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University of the Cumberlands Inferential Statistics in Decision Making Problem
Janice is a production manager for a company that designs and produces hydraulic valves that are used in aircraft systems. ...
University of the Cumberlands Inferential Statistics in Decision Making Problem
Janice is a production manager for a company that designs and produces hydraulic valves that are used in aircraft systems. The company is concerned that the number of valves not meeting the strict measurement parameters has incereased over the past several months. She implements a quality control program that includes random inspections. Employees are notified that these inspections will help the company to reduce expenses from poor product quality. Janice runs the program for 9 months and performs a different number of inspection each month. The table below shows the number of inspections and the number of faulty hydraulic valves produced each month.
InspectionsNumber of faulty valves6541196477985313766
Run the analysis and answer the following questions:
1. Does the number of inspections result in fewer faulty valves? (Run the analysis and report the results)
2. What decision should Janice make regarding the quality control inspections?
2 pages
Hw Week 9 Geometry
6. Two rectangles are similar. The first is 4 in. wide and 15 in. long. The second is 9 in. wide. Find 7. Two rectangles ...
Hw Week 9 Geometry
6. Two rectangles are similar. The first is 4 in. wide and 15 in. long. The second is 9 in. wide. Find 7. Two rectangles are similar. One is 5 cm by ...
Walden University Confidence Intervals and Afrobarometer Dataset Discussion Paper
In your Week 2 Assignment, you displayed data based on a categorical variable and continuous variable from a specific data ...
Walden University Confidence Intervals and Afrobarometer Dataset Discussion Paper
In your Week 2 Assignment, you displayed data based on a categorical variable and continuous variable from a specific dataset. In Week 3, you used the same variables as in Week 2 to perform a descriptive analysis of the data. For this Assignment, you will calculate a confidence interval in SPSS for one of the variables from your Week 2 and Week 3 Assignments.
To prepare for this Assignment:
Review the Learning Resources related to probability, sampling distributions, and confidence intervals.
For additional support, review the Skill Builder: Confidence Intervals and the Skill Builder: Sampling Distributions, which you can find by navigating back to your Blackboard Course Home Page. From there, locate the Skill Builder link in the left navigation pane.
Using the SPSS software, open the Afrobarometer dataset or the High School Longitudinal Study dataset (whichever you chose) from Week 2.
Choose an appropriate variable from Weeks 2 and 3 and calculate a confidence interval in SPSS.
Once you perform your confidence interval, review Chapter 5 and 11 of the Wagner text to understand how to copy and paste your output into your Word document.
For this Assignment:
Write a 2- to 3-paragraph analysis of your results and include a copy and paste of the appropriate visual display of the data into your document. If you are using the Afrobarometer Dataset, report the mean of Q1 (Age). If you are using the HS Long Survey Dataset, report the mean of X1SES.
Based on the results of your data in this confidence interval Assignment, provide a brief explanation of what the implications for social change might be.
Learning Resources
Required Readings
Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Thousand Oaks, CA: Sage Publications.
Chapter 5, “The Normal Distribution” (pp. 151-177)
Chapter 6, “Sampling and Sampling Distributions” (pp. 179-209)
Chapter 7, “Estimation” (pp. 211-240)
Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.
Chapter 3, “Selecting and Sampling Cases”
Chapter 5, “Charts and Graphs”
Chapter 11, “Editing Output”
Magnusson, K. (n.d.). Welcome to Kristoffer Magnusson’s blog about R, Statistics, Psychology, Open Science, Data Visualization [blog]. Retrieved from http://rpsychologist.com/index.html
As you review this web blog, select the Interpreting Confidence Intervals – new d3.js visualization link, once you select the link, follow the instructions to view the interactive for confidence intervals. This interactive will help you to visualize and understand confidence intervals.
Note: This is Kristoffer Magnusson’s personal blog and his views may not necessarily reflect the views of Walden University faculty.
Walden University Library. (n.d.). Course Guide and Assignment Help for RSCH 8210. Retrieved from http://academicguides.waldenu.edu/rsch8210
For help with this week’s research, see this Course Guide and related weekly assignment resources.
Optional Resources
Rice University, University of Houston Clear Lake, and Tufts University. (n.d.). Online Statistics Education: An Interactive Multimedia Course of Study. Retrieved from http://onlinestatbook.com/2/estimation/ci_sim.html
Use this website for your practice as you consider confidence intervals and how the width changes. Also, consider why the width might be important.
Skill Builders:
Confidence Intervals
Sampling Distributions
To access these Skill Builders, navigate back to your Blackboard Course Home page, and locate “Skill Builders” in the left navigation pane. From there, click on the relevant Skill Builder link for this week.
STATS 200 AU Covid 19 Hospitalization Data Across US States Discussion
covid-19 hospitalization summary report Status: RequiredDue Date: October 22, 2020Project GoalsIn the last two weeks, we d ...
STATS 200 AU Covid 19 Hospitalization Data Across US States Discussion
covid-19 hospitalization summary report Status: RequiredDue Date: October 22, 2020Project GoalsIn the last two weeks, we discussed the importance of summarizing data using descriptive statistics. Based on which attributes of data were considered, different descriptive statistics were implemented to the data using Excel’s Data Analysis Tool Pack to get insights into the issue of interest to help the decision-making process.The main goal of this project consists of using Excel or SPSS software and COVID-19 Hospitalization data to calculate descriptive statistics, compare them across US states, and draft a summary report on the data attributes including central location (i.e. arithmetic mean, median, and mode), dispersion (i.e. range, mean absolute deviation, variance, and standard deviation), relative position (i.e. Five summary measures and Boxplot), and association between variables (Covariance and Correlation Coefficient).Students are expected to compare attributes of COVID-19 data using two states and write their final report which must describe the population of interest to the analysis, the data collection procedure, the implementation of the statistical procedure to calculate or estimate the population parameters, the interpretation of the results, and the policy recommendations.
The Logic of Inference: The Science of Uncertainty
Describing and explaining social phenomena is a complex task. Box’s quote "All models are wrong. Some models are useful" ...
The Logic of Inference: The Science of Uncertainty
Describing and explaining social phenomena is a complex task. Box’s quote "All models are wrong. Some models are useful" speaks to the point that it is a near impossible undertaking to fully explain such systems—physical or social—using a set of models. Yet even though these models contain some error, the models nevertheless assist with illuminating how the world works and advancing social change.The competent quantitative researcher understands the balance between making statements related to theoretical understanding of relationships and recognizing that our social systems are of such complexity that we will always have some error. The key, for the rigorous researcher, is recognizing and mitigating the error as much as possible.As a consumer of research, you must recognize the error that might be present within your research and the research of others.To prepare for this Discussion select one of the quantitative articles with social change implications attached. As you read the article, reflect on George Box’s quote.Post a brief description (2–3 sentences) of the article you found.Describe how you think the research in the article is useful (e.g., what population is it helping? What problem is it solving?).Using Y=f(X) +E notation, identify the independent and dependent variables.How might the research models presented be wrong? What types of error might be present in the reported research?
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Most Popular Content
University of the Cumberlands Inferential Statistics in Decision Making Problem
Janice is a production manager for a company that designs and produces hydraulic valves that are used in aircraft systems. ...
University of the Cumberlands Inferential Statistics in Decision Making Problem
Janice is a production manager for a company that designs and produces hydraulic valves that are used in aircraft systems. The company is concerned that the number of valves not meeting the strict measurement parameters has incereased over the past several months. She implements a quality control program that includes random inspections. Employees are notified that these inspections will help the company to reduce expenses from poor product quality. Janice runs the program for 9 months and performs a different number of inspection each month. The table below shows the number of inspections and the number of faulty hydraulic valves produced each month.
InspectionsNumber of faulty valves6541196477985313766
Run the analysis and answer the following questions:
1. Does the number of inspections result in fewer faulty valves? (Run the analysis and report the results)
2. What decision should Janice make regarding the quality control inspections?
2 pages
Hw Week 9 Geometry
6. Two rectangles are similar. The first is 4 in. wide and 15 in. long. The second is 9 in. wide. Find 7. Two rectangles ...
Hw Week 9 Geometry
6. Two rectangles are similar. The first is 4 in. wide and 15 in. long. The second is 9 in. wide. Find 7. Two rectangles are similar. One is 5 cm by ...
Walden University Confidence Intervals and Afrobarometer Dataset Discussion Paper
In your Week 2 Assignment, you displayed data based on a categorical variable and continuous variable from a specific data ...
Walden University Confidence Intervals and Afrobarometer Dataset Discussion Paper
In your Week 2 Assignment, you displayed data based on a categorical variable and continuous variable from a specific dataset. In Week 3, you used the same variables as in Week 2 to perform a descriptive analysis of the data. For this Assignment, you will calculate a confidence interval in SPSS for one of the variables from your Week 2 and Week 3 Assignments.
To prepare for this Assignment:
Review the Learning Resources related to probability, sampling distributions, and confidence intervals.
For additional support, review the Skill Builder: Confidence Intervals and the Skill Builder: Sampling Distributions, which you can find by navigating back to your Blackboard Course Home Page. From there, locate the Skill Builder link in the left navigation pane.
Using the SPSS software, open the Afrobarometer dataset or the High School Longitudinal Study dataset (whichever you chose) from Week 2.
Choose an appropriate variable from Weeks 2 and 3 and calculate a confidence interval in SPSS.
Once you perform your confidence interval, review Chapter 5 and 11 of the Wagner text to understand how to copy and paste your output into your Word document.
For this Assignment:
Write a 2- to 3-paragraph analysis of your results and include a copy and paste of the appropriate visual display of the data into your document. If you are using the Afrobarometer Dataset, report the mean of Q1 (Age). If you are using the HS Long Survey Dataset, report the mean of X1SES.
Based on the results of your data in this confidence interval Assignment, provide a brief explanation of what the implications for social change might be.
Learning Resources
Required Readings
Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Thousand Oaks, CA: Sage Publications.
Chapter 5, “The Normal Distribution” (pp. 151-177)
Chapter 6, “Sampling and Sampling Distributions” (pp. 179-209)
Chapter 7, “Estimation” (pp. 211-240)
Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.
Chapter 3, “Selecting and Sampling Cases”
Chapter 5, “Charts and Graphs”
Chapter 11, “Editing Output”
Magnusson, K. (n.d.). Welcome to Kristoffer Magnusson’s blog about R, Statistics, Psychology, Open Science, Data Visualization [blog]. Retrieved from http://rpsychologist.com/index.html
As you review this web blog, select the Interpreting Confidence Intervals – new d3.js visualization link, once you select the link, follow the instructions to view the interactive for confidence intervals. This interactive will help you to visualize and understand confidence intervals.
Note: This is Kristoffer Magnusson’s personal blog and his views may not necessarily reflect the views of Walden University faculty.
Walden University Library. (n.d.). Course Guide and Assignment Help for RSCH 8210. Retrieved from http://academicguides.waldenu.edu/rsch8210
For help with this week’s research, see this Course Guide and related weekly assignment resources.
Optional Resources
Rice University, University of Houston Clear Lake, and Tufts University. (n.d.). Online Statistics Education: An Interactive Multimedia Course of Study. Retrieved from http://onlinestatbook.com/2/estimation/ci_sim.html
Use this website for your practice as you consider confidence intervals and how the width changes. Also, consider why the width might be important.
Skill Builders:
Confidence Intervals
Sampling Distributions
To access these Skill Builders, navigate back to your Blackboard Course Home page, and locate “Skill Builders” in the left navigation pane. From there, click on the relevant Skill Builder link for this week.
STATS 200 AU Covid 19 Hospitalization Data Across US States Discussion
covid-19 hospitalization summary report Status: RequiredDue Date: October 22, 2020Project GoalsIn the last two weeks, we d ...
STATS 200 AU Covid 19 Hospitalization Data Across US States Discussion
covid-19 hospitalization summary report Status: RequiredDue Date: October 22, 2020Project GoalsIn the last two weeks, we discussed the importance of summarizing data using descriptive statistics. Based on which attributes of data were considered, different descriptive statistics were implemented to the data using Excel’s Data Analysis Tool Pack to get insights into the issue of interest to help the decision-making process.The main goal of this project consists of using Excel or SPSS software and COVID-19 Hospitalization data to calculate descriptive statistics, compare them across US states, and draft a summary report on the data attributes including central location (i.e. arithmetic mean, median, and mode), dispersion (i.e. range, mean absolute deviation, variance, and standard deviation), relative position (i.e. Five summary measures and Boxplot), and association between variables (Covariance and Correlation Coefficient).Students are expected to compare attributes of COVID-19 data using two states and write their final report which must describe the population of interest to the analysis, the data collection procedure, the implementation of the statistical procedure to calculate or estimate the population parameters, the interpretation of the results, and the policy recommendations.
The Logic of Inference: The Science of Uncertainty
Describing and explaining social phenomena is a complex task. Box’s quote "All models are wrong. Some models are useful" ...
The Logic of Inference: The Science of Uncertainty
Describing and explaining social phenomena is a complex task. Box’s quote "All models are wrong. Some models are useful" speaks to the point that it is a near impossible undertaking to fully explain such systems—physical or social—using a set of models. Yet even though these models contain some error, the models nevertheless assist with illuminating how the world works and advancing social change.The competent quantitative researcher understands the balance between making statements related to theoretical understanding of relationships and recognizing that our social systems are of such complexity that we will always have some error. The key, for the rigorous researcher, is recognizing and mitigating the error as much as possible.As a consumer of research, you must recognize the error that might be present within your research and the research of others.To prepare for this Discussion select one of the quantitative articles with social change implications attached. As you read the article, reflect on George Box’s quote.Post a brief description (2–3 sentences) of the article you found.Describe how you think the research in the article is useful (e.g., what population is it helping? What problem is it solving?).Using Y=f(X) +E notation, identify the independent and dependent variables.How might the research models presented be wrong? What types of error might be present in the reported research?
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