Inferential Statistics And Findings Grading Guide QNT561 Ver

Inferential Statistics And Findings Grading Guideqnt561 Version 72ind

The research question and two variables your learning team developed for the Week 2 assignment are used and include: (a) a research question (b) mock data for the independent and dependent variables. You have provided some insight into the research question and have provided the data. The appropriate statistical tool to test the hypothesis based on the research question is determined. The statistical tool here in this case refers to the type of statistical methodology you will used to address the question and analyze the data. Here you have mentioned the usage of a One Way ANOVA. This is fine, however it seems more reasonable to approach this using a two sample independent T-Test. Also, you have not included any hypothesis statements. You make mention of them, but never actually included them anywhere. A hypothesis test with a 95% confidence level, using the statistical tool is conducted. Interpretation of the results and your findings are provided. You have not provided some discussion on the matter, however, I think your interpretation of rejection is off. You have not included your hypothesis statements, however, your null hypothesis should always suggest some sort of equality. Therefore if you fail to reject, you will not have enough evidence to suggest there is a difference… Both the spreadsheet and the paper that is no more than 350 words are submitted.

Paper For Above instruction

The use of inferential statistics is essential in research to draw meaningful conclusions from sample data about larger populations. In this context, selecting the appropriate statistical tool depends on the research question, data type, and study design. The initial approach suggested the use of One Way ANOVA; however, considering the nature of the variables—comparing two independent groups—a two-sample independent t-test is more appropriate. This section discusses the rationale behind the choice of statistical tests, formulation of hypotheses, interpretation of results, and implications for decision-making.

Research Question and Hypotheses

The central question guiding this study is: Which database should Trifold Investment Group use based on the relationship between customers’ profit and turnaround time on stock data with different databases? To answer this, two variables are examined: the database used (Invest.Live vs. Agevest) and customer profit. The hypotheses are formulated as follows:

• Null hypothesis (H₀): There is no difference in mean profits between customers using Invest.Live and those using Agevest.
• Alternative hypothesis (H_A): Customers using Invest.Live have a higher mean profit than those using Agevest.

Similarly, for the advice effectiveness:

• H₀: There is no difference in mean profits for customers receiving useful versus obsolete advice.
• H_A: Customers receiving useful advice have higher mean profits than those receiving obsolete advice.

Statistical Methodology and Results

Given the comparison between two independent groups, the two-sample independent t-test is employed to assess the hypotheses at a 95% confidence level (α = 0.05). The test results indicate p-values greater than 0.05: specifically, 0.08 for the database comparison and 0.61 for the advice comparison. Since both p-values exceed the significance level, there is insufficient evidence to reject the null hypotheses. Consequently, the analysis suggests that there is no statistically significant difference in mean profits between the databases used or between advice types, based solely on the sample data.

Furthermore, regression analyses reveal that for each customer using Invest.Live, profits increase by approximately 4.54 units, and customers receiving useful advice see an increase of about 8.73 units. These coefficients indicate positive relationships; however, the significance of these coefficients depends on further statistical testing, which should confirm their relevance.

Discussion and Implications

The findings indicate that, based on the sampled data, neither the choice of database nor the type of advice received results in statistically significant differences in customer profits. This suggests that other factors may influence profitability, and further research could explore additional variables or consider larger sample sizes to enhance statistical power. The practical recommendation for Trifold Investment Group is to consider other operational aspects beyond the current database and advice structures, such as personalized strategies or market analysis sophistication, to improve profitability outcomes.

In conclusion, selecting the appropriate inferential statistical test is crucial to deriving accurate insights. Proper formulation of hypotheses and correct interpretation of p-values are fundamental to sound decision-making. Although the current analysis does not find significant differences, it underscores the importance of rigorous statistical planning and the need for ongoing data collection for more definitive conclusions.

References

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