BUSA2182 Introduction To Business Statistics Fall 2018 Exam

BUSA2182 Introduction To Business Statistics Fall 2018 Exam 2

Analyze data gathering, probability, and descriptive statistics based on multiple real-world scenarios and datasets involving customer counts, dice rolls, traffic, and service times. Cover calculations of quartiles, boxplots, skewness, probability distributions, and measures of central tendency and variability, including mean and variance. Apply Excel functions for statistical calculations and interpret results within practical contexts such as restaurant traffic, dice experiments, traffic monitoring, and car wash operations.

Paper For Above instruction

The second examination in an introductory business statistics course encompasses a series of problems requiring comprehensive understanding and application of statistical concepts, including descriptive statistics, probability, and data analysis techniques. The tasks involve analyzing datasets collected from diverse scenarios—restaurants, traffic, and car washes—demonstrating proficiency in calculating quartiles, constructing boxplots, computing skewness, assessing probabilities of specific events, and understanding distributions of discrete random variables. Additionally, the exam emphasizes the use of software tools like Excel for statistical computations and interpretation of results within real-world contexts.

The first question focuses on summarizing restaurant customer data collected over 15 days, asking for calculations of specific quartiles (Q1, Q2, D3, D6), the creation of a boxplot, and the determination of software-based skewness. These measures provide insights into data distribution, central tendency, and outliers, which are essential in business analytics for decision-making related to operational planning and customer behavior assessment.

Question two involves a probability experiment with rolling two dice multiple times, requiring identification of the experiment's nature, enumeration of outcomes, and calculation of probabilities for sums of 4, 6, and less than 5. This problem models chance events with discrete outcomes, illustrating fundamental probability principles relevant in various operational and risk assessments.

In the third task, data on daily customer counts at a restaurant during evening hours over two weeks are analyzed to compute probabilities of observing at least one, exactly one, zero, or at most two customers. These questions engage with Poisson or binomial probability models to interpret event frequencies within a specific time window, facilitating operational capacity planning and service level optimization.

Question four presents a probability distribution table for customer entry counts in a small restaurant, requiring calculations of the distribution's mean and variance, both manually and using Excel. This problem demonstrates discrete probability distribution analysis, key in modeling and forecasting customer traffic and resource allocation.

Similarly, question five examines traffic volume data recorded across 14 days, calculating probabilities of observing various counts of passing cars during early morning hours. These analyses utilize probability models to evaluate likelihoods of different traffic scenarios, important in traffic management and infrastructure planning.

The final question addresses customer arrival times at a car wash, modeled as a uniform distribution over a 48-minute interval. It involves calculating the expected waiting time (mean), and probabilities that the station remains idle for more than 20 minutes or between 15 and 45 minutes. These problems test understanding of continuous uniform distributions and their application in time-related service processes, critical for optimizing service durations and workflow scheduling.

Across all questions, students are expected to show full calculations, interpretations, and utilize Excel where appropriate, to demonstrate their hands-on competence in applying statistical techniques to real-world business and operational data. This comprehensive evaluation highlights foundational skills in descriptive and inferential statistics, vital for data-driven decision-making in business contexts.

References

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