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Topic 1: Results from surveys or opinion polls often report a range of values—th

Topic 1:
Results from surveys or opinion polls often report a range of values—th

Topic 1:
Results from surveys or opinion polls often report a range of values—the sample statistic plus or minus a margin of error (the resulting range is called a confidence interval). This tells us that the range is likely to contain the population parameter. How much wiggle room we provide is based on how much confidence we wish to have that the range contains the actual population mean. That confidence level is directly related to the middle “truth” area we will accept versus the dubious tail area we will reject—also known as alpha (α). The more confidence we wish to have—the more middle ground we will need to accept (more wiggle room)—thus a smaller tail area. If we insist on a larger alpha (more dubious tail area), we narrow the middle ground we will accept and thus provide less wiggle room—so the more likely it is that we will miss the true average (and, thus, we have a lower confidence level). A 95% confidence level leaves 5% alpha. A 99% confidence level leaves 1% alpha.
Now, without calculating a mean or margin of error or a confidence level, provide an example from your personal life that describes a measurement that is normal—and how much wiggle room on either side would be appropriate. When would you want a 95% confidence interval, and when would you want a 99% confidence level (a little more wiggle room—so a wider range)?

This work is related to the research matrix you did you me. It is the narrative

This work is related to the research matrix you did you me. It is the narrative of what you had already written in research matrix. For reference, I’m attaching the matrix with this question.You already completed the research matrix so ignore it where it is written to submit it. Just need a narrative now.
As you complete the work on your matrix, observe how the approaches are similar and different across and down the cells. You will need to draw on those observations as you develop your narrative this week. As you prepare to develop your narrative, it is also important to organize your references so that you can refer back to the original sources. For this Assignment, you will create a brief narrative summarizing what you have learned, citing your sources. This Assignment will be a helpful guide as you read other qualitative studies for your capstone.
To Prepare for this Assignment:
Review the work that you have done in the last two weeks, making sure that each cell in the matrix is completed. Create your reference list, including books and methodological articles on each area. You can use the ones listed in the Learning Resources and search for your own as well.
BY DAY 5
Write a 2- to 3-page narrative. In your narrative, be sure to respond to the following:
Summarize what you have learned about the similarities and differences among the approaches.
Describe how what you’ve learned by developing the matrix has allowed you to choose the approach that you plan to use for your research plan in this course.
Identify the approach you intend to use for your research question.
Describe your rationale for your choice of approach.
Submit your Qualitative Research Approaches Matrix Template (which you worked on during Weeks 2 and 3) and your narrative to your Instructor.

One goal of statistics is to identify relations among variables. What happens to

One goal of statistics is to identify relations among variables. What happens to one variable as another variable changes? Does a change in one variable cause a change in another variable? These questions can lead to powerful methods of predicting future values through linear regression.

It is important to note the true meaning and scope of correlation, which is the nature of the relation between two variables. Correlation does not allow us to say that there is any causal link between the two variables. In other words, we cannot say that one variable causes another; however, it is not uncommon to see such use in the news media. An example is shown below. Here we see that, at least visually, there appears to be a relation between the divorce rate in Maine and the per capita consumption of margarine. Does this data imply that all married couples in Maine should immediately stop using margarine to stave off divorce? Common sense tells us that is probably not true.

This is an example of a spurious correlation in which there appears to be a relation between the divorce rate and margarine consumption, but it is not a causal link.

The appearance of such a relation could merely be due to coincidence or perhaps another unseen factor.
What is one instance where you have seen correlation misinterpreted as causation? Please describe.

Dr. Rose was interested in studying student involvement in extracurricular activ

Dr. Rose was interested in studying student involvement in extracurricular activities, residence, school motivation scores, life satisfaction scores, and their scores on exams 1, 2, and 3. He found that students were either (0) not involved with extra-curricular activities on campus or (1) involved with extracurricular activities and either lived (1) on campus, (2) off-campus, or (3) with their parents. Using these data Lab 1.omvDownload Lab 1.omv, please answer the questions below and provide your Jamovi file with analyses completed. Be sure to open the Jamovi software FIRST and then open the file within the Jamovi software. Here is a Step-by-Step Guide Actionsto assist you in completing this lab in Jamovi. Note the different variable icons used in Jamovi to represent different variable levels. These are important since a mean or average, for example, cannot be computed with Nominal Data. LAB DETAILSYour submission for this Lab Assignment should be a Jamovi file with an omv extension. NOTE that you will not be able to Preview your submission since you are only able to see the file when in the Jamovi application. Create a variable called Average Exam Score, the average of exam scores 1, 2, and 3. Use the compute mean command to do this. Transform the Motivation Variable into Motivation Groups with three groups: Low (1-3), Medium (4-6), and High (7-9). Use the layered IF command to do this. Create a frequency table for the residence variable. Use the descriptive statistics function to find the mean and standard deviation of the life satisfaction variable split by two variables; extra-curricular involvement and Motivation_Groups. Report each mean and standard deviation and provide output. Find the mean and standard deviation for exam 1, exam 2, exam 3, and average exam scores variables. Compute histograms with a normal curve (density) for average exam scores and life satisfaction scores. Create a histogram without a normal curve (density) for exams 1, 2, and 3. Split the data by the motivation groups variable and find the mean exam score and life satisfaction score for each group as well as the histogram and density for each group. IMPORTANT This lab will utilize Jamovi, to review the resources linked in your Course Tools and Resources to get started with Jamovi. Please reach out to your instructor with any questions or concerns.
You may find it best to download the software for Jamovi directly to your device for free if you wish. This can be done through the Jamovi webpage.

example answer: Class, I used Data set 17 Mean: 4.77 Standard Deviation=$1.47 E

example answer:
Class,
I used Data set 17
Mean: 4.77
Standard Deviation=$1.47
E

example answer:
Class,
I used Data set 17
Mean: 4.77
Standard Deviation=$1.47
E Value= $0.53
95% Confidence Interval= $4.24- $5.30
Confidence Intervals For this forum we will discuss confidence intervals and what they mean. Initial Post Instructions:
Choose a data set of fuel prices from the attached Excel file. Find the mean and standard deviation for the data set. Please do not try to do this by hand, but use the functions in Excel. Find the 95% Confidence Interval for your data. Response Post Instructions:
Respond to two of your classmates by analyzing what the confidence interval means in this scenario and how this data can be used. And then compare how the results vary between the different data sets. Sample initial post: First find the mean and standard deviation. (Refer to video in week 2 discussion.)
Then type this in an excel cell =confidence.norm(0.05,stdev,samplesize)and hit enter. This is the E value. (Note: please use your standard deviation and sample size and not the words.)
Set up your confidence interval: (mean-E, mean+E) It is important to go back and read follow up responses from your instructor.
Formula you used above for the confidence interval:
The formula is E= zsubc * sigma / sqrt n
Where the left hand endpoint is xbar – E and the right hand endpoint is xbar + E
To earn full credit for this discussion, you must respond to at least two (2) of your peers and post your initial post by Thursday. Rubric for grading discussions: Quick View of Discussion Grading Rubric (Please see full details for explanation of criteria below)

example answer: Class, I used Data set 17 Mean: 4.77 Standard Deviation=$1.47 E

example answer:
Class,
I used Data set 17
Mean: 4.77
Standard Deviation=$1.47
E

Dr. Rose was interested in studying student involvement in extracurricular activ

example answer: Class, I used Data set 17 Mean: 4.77 Standard Deviation=$1.47 E

example answer:
Class,
I used Data set 17
Mean: 4.77
Standard Deviation=$1.47
E

Dr. Rose was interested in studying student involvement in extracurricular activ

The subject area is: Data Analytics Background to the task: IBM is a large sof

The subject area is: Data Analytics
Background to the task:
IBM is a large sof

The subject area is: Data Analytics
Background to the task:
IBM is a large software and hardware vendor, with a focus on AI and Quantum. Part of IBM’s strategy is to support the skilling up to 30 million people by 2030, in order to cater to the changing needs of the information technology industry. One of our main vehicles for this is IBM Skills Build, a platform where students and adult learners can have free access to online courses/badges.
IBM Skills Build is IBM’s main online platform for delivering learning to both students and adult learners who are looking to transition to another role. IBM Skills Build is the avenue to achieve IBM’s goal of skilling up 30 million people by 2030. In order to achieve this goal, IBM would need to ensure that the learning material contained in IBM Skills Build meets the needs of the information technology industry, and will help learners cross the skills gap, thereby filling the skills needs of the industry, both today and in the future (next 3 years).
In order to ensure that Skills Build continues to be the relevant to the user base, you are required to review the Skills Build content, and perform an analysis of the data provided by the UK Government Employment and Skills survey, and provide recommendations on the evolution of the IBM Skills Build, especially to determine which content should be sunset, which content is relevant, and what content IBM should consider including.
Some links to help you get started:
https://www.ibm.com/uk-en
https://skillsbuild.org/about
https://digital-skills-jobs.europa.eu/en/inspiration/resources/ibm-skillsbuild-empowering-digital-skills-all
IBM Skills Build overview:
https://bit.ly/PathWaytoLearning
UK Government Data Skills Survey (2022):
https://explore-education-statistics.service.gov.uk/find-statistics/employer-skills-survey/2022
Further research (as required) is to be carried out by the student.
Requirements:
1. Familiarize with the IBM Skills Build program, how the program has evolved and what skills needs are currently addressed.
Carry out research on skills programs and their contribution to the information technology industry, business community and society at large.
2. Familiarize with IBM as an organization, and their business strategy to support the skills requirements of the fast-changing information technology industry.
3. Familiarize with the UK Government Data Skills Survey of 2022, understand the recent study skills gaps in the UK and how the skills needs have evolved.
Carry out research on any other digital skills requirements that may address future needs.
4. Analyze the data contained in the UK Government Skills Employment Survey of 2022.
Select a dataset that will support your analysis, cleanse the data as required.
5. Analyze the skills needs currently addressed by the IBM Skills Build program.
Prepare a quality dataset that will support your analysis, by using the sources given.
6. Import the data into a data analytics platform of your choice. It is recommended to use SAS Enterprise Miner, which was introduced as part of the university short course.
7. Transform the raw data by carrying out your own analysis using the software tools and produce compelling, meaningful, and relevant visualizations. Discuss your analysis in the report.
8. Frame the data set based on the requirement, apply linear regression model and interpret the results using the features of the software tool, to predict the trend of future increased skills needs. Discuss your findings in report.
The analysis must include (but not limited to):
– Projected skills shortfalls
– Sector-wise analysis
9. Reflect on the project
– what was your learning?
– challenges faced?
– what would you do differently?
10. Explain the work carried out (as separate appendices):
a. What data was collected for the analysis and what sources were used.
b. How the data was cleansed.
c. What model was selected for the predictive analysis and why.
11. Evidence of the work carried out (in report and/or as separate appendices):
a. Cleansed data sets
b. Data visualizations
c. Predictions
Requirement #5 dataset is doing an excel sheet.
6 & 7 needs someone who knows SAS miner
All of the above requirements doesn’t require much words
Except for the part 1,2,3,4, 9 and 10
Please show proof of answers (screen shot from the SAS and other softwares)
No need for an introduction or conclusion
Just answer the questions