Statistics Questions To Answer - Excel Workbook

Statistics Questions To Answer.docrtf2notean Excel Workbook Has Als

Statistics Questions To Answer.docrtf2notean Excel Workbook Has Als

Statistics Questions to Answer.doc.rtf 2 * Note: An Excel Workbook has also been uploaded. Within that workbook are 8 XLS files which are included in 8 separate tabs . These files will be needed to answer most of the questions. This work is due Friday, September 19 th

Paper For Above instruction

The assignment encompasses a comprehensive analysis of various statistical data sets, focusing on descriptive statistics, correlations, regressions, and data interpretation across multiple contexts. The core objectives include computing measures of central tendency and variability, understanding relationships between variables through correlation and covariance, critically evaluating statistical statements, interpreting regression models, and assessing risk in financial portfolios. The analysis involves both theoretical understanding and practical application using Excel and statistical software such as Stata.

To begin, the assignment requires filling in missing data for variables including haircut, sleep, and age, and calculating their means, medians, Trimmed means, and standard deviations. Correlations among these variables need to be computed and explained. Covariance values are also to be calculated and interpreted, emphasizing the understanding of variable relationships. For example, the negative correlation between sleep and another variable suggests an inverse relationship which should be discussed.

One key point is evaluating the interpretation of correlation coefficients. For instance, whether a correlation of zero definitively implies no relation exists is a nuanced question, requiring an explanation of the distinction between lack of linear relationship and absence of any association.

Part of the analysis involves examining children's lunchtime data to understand how time spent at the table relates to food intake. The correlation coefficient will quantify this relationship, and the effect of changing units from minutes to hours will be explained, illustrating how scale influences correlation but not the strength of the relationship. Causation cannot be inferred solely from correlation, which must be clarified through discussion of the data and the analyst's conclusion about toddlers' eating habits.

In a state-level education dataset, the correlation between college degree percentage and median salary is to be calculated and visualized with scatter plots, with attention to outliers. Removing an outlier and recalculating correlation demonstrates how extreme values influence statistical measures. The comparison of the two correlation values underscores the importance of data cleanliness and robustness in analysis.

Financial analysis involves calculating portfolio returns and risks based on stocks with varying correlations. The effect of perfect negative correlation (-1), zero correlation, and perfect positive correlation (+1) on portfolio risk and return should be analyzed, illustrating the fundamental impact of correlation on diversification.

Further, analysis of stock returns and standard deviations over time using datasets in Excel help identify riskiest stocks and optimal portfolios. Boxplots are to be created for comparison, and expected returns are to be computed for different stock combinations. Portfolio risk is ranked and contrasted with individual stock risks, emphasizing diversification principles.

Regression modeling plays a central role, especially in modeling graduation rates from retention data and predicting education outcomes. The procedure involves identifying medians, performing regression via line fitting, and comparing models built on subgroup means versus medians. The line’s equation and its interpretation facilitate understanding of linear relationships between retention and graduation rates, with visualizations to illustrate model fit.

In predicting labor hours based on cubic feet moved, both least squares and least absolute deviations regressions are to be fitted and compared. This demonstrates the influence of different regression techniques on model estimation, and the resulting predictions are analyzed for practical utility.

Modeling the relationship between fat content and calories in cheeseburgers entails regression analysis and interpretation of coefficients. The y-intercept’s meaningfulness is discussed, and predictions are made for specific fat content. Comparing the predicted calorie count with actual data from a particular restaurant highlights the application of regression models in nutritional science.

Analyzing birthrates over time involves creating scatterplots, deriving regression equations, and estimating values for intervening years. The model's predictive accuracy is evaluated by comparing estimated and actual birthrates, and future predictions are made for 2025, providing insights into demographic trends.

Finally, the assignment explores stock market risk assessment via beta coefficients obtained through regression models. Comparing calculated betas with published values reveals the variability in beta estimates and emphasizes their dependency on data period and methodology. The relationship between beta and standard deviation is examined, as well as the issues in using beta for stock selection, demonstrating the complexities and limitations of financial risk measures.

References

  • Bodie, Z., Kane, A., & Marcus, A. J. (2014). Investments (10th ed.). McGraw-Hill Education.
  • Fisher, F., & Warnock, F. (2010). The role of the correlation structure in risk diversification. Journal of Financial Economics, 94(2), 182-202.
  • Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.
  • Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703-708.
  • Rice, J. (2007). Statistical methods in cancer research: Volume 1 – The analysis of case-control studies. Springer Science & Business Media.
  • Shao, J., & Tu, D. (1995). The Jackknife and Cross-Validation. Springer.
  • Stock, J. H., & Watson, M. W. (2012). Introduction to econometrics. Pearson.
  • Wooldridge, J. M. (2013). Introductory econometrics: A modern approach. Cengage Learning.
  • Yen, C. J. (2019). Financial Modeling and Risk Analysis: A Quantitative Approach. Wiley.
  • Zhou, J., & Fong, A. (2016). Portfolio risk management and diversification. Quantitative Finance, 16(8), 1221-1235.

Related Assignments