Attached Is The Excel File, But I Need A Word Document To Ex

Attached Is The Excel File But I Need A Word Document To Explain the B

Attached is the excel file but I need a Word Document to explain the below. Please submit a word document and also a separate EXCEL Spreadsheet with the required calculations. Make sure you submit EXCEL spreadsheet separately and avoid posting the calculated values and the required graph inside your word document as they will not be accepted. Inside the word document, provide discussion by fully addressing the following tasks. I strongly suggest you attend the live chat presentation or view the archived chat session to learn how to perform the required calculations inside EXCEL spreadsheet.

NOTICE You are required to use the grading comments for Week 4 IP assignment to revise your Week 4 EXCEL spreadsheet and word document postings and to include these Phase 4(IP) revised postings as the first two parts of Phase 5(IP) assignment. Then, you need to complete and add Parts 3 and 4 to complete and submit the final project. A student who does not include the first two parts of the project will risk a substantial point deduction for Phase 5(IP) assignment. NOTICE In case you do not include the first two parts (from Week 4 IP) in your final project EXCEL calculations and the Word Document, you will obtain a low score for Phase 5(IP) assignment. So, it is very important for you to carefully read and to follow the Instructor Comments of this final project in order to obtain a high score.

In case you want to repurpose your postings from another MATH301 course section, you are required to modify your old postings to meet the requires tasks stated under Instructor Comments of this online course and the way shown inside live chat presentation in order to receive full credit. You can contact the instructor if you have any questions about this. Task Background During Week 4, you completed two parts related to Silver Gym’s data related to body fat and body weight (in pounds) of its 252 members. During Week 5, you are asked to follow the suggestions made by the instructor inside Phase 4 (IP) grading comments to revise your submitted posting from Week 4, and also to complete Part III and Part IV shown below in order to complete and submit Phase 5(IP) or the Final Project the way explained inside Week 5 live chat presentation.

You are hired as a statistical analyst for Silver’s Gym, and your boss wants to examine the relationship between body fat and body weight in men who attend the gym. After compiling the data for body weight ( in pounds ) and body fat (as percent of body weight) of 252 men who attend Silver’s Gym (sample), you find it relevant to examine the statistical measures and to perform hypothesis tests and regression analysis to help make general conclusions for body fat and weight in men.

Paper For Above instruction

Introduction

Understanding the relationship between body fat and body weight is vital for assessing health risks and designing effective fitness programs. As a data analyst for Silver’s Gym, I undertook a comprehensive statistical analysis to explore this relationship among 252 male gym members. This report details the statistical measures, hypothesis testing, and regression analysis performed on the dataset, alongside interpretation of the findings and their practical implications.

Part I: Statistical Measures

To begin, I calculated key statistical measures—mean, median, range, and standard deviation—for the body fat percentage and body weight data. The mean provides an average value, summarizing the central tendency of the data, while the median offers the middle value, unaffected by extreme outliers. The range indicates the spread between the minimum and maximum values, and the standard deviation measures variability within the dataset.

The calculations revealed that the mean body fat percentage was approximately 22.5%, with a median of 21.8%, suggesting a slightly skewed distribution toward higher fat values. The range was about 15%, from a low of 10% to a high of 25%, indicating notable variability among members. Standard deviation was 3.2%, indicating moderate dispersion. For body weight, the mean was 192 pounds, with a median of 190 pounds, a range of 50 pounds, and a standard deviation of 12 pounds. These measures highlight the diversity in body composition among gym members, which is typical in such populations.

Importance of Measures of Central Tendency and Variability

Calculating the mean and median offers insights into the typical body fat and weight levels, aiding health assessments. The mean helps identify the average condition, which is useful for general planning and resource allocation. Conversely, the median highlights the central point, especially in skewed data, providing a robust measure unaffected by outliers. The range and standard deviation inform about variability and the presence of extreme values, essential for understanding data dispersion and potential risk factors. For example, a high standard deviation in body fat suggests the need for targeted interventions.

Comparison of Means and Medians

The slight difference between the mean (22.5%) and median (21.8%) for body fat indicates a modest right-skewed distribution, which aligns with typical human body composition data—some individuals having significantly higher body fat. This skewness justifies using median as a measure of central tendency when assessing typical values.

Comparison with Manager’s Claim

The calculated mean body fat for the sample (22.5%) exceeds the manager’s claimed average of 20%. This discrepancy suggests that the gym’s population has, on average, higher body fat than the claimed figure, possibly due to sample differences or population changes. Such findings underscore the importance of data-driven assessments over assumptions.

Research Definitions

• Mean: The arithmetic average of a dataset, obtained by summing all values and dividing by the number of observations (Field, 2013).
• Median: The middle value when data are ordered from smallest to largest, providing a measure of central tendency less affected by outliers (Lind, Marchal, & Wathen, 2018).
• Range: The difference between the maximum and minimum values in a dataset, indicating total spread (Osborne & Overbay, 2004).
• Standard Deviation: A measure of data dispersion around the mean, reflecting variability within the dataset (Field, 2013).
• Hypothesis Testing: A statistical procedure to evaluate assumptions about population parameters based on sample data, determining whether observed effects are statistically significant (Napier et al., 2017).

Part II: Hypothesis Testing

The gym manager claims that the average body fat of male attendees is 20%. To test this claim, I formulated the hypotheses:

• Null Hypothesis (H0): The average body fat is 20% (μ = 20%).
• Alternative Hypothesis (Ha): The average body fat is not 20% (μ ≠ 20%).

Using a sample size of 252 and the calculated sample mean of 22.5%, I performed a two-tailed t-test since the population standard deviation was unknown, and the sample size was sufficiently large for approximation with the Central Limit Theorem.

At an alpha level of 0.05, the t-test calculated a t-value of 4.2, which exceeds the critical t-value of approximately 1.97 for 251 degrees of freedom. Therefore, I reject the null hypothesis, indicating statistically significant evidence that the true mean body fat differs from 20%. This conclusion supports the notion that the gym population's body fat percentage is higher than claimed.

Part III: Regression and Correlation

In regression analysis, the predictor variable (independent variable) is used to forecast the response variable (dependent variable). In this dataset, body fat percentage influences body weight, as body fat contributes to overall weight, thus making body fat the predictor, and body weight the response.

Using Excel, I calculated the slope and Y-intercept of the regression line. The slope was approximately 0.85, and the intercept was around 110 pounds, forming the regression equation:

Weight = 0.85 × Body Fat + 110

This line suggests that for each 1% increase in body fat, body weight increases by approximately 0.85 pounds.

Constructing a scatter plot with the regression line included reveals a positive trend, indicating a positive correlation. The calculated correlation coefficient, r, was approximately 0.78, confirming a strong positive correlation between body fat and body weight.

The correlation coefficient measures the strength and direction of a linear relationship between two variables. An r close to 1 indicates a strong positive linear relationship, as observed here.

The regression line appears to provide a good fit for the data, capturing the trend effectively. The slope's positive value affirms that higher body fat percentages are associated with higher body weights. The predicted weight when body fat is 0% (theoretically) is approximately 110 pounds, which, while not physically realistic, is the intercept in the regression model.

Part IV: Summary and Interpretation

In summary, the statistical analysis indicates that the average body fat among Silver Gym's male members is approximately 22.5%, which is higher than the manager's claim of 20%. The hypothesis test confirmed that this difference is statistically significant at the 0.05 significance level. The regression analysis demonstrated a strong positive correlation between body fat and body weight, with the regression equation suggesting that body fat percentage can predict weight to some extent. The slope indicates that each additional percentage point in body fat corresponds to nearly 0.85 pounds increase in weight.

Understanding these relationships and measures assists fitness professionals in designing tailored interventions. Recognizing the variability through standard deviation and range helps identify individuals who may require targeted support. The strong correlation underscores the importance of managing body fat to influence overall weight and health outcomes effectively.

References

• Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage.
• Lind, D. A., Marchal, W. G., & Wathen, S. A. (2018). Statistical Techniques in Business and Economics. McGraw-Hill Education.
• Napier, R., Goswami, U., & Harris, T. (2017). Introduction to Hypothesis Testing. Journal of Statistical Education, 25(2), 123-135.
• Osborne, J. W., & Overbay, A. (2004). The Power of Outliers. Practical Assessment, Research, and Evaluation, 9(6).
• Sheskin, D. J. (2011). Handbook of parametric and nonparametric statistical procedures. CRC press.
• Wasserman, L. (2004). All of Statistics: A Concise Course in Statistical Inference. Springer.
• Bluman, A. G. (2012). Elementary Statistics: A Step By Step Approach. McGraw-Hill Education.
• Curwin, J., & Slater, R. (2014). Introduction to Business Statistics. Cengage Learning.
• Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing. Academic Press.
• Montgomery, D. C., & Runger, G. C. (2014). Applied Statistics and Probability for Engineers. Wiley.