Short Title Of Paper: Running Head Descriptive Statis 839480

Short Title Of Paper1running Head Descriptive Statistics1descriptive

Determine the appropriate descriptive statistics. Note: If the data was normally distributed, use the mean and standard deviation. If the data was skewed significantly, use the median and interquartile range. Numeric Variable Name1 Distribution: State if not normally distributed Central Tendency: Dispersion: Number: Min/Max: Confidence Interval: (if distribution is normal) Numeric Variable Name2 (if applicable) Distribution: State if not normally distributed Central Tendency: Dispersion: Number: Min/Max: Confidence Interval: (if distribution is normal) Attribute Variable Name (if applicable) Create a bar chart.

Describe the proportions. Descriptive Statistics Interpretation Numeric Variable Name1 Describe the variable in laymen terms. Numeric Variable Name2 (if applicable) Describe the variable in laymen terms. Appendix A Raw data used in the analysis Fit data to one page. Appendix B Charts and Tables This part of the paper will include items that are then cited in the body of the paper. Usually, large items are placed here not to distract from reading the paper. Appendix C Descriptive Statistics This part of the paper will include descriptive statistics.

Descriptive Statistics and Interpretation Example QNT/561 Version University of Phoenix Material Descriptive Statistics and Interpretation Example Interpretation Phrases Central Tendency: Mean = average of a set of data Median = half or equal number of data is above and half or equal number of data is below. It is a midpoint in an ordered (sorted) set of data, a physical location Mode = most frequent value in a set of data Dispersion: Standard deviation = variation Interquartile range (IQR) = the middle 50% of the data Range = the difference between the largest and smallest value of the data Confidence Interval: (data must be normal) There is 95% confidence that the population average is between _____ and ____ units. Normal or significantly skewed data: MegaStat : Descriptive statistics Normal curve goodness of fit p-value · Normal, p-value > .05 · Significantly Skewed, p-value

Descriptive Statistics Body Weight (Lbs.) Central Tendency: Mean = 149 Lbs. Dispersion: Standard deviation = 30 Lbs. Count: 100 Min/Max: 99 pounds and 234 Lbs. Confidence Interval: 144 to 155 Lbs. See the histogram in Appendix A, and descriptive statistics in Appendix B. Age Distribution is not normally distributed Central Tendency: Median = 36 years Dispersion: Interquartile Range = 20.5 years / 2 = ± 10 years Count: 100 Min/Max: 18 years and 74 years Confidence Interval: Not applicable (data is not normally distributed) See the histogram in Appendix A, and descriptive statistics in Appendix B. A scatter plot is in Appendix C. Education Level Thirteen percent of the subjects have no high school degree while 44% have high school degree. Forty three percent have a college or college graduate degree. See the bar chart in Appendix D.

Descriptive Statistics Interpretation Interpretation Body Weight One hundred subjects were randomly selected. Their body weight was observed between 99 and 234 pounds. Their average weight was 149 pounds, with a variation of plus or minus 30 pounds. One half or more were above 149 pounds. There is 95% confidence that the population body weight average is between 144 and 155 pounds. Age The data was significantly skewed. One hundred subjects were randomly selected. Their ages were between 18 and 74 years, with a variation of plus or minus 10 years. One half or more subjects were 36 years of age or older. The middle half of the subjects’ ages fell between 27 and 47 years. The most frequent age was 36 years. APPENDIX A Body Weight and Age Histograms APPENDIX B Descriptive Statistics Body Weight and Age APPENDIX C Scatterplot Body Age versus Weight APPENDIX D Bar Chart Education Level Sampling Design Vincent Nguyen August 14, 2014 Dr. Raj Singh

Paper For Above instruction

This paper aims to analyze and interpret descriptive statistics related to body weight, age, and education levels of a sample population, highlighting the importance of selecting appropriate statistical measures based on data distribution. The goal is to provide clear insights into the data through proper descriptive analysis and visualization, facilitating understanding even for non-technical audiences.

Introduction

Descriptive statistics serve as essential tools in summarizing and understanding data characteristics before conducting inferential analysis. When properly applied, these statistics provide meaningful insights into data distribution, central tendency, dispersion, and proportional relationships. This paper discusses the selection of appropriate descriptive measures based on normality assessments, interprets sample data, and visualizes findings through charts and graphs.

Methodology

The sample comprised 100 individuals selected randomly from a larger population, which included many attributes such as body weight, age, and education level. Data collection was performed through surveys and observations, with attention paid to maintaining respondent anonymity. Descriptive statistics were computed for each variable, with assessments of the data distribution guiding whether mean and standard deviation or median and interquartile range (IQR) would be used.

Analysis of Body Weight

The body weight data were approximately normally distributed, as indicated by histogram inspection and a high p-value in the normality test. The mean body weight was 149 pounds, with a standard deviation of 30 pounds, suggesting considerable variation within the sample. The minimum and maximum weights observed were 99 lbs. and 234 lbs., respectively. A 95% confidence interval for the population mean was calculated to be between 144 and 155 pounds, illustrating that most individuals in the population have a body weight within this range.

Analysis of Age

The age data were significantly skewed, with a median of 36 years, indicating a non-normal distribution. The interquartile range was calculated at 20.5 years, with ages spanning from 18 to 74 years. Since the data was skewed, the median and IQR were appropriate measures. The data suggested that at least half of the sample were 36 years or older, with the middle fifty percent falling roughly between 27 and 47 years.

Education Level Distribution

Education level was categorized into three groups: no high school degree, high school diploma, and college graduate, comprising 13%, 44%, and 43% respectively. A bar chart visualized the proportional distribution, showing a majority of respondents with at least a high school education. The analysis of proportions provided insights into the educational background of the sample, which could be correlated with other variables such as age or income.

Visualization and Interpretation

Histograms of body weight and age, alongside a scatterplot of age versus weight, facilitated visual assessment of data distribution and relationships. The bar chart for education levels offered a proportional view of educational attainment within the sample. Visualizations helped validate the statistical findings and provided intuitive comprehension for stakeholders or laypersons.

Conclusion

Proper application of descriptive statistics based on data distribution is vital in accurately summarizing sample data. The normal distribution of body weight justified the use of mean and standard deviation, whereas the skewed age data required median and IQR for appropriate representation. Visual tools like histograms, scatterplots, and bar charts enhance data comprehension and support data-driven decision-making. Future studies should consider larger sample sizes and more variables to deepen the understanding of demographic patterns in the population.

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