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.
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
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?
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:
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:
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.
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?
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.
