The Following Table Describes Variables From Height And Weig

The Following Table Describes Variables From The Heightweightdta F

The assignment involves analyzing data sets related to wages, height, and driving safety, as well as evaluating customer loyalty system options for Petrie Electronics. Specifically, you are asked to perform multiple regression analyses on the height and wage data, assess omitted variable bias in the models, and interpret the implications of excluding variables like IQ and eye color. Additionally, you should analyze the relationship between cell phone subscriptions and traffic fatalities, considering additional variables like population and miles driven, to understand model dynamics. The assignment also explores factors influencing speeding ticket fines, examining age's significance, potential endogeneity, and the effects of sample size on statistical inference. Lastly, it requires an evaluation of different customer loyalty system alternatives for Petrie Electronics, considering their features, costs, and suitability, while discussing reasons against building a custom system in-house.

Paper For Above instruction

The comprehensive analysis of the provided data sets and business scenarios aims to deepen understanding of regression modeling, omitted variable bias, and strategic decision-making in system development. Beginning with the height-weight data, the first task involves estimating two Ordinary Least Squares (OLS) regression models to elucidate the relationship between wages and height. The initial model regresses adult wages solely on adult height, providing a baseline understanding of this relationship. The second includes adolescent height as an additional predictor, revealing whether early-life height influences adult wages independently or shares variance with adult height. Comparing coefficients and model fit emphasizes how incorporating relevant variables impacts the estimated effects, highlighting potential confounding factors. Notably, the change in the coefficient on adult height between models suggests possible mediating or confounding influences from adolescent height, which warrants discussion on multicollinearity and variable omission.

Addressing the issue of omitted variables, the absence of IQ from the model is scrutinized. IQ, correlated with both height and wages, might bias estimates if omitted, raising the question of whether this omission causes endogeneity or bias. Empirical and theoretical evidence suggests that unless IQ correlates with the included variables in a way that violates exogeneity assumptions, its omission may not fundamentally threaten unbiased estimation—though it can impact model completeness. Similarly, excluding eye color, a non-influential characteristic in economic outcomes, is unlikely to pose a problem due to its negligible correlation with wages and other regressors.

The analysis extends to vehicle safety data, examining whether cell phone use exacerbates traffic fatalities. The initial regression of traffic deaths on cell phone subscriptions attempts to quantify this relationship, recognizing measurement limitations—since subscriptions do not directly indicate usage intensity or driver behavior. Subsequently, incorporating population and miles driven refines the model, accounting for variations in exposure and demographics. Changes in the coefficient on cell phone subscriptions reveal how confounding variables may influence the apparent effect, underscoring the importance of comprehensive models for causal inference.

In the context of traffic tickets issued for speeding, the effect of age on fine amounts is examined using regression models. The initial model tests whether age is a significant predictor, considering potential endogeneity due to omitted variables or reverse causality—such as whether certain age groups are more likely to be fined or more prone to speeding. Incorporating additional controls like miles over the speed limit modifies the estimated effect, often reducing bias and improving model validity. The impact of sample size reduction on statistical inference, especially standard errors and t-statistics, emphasizes the importance of adequate data for reliable results, illustrating how smaller samples tend to inflate standard errors and reduce statistical power.

Finally, the assignment explores strategic decision-making for Petrie Electronics' customer loyalty system development. The high-level requirements and constraints highlight key considerations: system effectiveness, usability, proven performance, ease of implementation, scalability, vendor support, costs, timeline, and staff capabilities. The evaluation of three alternative vendor systems— warehousing-centric, CRM-based, and proprietary cloud-based— demonstrates how features, costs, vendor support, and technological fit influence selection. The discussion advocates against in-house development, emphasizing risks such as high costs, time consumption, and technical complexity, supporting the strategic choice of licensing existing solutions for efficiency and reliability.

References

• Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
• Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson Education.
• Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis. Guilford Publications.
• Stock, J. H., & Watson, M. W. (2015). Introduction to Econometrics (3rd ed.). Pearson.
• Wooldridge, J. M. (2016). Introductory Econometrics: A Modern Approach (6th ed.). Cengage Learning.
• Aaker, D. (1991). Managing Brand Equity: Capitalizing on the Value of a Brand Name. The Free Press.
• Reinartz, W., & Kumar, V. (2000). On the profitability of long-term customers in retailing: An examination of the impact of satisfaction on consumer retention. Journal of Marketing, 65(4), 1-20.
• Rhee, S. K., & Rha, J. S. (2014). The effect of technology adoption on customer satisfaction and loyalty in retail banking. International Journal of Bank Marketing, 32(2), 209-229.
• Schneider, H. (2019). Strategic management of customer loyalty programs. Journal of Business Strategies, 35(3), 45-60.
• Compatibility Standards in Customer Loyalty Systems (2020). Journal of Computer Systems and Software, 36(4), 75-85.