Create A Microsoft Excel Spreadsheet With Two Variabl 691574

Create a Microsoft Excel spreadsheet with the two variables from you

Create a Microsoft® Excel® spreadsheet with the two variables from your learning team's dataset. Analyze the data with MegaStat®, StatCrunch®, Microsoft® Excel® or other statistical tool(s), including: (a) Descriptive stats for each numeric variable (b) Histogram for each numeric variable (c) Bar chart for each attribute (non-numeric) variable (d) Scatter plot if the data contains two numeric variables. Determine the appropriate descriptive statistics. (a) For normally distributed data, use the mean and standard deviation. (b) For significantly skewed data, use the median and interquartile range. Use the Individual Methodology Findings Template to complete the descriptive statistics. Use the Descriptive Statistics and Interpretation Example to develop an interpretation of the descriptive statistics.

Format your paper consistent with APA guidelines. A new study will be conducted to determine whether a low-fat diet is effective in lowering SBP. Study subjects from the sample will be randomized to the low-fat diet group or to the normal-diet group. The investigators will randomly assign 50% of subjects to the low-fat group and 50% to the normal-diet group. How many total subjects should be studied to have 80% power for detecting a reduction of 10 mmHg in SBP due to exercise, assuming a 0.01 type I error and 2-sided alternative hypothesis? Briefly describe the approach you used. How do I figure this out? There are two data sets (which contain demographics (subject.dat) and clinical data (clinic.dat) with the following data descriptions (and are attached): Subject.DAT Variable Columns Description ------------------------------------------------------------------------------------------------------- ID 1-5 Subject ID Sex 7 , Age 9-12 Age in years Weight 14-22 Weight in kilograms Height 25-32 Height in meters ------------------------------------------------------------------------------------------------------- Clinic.DAT Variable Columns Description ------------------------------------------------------------------------------------------------------- ID 1-5 Subject ID SBP 15-17 Systolic blood pressure (mmHg) Chol 25-27 Total cholesterol (mg/dl) HDL 30-32 High density lipids (mg/dl)

Paper For Above instruction

The task involves creating a Microsoft Excel spreadsheet utilizing two variables from your dataset, followed by comprehensive statistical analysis and visualization. Additionally, a sample size calculation for a clinical study is required, based on the given parameters. This paper elucidates the methodology, analysis techniques, and reasoning applied, aligning with APA standards for formatting and scholarly rigor.

Data Selection and Preparation

The first step is selecting two relevant numeric variables from the dataset. For this analysis, we focus on systolic blood pressure (SBP) and age, given their clinical significance and variability. The data is extracted from the provided clinical dataset (clinic.dat), specifically from columns corresponding to SBP (columns 15-17) and Age (columns 9-12). These variables are imported into Excel to facilitate analysis. Proper data cleaning involves checking for missing values, inconsistencies, or outliers that could distort descriptive statistics or visualizations.

Descriptive Statistics

Descriptive analytics encompass calculating measures such as mean, median, standard deviation, and interquartile range, depending on data distribution. To determine the appropriate measures, tests for normality—such as the Shapiro-Wilk or Kolmogorov-Smirnov test—are recommended. If SBP and Age appear normally distributed, the mean and standard deviation are used; if skewed, median and interquartile range are more appropriate.

For example, if the SBP data is approximately normal, its mean and standard deviation will be calculated. Conversely, if the data shows skewness, median and interquartile range are reported. Similar procedures apply to Age. Excel functions such as =AVERAGE(), =STDEV(), =MEDIAN(), =QUARTILE.INC() assist in these calculations.

Data Visualization

Visual representations include histograms for each numeric variable, to observe distribution shape; bar charts for categorical variables like Sex; and scatter plots for pairs of numeric variables, such as SBP versus Age. Histograms can be generated using Excel's Insert Chart feature, selecting the histogram type or using data analysis tools like MegaStat or StatCrunch. Bar charts illustrate the frequency distribution of categorical data, while scatter plots reveal potential correlations or trends.

Determining Summary Measures

Based on the distribution analysis, the appropriate descriptive statistics are selected. For normally distributed data, report means and standard deviations; for skewed data, report medians and interquartile ranges. This ensures accurate representation of the data’s central tendency and variability, facilitating correct interpretation and subsequent inferential analysis.

Sample Size Calculation for Clinical Study

To determine the number of subjects needed for the low-fat diet study assessing SBP reduction, we apply the standard formula for comparing two means with specified power and significance level:

n = ( (Z_1-α/2 + Z_1-β)² 2 σ² ) / Δ²

Where:

• Z_1-α/2 = 2.576 (for α=0.01, two-sided)
• Z_1-β = 0.842 (for 80% power)
• σ = standard deviation of SBP (estimated from previous studies or pilot data)
• Δ = clinically meaningful difference = 10 mmHg

Assuming an estimated standard deviation (σ) of 15 mmHg (from literature or preliminary data), the calculation proceeds as follows:

n = ( (2.576 + 0.842)² 2 15² ) / 10² = (3.418)² 2 225 / 100 = 11.69 * 450 / 100 ≈ 52.6

Thus, approximately 53 subjects per group are needed. To account for potential dropouts or non-compliance, increasing sample size by 10-20% is prudent, leading to about 58-64 subjects per group. Therefore, total sample size amounts to approximately 116-128 subjects.

Approach Summary

The approach integrates descriptive statistical analysis, distribution assessment, visualization, and sample size estimation. Data from the clinical dataset (clinic.dat) is imported into Excel and cleaned. Distribution normality tests guide the choice of descriptive measures. Histograms, bar charts, and scatter plots aid visualization. For the sample size calculation, standard epidemiological formulas are applied with parameters derived from previous research or pilot data, ensuring adequate power to detect the specified SBP reduction.

Conclusion

This comprehensive methodology provides a solid foundation for data analysis and clinical trial planning. Accurately selecting descriptive statistics ensures valid interpretation, and proper sample size calculation guarantees sufficient power for hypothesis testing. Utilizing Excel and statistical tools streamlines this process, supporting evidence-based decision-making in medical research.

References

• Armitage, P., & Berry, G. (2014). Statistical Methods in Medical Research. Blackwell Publishing.
• Field, A. (2013). Discovering Statistics Using SPSS. Sage Publications.
• Moore, D. S., & McCabe, G. P. (2012). Introduction to the Practice of Statistics. W. H. Freeman.
• Vittinghoff, E., & McCulloch, C. E. (2007). Relaxing the rule of ten events per variable in logistic and Cox regression. American Journal of Epidemiology, 165(6), 617-622.
• Rosner, B. (2015). Fundamentals of Biostatistics. Cengage Learning.
• Higgins, J. P., & Green, S. (Eds.). (2011). Cochrane Handbook for Systematic Reviews of Interventions. Cochrane Collaboration.
• Hulley, S. B., et al. (2013). Designing Clinical Research. Lippincott Williams & Wilkins.
• Gupta, S. (2011). Fundamentals of Biostatistics for Medical Students and Clinicians. Jaypee Brothers Medical Publishers.
• Dolan, G. (2016). Sample Size Calculations in Clinical Research. Journal of Clinical Research & Bioethics, 7(2), 210-215.
• Albert, P. S., & Bickel, D. R. (2018). Statistical Methods in Medical Research. Springer.