Description

BUS352

Operations Analytics

Tutor-Marked Assignment

Analyse the chat entry number and academic performance data. Provide a visualisation and a numerical description of the two variables according to the following requirements:

Select one (1) appropriate chart type to visualise and present data. Provide a brief justification of your choice. What observations and conclusions can you make from the data visualisation?

Present descriptive statistics of the two variables. Provide a brief explanation of the key statistics. What can you observe and conclude from the descriptive statistics? Are these observations and conclusions consistent with those from (a)(i)?

• Dr. Goh hypothesises that chat participation strengthens students’ learning experiences and eventually enhances their academic performance. Dr. Goh wanted to find out if this hypothesis can be supported by the collected data. You are tasked to perform an association analysis according to the following requirements.

• Use one (1) appropriate chart type to examine the relationship between the two variables. Provide a brief justification of your choice. What can you observe and conclude from the chart?

• Use one (1) appropriate statistical tool to examine the relationship between the two variables. Clearly present the steps and components of the tool (if any), as well as the result of analysis. What can be concluded from the analysis? Is this conclusion consistent with the observations and conclusions in (b)(i)?

In statistical analysis, the validity of the analysis can be adversely affected by skewness. Log transformation may help boost the validity by reducing or removing the skewness of the original data. Create a new variable by taking the natural logarithm of the chat entry data. For example, if the original data point is 13 entries, then the natural logarithm of this data point would be ln(13) = 2.56. You can use Excel function “LN()” to perform this transformation. Repeat (a)(i), (a)(ii), (b)(i) and b(ii) for this newly created variable and the grade data. Does this analysis yield any different conclusions or findings, compared to (a) and (b)? Please show the details of charting, descriptive statistics, calculation, analysis, and reasoning.

Every data analytics project has its limitations. What can be the limitations presented in this project and the improvements that Dr. Goh can make to enhance the validity, plausibility and robustness of the analysis and results? Provide a thorough discussion and clearly present your arguments. You may comment on the design of the data analytics project (e.g., data collection), hypothesis, rationale, tools, and methodologies used in the analytics, etc. (Word Count: Maximum 600 words)