Bus 105 Trimester 1 2018 Instructions For The Computing Assi

Bus 105 Trimester 1 2018instructions For The Computing Assignment

Analyze and complete a comprehensive computing assignment related to statistical analysis, data summarization, hypothesis testing, and data visualization across eight sections. The assignment involves creating and interpreting pivot tables, charts, confidence intervals, and conducting hypothesis tests based on datasets provided or collected. Proper understanding of variables, datasets, and statistical concepts is crucial. Students must submit a unified document in Word or PDF format, incorporating their own analyses, interpretations, and graphical representations, ensuring academic integrity and demonstrating learning outcomes. The task emphasizes hands-on data analysis, accurate use of Excel features, critical interpretation, and application of statistical theories to real-world/business scenarios. The assignment is distributed across multiple sections covering descriptive statistics, comparisons between groups, visualizations, hypothesis testing, confidence intervals, and abstract statistical reasoning involving z-scores and sampling distributions.

Paper For Above instruction

The assignment requires a thorough understanding of descriptive and inferential statistics, emphasizing data summarization, graphical analysis, and hypothesis testing. Across the eight sections, students engage in practical data analysis tasks that reinforce theoretical concepts and develop skills in interpreting real datasets. This process involves the use of Excel pivot tables, charts, and online tools for p-value calculations, along with critical reasoning about relationships among variables and their implications for business decisions.

Section 1: Understanding Variables and Datasets

In this initial section, students are asked to review a sample statistical report from the previous semester and evaluate how the author applied the principles outlined in the dataset summarization guide. The focus is on demonstrating comprehension of fundamental concepts such as variables—characteristics or attributes that can vary among data points—and datasets, which are collections of observations. Students need to analyze provided examples, identifying how variables are classified as categorical (qualitative) or quantitative, and how datasets are structured for analysis. This foundational understanding helps in forming correct interpretations and avoiding common mistakes encountered in prior assessments, such as confusing frequency with quantitative measurements or mislabeling variable types.

Section 2: Relationship Between Age and Product Preference

Using a sampled dataset, students employ Excel pivot tables to summarize responses related to age groups (“old” or “young”) and preferences (“like” or “hate” the product). The task involves extracting sample sizes and proportions of positive responses within each group. The summarized data help compare the喜欢 rate between age groups and provide empirical estimates for the difference in proportions (p₁ - p₂). Interpreting these results involves assessing whether observed differences suggest a meaningful relationship or are likely due to chance. A comment on the nature of the relationship, based on the difference in proportions, may involve discussing whether age influences product preference, considering sample variability and potential implications for marketing strategies.

Section 3: Comparing Profits from Different Machines

This section requires analysis of a dataset related to profits earned from two different gaming machines, segmented by age groups. Using pivot tables, students calculate sample sizes, average profits, and standard deviations for each subgroup. These statistics facilitate comparison of mean profits and their variability, offering insights into which machine yields higher returns for different age segments. The estimated difference in means (µ₁ - µ₂) is obtained by subtracting the computed sample means. This analysis aids in deciding optimal machine placement or targeting specific demographics, based on profit performance.

Section 4: Data Visualization and Profit Estimation

Students are provided with data points to create scatterplots illustrating relationships between variables—such as advertising expenditures and sales or other relevant metrics. Visual interpretation of these plots helps determine the nature and strength of relationships. Using the models derived from this data, students estimate profits or outcomes for hypothetical scenarios—such as predicting casino earnings with 1,000 bets—by applying regression equations or simple calculations based on the scatterplot trends.

Section 5: Hypothesis Testing for Proportions and Means

This critical section involves conducting formal statistical hypothesis tests to evaluate claims about population parameters. For proportions, students test whether the difference between two sample proportions is statistically significant at a 5% significance level, by formulating null and alternative hypotheses, calculating p-values (using online resources), and making rejection decisions. Similarly, for means, students test differences in average profits or other continuous measures, using sample data, p-values, and conclusions articulated in plain language. These tests assess whether observed differences are statistically meaningful or attributable to sampling variability.

Section 6: Confidence Interval Estimation

Using a separate opinion poll sample, students calculate the sample proportion and construct a 90% confidence interval for the true proportion of support for a proposed business change. The calculation involves determining the standard error, applying the appropriate z-value (1.645 for 90%), and presenting the interval. This estimation quantifies the range within which the actual population parameter is likely to lie, informing business decisions with a measure of statistical certainty.

Section 7: Data Visualization and Variable Classification

Students are tasked with sourcing a back-to-back histogram example from the web, then analyzing it to understand comparative distributions of two variables. They classify each variable as categorical or quantitative based on the question posed, and interpret the relationship between the variables presented, considering how such visualizations can aid in business insights. Additionally, they evaluate the relevance of the findings for decision-making, drawing from an example involving gender and willingness to pay, illustrating how visual analysis can inform marketing and product strategies.

Section 8: Abstract Data Analysis Using Z-scores and Sampling Distributions

This advanced section involves probabilistic reasoning with z-scores and normal distributions. Students calculate z-scores for estimated parameters (from sections 2, 3, and 4), determine cumulative probabilities (using online tools), predict the rank position of specific estimates in hypothetical ordered lists, and compare these predictions with actual rankings. The exercise emphasizes understanding the properties of sampling distributions, the role of p-values in hypothesis testing, and how different datasets can share similar statistical properties despite having different underlying data. These analytical skills support interpreting statistical results in research and business contexts.

References

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• Bluman, A. G. (2017). Elementary Statistics: A step-by-step approach. McGraw-Hill Education.
• Gal, I., & Romberg, T. A. (2009). A life cycle approach to statistics education. Springer.
• Moore, D. S., McCabe, G. P., & Craig, B. A. (2012). Introduction to the Practice of Statistics. W.H. Freeman.
• Newbold, P., Carlson, W., & Thorne, B. (2013). Statistics for Business and Economics. Pearson.
• Ott, R. L., & Longnecker, M. (2015). An Introduction to Statistical Methods and Data Analysis. Cengage Learning.
• Ross, S. M. (2014). Introductory Statistics. Academic Press.
• Siegel, S., & Castellan, N. J. (1988). Nonparametric Statistics for the Behavioral Sciences. McGraw-Hill.
• Wasserstein, R. L., & Lazar, N. A. (2016). The ASA Statement on p-values: Context, process, and purpose. The American Statistician, 70(2), 129-133.
• WolframAlpha. (n.d.). P(Z