues in
business process improvement.
· Write clearly and concisely about business process
improvement using proper writing mechanics.
***************************
MAT 510 Week 8 Homework Assignment 7
For more classes visit
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Homework Assignment 7
Due in Week 8 and worth 30 points
The experiment data in below table was to evaluate the effects of
three variables on invoice errors for a company. Invoice errors had
been a major contributor to lengthening the time that customers took
to pay their invoices and increasing the accounts receivables for a
major chemical company. It was conjectured that the errors might be
due to the size of the customer (larger customers have more complex
orders), the customer location (foreign orders are more complicated),
and the type of product. A subset of the data is summarized in the
following Table.
Reference: Moen, Nolan, and Provost (R. D. Moen, T. W. Nolan and
L. P. Provost. Improving Quality through Planned Experimentation.
New York: McGraw-Hill, 1991)
Use the date in table above and answer the following questions in the
space provided below:
43. 1. 1. What is the nature of the effects of the factors studied in
this experiment?
2. What strategy would you use to reduce invoice errors, given the
results of this experiment?
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MAT 510 Week 9 Homework Assignment 8
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Homework Assignment 8
Due in Week 9 and worth 30 points
Suppose the number of equipment sales and service contracts that a
store sold during the last six (6) months for treadmills and exercise
bikes was as follows:
Treadmill Exercise Bike
Total sold 185 123
Service contracts 67 55
The store can only sell a service contract on a new piece of
equipment. Of the 185 treadmills sold, 67 included a service contract
and 118 did not.
Complete the following questions in the space provided below:
1. 1. Construct a 95 percent confidence interval for the
difference between the proportions of service contracts sold on
treadmills versus exercise bikes.
2. Is there a major difference between the two pieces of equipment?
Why or why not?
***************************
MAT 510 Week 10 Homework Assignment 9
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44. www.snaptutorial.com
Homework Assignment 9
Due in week 10 and worth 30 points
Suppose that there are two (2) candidates (i.e., Jones and Johns) in the
upcoming presidential election. Sara notes that she has discussed the
presidential election candidates with 15 friends, and 10 said that they
are voting for candidate Jones. Sara is therefore convinced that
candidate Jones will win the election because Jones gets more than
50% of votes.
Answer the following questions in the space provided below:
1. 1. Based on what you now know about statistical inference, is
Sara’s conclusion a logical conclusion? Why or why not?
2. 2. How many friend samples Sara should have in order to
draw the conclusion with 95% confidence interval? Why?
3. 3. How would you explain your conclusion to Sara without
using any statistical jargon? Why?
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