When responding to your classmates, offer additional examples of appropriate uses for incidence and prevalence and explain how the two measures are related. 

Please respond to 2 classmates discussion

Both classmates discussion is below

Please speak in first person to each person.

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Regression analysis can estimate the magnitude of the impact of a change in one variable or another (Holmes et al., 2017). If regression analysis were to be completed on BMI, an independent variable associated with that would be height and weight. The size and weight are how the BMI is calculated. Usually, as we get older, the amount of body fat increases. This could be because we typically become less active as we age. Additionally, females usually have a higher percentage of body fat than males. Other independent variables included in the analysis would be sex, age, diet, body type, and disease processes. A statistic that will show the value of BMI regression would be physical activity. Typically, the more physical activity a person gets, the lower their BMI.

The article that I found interesting that uses regression analysis to study a medical concern discussed the relationship between changes in dietary cholesterol intake and alterations in lipoprotein-cholesterol markers for cardiovascular disease risk and provided a reference for clinicians on how changes in dietary cholesterol intake affect circulating cholesterol concentrations, after accounting for intakes of fatty acids (Vincent et al. 2019). The independent variables discussed in the article were trans fatty acid intake, saturated fatty acid intake, and cholesterol intake. The dependent variable is LDL-cholesterol concentration. I would use this study to highlight the difference between correlations and causation by accepting the conclusion that dietary cholesterol change was positively associated with the change in LDL-cholesterol concentration (Vincent et el. 2019).  

References

Holmes, A., Illowsky, B., & Dean, S. (2017). Introductory business statistics. OpenStax.

Vincent, M. J., Allen, B., Palacios, O. M., Haber, L. T., & Maki, K. C. (2019). Metaregression analysis of the             effects of dietary cholesterol intake on LDL and HDL cholesterol. The American journal of clinical                 nutrition, 109(1), 716

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Treat your data just as you would one of the datasets from the homework. Be sure you include appropriate measures of central tendency and dispersion etc.

 Construct a frequency distribution using 5 8 classes.

 Create 2 different but appropriate visual representations of your data (pie chart, bar graph, etc). You MUST use   Excel to do this.

 Complete the calculations for the   8 statistics you identified in your worksheet in week 3. You MUST use Excel     

Treat your data just as you would one of the datasets from the homework. Be sure you include appropriate measures of central tendency and dispersion etc.

 Construct a frequency distribution using 5 8 classes.

 Create 2 different but appropriate visual representations of your data (pie chart, bar graph, etc). You MUST use   Excel to do this.

 Complete the calculations for the   8 statistics you identified in your worksheet in week 3. You MUST use Excel to do this.

 Write a brief paragraph describing the meaning or interpretation for EACH of the statistics. For example, if some of the statistics chosen were the mean, median and mode, which is the best measure?

 Construct a 95% Confidence   Interval to estimate the population mean/proportion in the claim.

 Complete the calculations for the   8 statistics you identified in your What can you conclude from this result   regarding the topic?   to do this.

Write a brief paragraph describing the meaning or interpretation for EACH of the statistics. For example, if some of the statistics chosen were the mean, median and mode, which is the best measure?

 Construct a 95% Confidence   Interval to estimate the population mean/proportion in the claim. Complete the calculations for the   8 statistics you identified in your What can you conclude from this result   regarding the topic?

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 Scenario:

A citys administration isnt driven by the goal of maximizing revenues or profits but instead looks at improving the quality of life of its residents. Many American cities are confronted with high traffic and congestion. Finding parking spaces, whether in the street or a parking lot, can be time consuming and contribute to congestion. Some cities have rolled out data-driven parking space management to reduce congestion and make traffic more fluid. 

Youre a data analyst working for a mid-size city that has anticipated significant increments in population and car traffic. The city is evaluating whether it makes sense to invest in infrastructure to count and report the number of parking spaces available at the different parking lots downtown. This data would be collected and processed in real-time, feeding an app that motorists can access to find parking space availability in different parking lots throughout the city. 

Part 1 Data Manipulation:

Download the Parking Lot Use data set. The data set has the following columns:

  • LotCode: a unique code that identifies the parking lot
  • LotCapacity: a number with the respective parking lot capacity
  • LotOccupancy: a number with the current number of cars in the      parking lot
  • TimeStamp: a day/time combination indicating the moment when      occupancy was measured
  • Day: the day of the week corresponding to the TimeStamp

Insert a new column, OccupancyRate, recording occupancy rate as a percentage with 1 decimal. For instance, if the current LotOccupancy is 61 and LotCapacity is 577, then the OccupancyRate would be reported as 10.6 (or 10.6%).

Using the OccupancyRate and Day columns, construct box plots for each day of the week. You can use Insert > Insert Statistic Chart >Box and Whisker for this purpose. Is the median occupancy rate approximately the same throughout the week? If not, which days have lower median occupancy rates? Which days have higher median occupancy rates? Is this what you expected?

Using the OccupancyRate and LotCode columns, construct box plots for each parking lot. You can use Insert > Insert Statistic Chart >Box and Whisker for this purpose. Do all parking lots experience approximately equal occupancy rates? Are some parking lots more frequented than others? Is this what you expected?

Select any 2 parking lots. For each of them, prepare a scatter plot showing occupancy rate against TimeStamp for the week 11/20/2016 11/26/2016. Are occupancy rates time dependent? If so, which times seem to experience the highest occupancy rates? Is this what you expected?

Part 2: Presentation.

Prepare a 12- to 16-slide presentation with detailed speaker notes and audio, graphs, and tables.

Your audience is the city council, which is responsible for deciding whether the city invests resources to set in motion the smart parking space app.

Complete the following in your presentation:

  • Outline the rationale and goals      of the project
  • Utilize box plots showing the      occupancy rates for each day of the week. Include your interpretation of      results.
  • Utilize box plots showing the      occupancy rates for each parking lot. Include your interpretation of      results
  • Provide scatter plots showing      occupancy rate against the time of day of your selected 2 parking lots.      Include your interpretation of results
  • Make a recommendation about      continuing with the implementation of this project.

Include speaker notes that convey the details you would give if you were presenting. You will record your speaker notes.

Ensure that the slides contain only essential information and as little text as possible.

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A researcher wanted to know if there was a difference between male doctoral studentswho did there undergraduate work ata specificuniversity and wether or not these students were accepted into the same univs doctoral program. The research Q is – is there a difference between male and female doctoral students in regard to wether they are accepted or not into the doctoral program of the same univ where they did there undergraduate work?The researcher ran a chi square test and the results are listed as below,

Observed values

   Male         Female       Sum

Accepted          110            115          225

Not accepted    80               95         175

                           190             210           400

X2 (square) – 0.39782

P value 0.528217

What can researcher determinefrom the data analysis?

Summarize the statistical scenario and provide an analysis and critique of the scenario

Your response shud be in detail and paragraph format

Report whether the results are significant . discuss the statistical significance and practical significance in ur analysis and interpretation of the results. What are the implications for leaders in the organization based on these results

What shud be the researchers next steps in research, and what other factors can the researcher consider?

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  • With pain being the 6th vital sign, working in intake it is important to ask about our patients current pain. For 12 intake inmates reporting pain on a numerical scale of 1- 10, the mean pain rating is greater than 5/10. The variable is the pain rating for the incoming patients. I predicted that the mean would be above 6/10 and that was incorrect. Per week 7 lesson (2021), Hypothesis testing is a process that uses sample statistics to test a claim about the value of a population parameter. First stated the claim above. Next, I will determine if this is a Null or alternative hypothesis, The null and alternative hypotheses are two competing claims that researchers weigh evidence for and against using a statistical test (Turney, 2022). In this case, the claim is an alternative hypothesis because the use of or equal to is not included. The type of error identified is a type 1 error, and according to Holmes (2017), = probability of a Type I error = P(Type I error) = probability of rejecting the null hypothesis when the null hypothesis is true: rejecting a good null. When basing the error type on the alternative hypothesis we find that it is rejecting the null hypothesis because the average pain score is less than or equal to 5/10 when the average pain score is actually greater than 5/10.

RESOURCE

Chamberlain College of Nursing (2021). Math 225N Statistical Reasoning for Health Sciences. Week 7 Lesson. Downers Grove, IL. Online Publication.

Holmes, A., Illowsky, B., & Dean, S. (2017). Introductory business statistics. Openstax.

Turney, S. (2022, May 13). Null and Alternative Hypotheses | Definitions & Examples. Scribbr.

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The Analysis of Variance (ANOVA) method is the most popular and highly used statistical technique by researchers. Like T tests, ANOVAs are used to compare the means of groups for a given independent variable to determine whether they are statistically different from each other. However, T tests can only compare two groups. ANOVAs allow you to compare two or more (usually, ANOVAs are conducted with at least three groups). Using ANOVA, you can compare all mean differences simultaneously. This is advantageous as using multiple t tests increases the rate of Type I error, which means rejecting the null hypothesis when it is true (Tarlow, 2016).

For example, say you are interested in whether socioeconomic status is related to individuals self-reported happiness. By separating the individuals into groups representing low, middle, and high socioeconomic status (or even low, low-mid, mid, mid-high and high, or some other combination of groups), you could then determine whether mean reported happiness is different across each of the groups. How many groups might you need to investigate in your own research? If the answer is more than two, then this will be an important week. This week, you will extend your knowledge by identifying and applying the use of a one-way ANOVA test.

References/ Materials

Tarlow, K. R. (2016). Teaching principles of inference with ANOVA. Teaching Statistics, 38(1), 1621.

  • American Psychological Association. (2020). Academic Writer. American Psychological Association.
  • Knapp, H. (Academic). (2016). ANOVA [Streaming video]. SAGE Research Methods.

Statistical reasoning for everyday life

Bennett, J. O., Briggs, W. L., & Triola, M. F. (2017). Statistical reasoning for
everyday life (5th ed.). Boston, MA: Pearson-Addison Wesley.

This is the required Redshelf textbook for the course. You will be using this textbook all through the course for weekly assignments. For this week, review Chapters 9 and 10. Access the textbook in the Getting started module of your course or the Bookshelf link at the top of your course. 

Instructions

Download the assignment template and data files from this weeks resources and review the steps for this assignment. Once you have reviewed the steps, complete the problems and questions as presented. Show your work using your statistical program output. You may show manual/hand calculations only if the SPSS program cannot be accessed. There are two deliverables you must submit this week:

  • SPSS output file or manual calculations: Submit the SPSS output file in PDF form, or you can scan your work and submit it as a low-resolution graphic.
  • Word document: Submit a Word file answering the questions asked in the assignment. Describe the results in APA style incorporating relevant tables and figures formatted in APA style. 

Length: SPSS output or manual calculations and 1 to 2-page Word document

The completed assignment should address all of the assignment requirements, exhibit evidence of concept knowledge, and demonstrate thoughtful consideration of the content presented in the course. The writing should integrate scholarly resources, reflect academic expectations and current APA standards, and adhere to Northcentral University’s Academic Integrity Policy.

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Assignment 2 & Your first step for the final project project is to identify a data set of interest from Kaggle that is amenable to Excel manipulation and allows for extensive application of analytics models from our course. For example, a data set about types of jobs in the technology field might be interesting, but does not qualify (not a business analytics issue at the center of the topic). However, a data set about a company’s sales or customer satisfaction, etc. works fine. Kaggle has some great data sets for our purposes — Sales, satisfaction, HR analytics, etc. Have fun – find something you like.

Once you have identified a data set upload the data set in Excel. If it is approved, 10 points extra credit  are awarded if submitted before 11:45 PM on 6-12-22. I will also confirm in writing that it is approved (i.e. will add comment on Sakai). If it is not approved, it will receive a score of zero (0) in the grade center. Keep submitting a new data set until approved.

Here is the Kaggle location for the data set search:

https://www.kaggle.com/datasets?fileType=csv

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On an APA formatted paper that demonstrates your understanding of the hypothesis test. Think of an organization (real or fictitious), state an organizational problem (develop a problem statement), as a result of the problem statement state three research questions, develop real or fictitious data, and then demonstrate three hypothesis test. State your conclusion for each hypothesis and one general overall conclusion for the paper. At least one textbook, journal, or article as a Reference. About 5 – 6 pages. 

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Discussion Prompt: How do you use statistics in your work as a nurse or healthcare provider? Find and discuss at least two examples of how statistics are used in your field. Do you feel like statistics are a vital part of the nursing field? Why or why not?

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