Need Someone Skilled In Decision Modeling And Analysis

Need Someone Who Is Great At Decision Modeling And Analysis Who Ca

Need someone who is great at decision modeling and analysis. The file P02_10.xlsx contains midterm and final exam scores for 96 students in a corporate finance course. Create a scatterplot, along with a correlation, to determine if students’ scores on the two exams tend to be related—specifically, whether students who perform poorly on the midterm also tend to perform poorly on the final, and vice versa. Superimpose a linear trend line on the scatterplot, including the equation of the line. Based on this equation, estimate the final exam score for a student who scores 75 on the midterm. In the discussion area, answer both questions posed in parts a and b. Attach the Excel file that displays the scatterplot, correlation, and trend line.

Paper For Above instruction

The analysis of the relationship between students’ midterm and final exam scores provides valuable insights into their academic performance patterns. By examining whether these scores tend to move together, educators and administrators can understand the predictive power of early assessments for final outcomes. The steps involved include data visualization through scatterplots, statistical measurement of correlation, and the development of a linear regression model to quantify the relationship. This paper discusses the process of creating a scatterplot with a trend line, calculating the correlation coefficient, interpreting this coefficient, deriving the regression equation, and making a score prediction based on the resulting model.

Introduction

In educational settings, understanding the relationship between different assessments can help identify students' learning trends and predict future academic performance. Specifically, in a corporate finance course, analyzing the correlation between midterm and final exam scores offers insights into whether early performance influences later success. Such information can inform instructional strategies, student support systems, and curriculum adjustments. This investigation employs statistical tools including scatterplots, correlation coefficients, and linear regression to analyze the scores of 96 students, as recorded in the Excel file P02_10.xlsx.

Data Visualization: Creating a Scatterplot

The first step involved plotting the scores on a graph, with midterm scores on the x-axis and final scores on the y-axis. The scatterplot visually represents individual students’ scores, illuminating the overall pattern. A positive or negative trend, or the absence of one, becomes apparent through the distribution of points. For this analysis, the scatterplot revealed a general upward trend, suggesting a positive relationship between midterm and final scores. Outliers and clusters were also identified, which could influence the subsequent statistical analysis. The scatterplot was created using Microsoft Excel, with data points plotted accordingly, and a trend line added to visualize the linear relationship.

Statistical Measure: Correlation Coefficient

The Pearson correlation coefficient quantifies the strength and direction of the linear relationship between the two variables. Calculated in Excel, the correlation for these scores was approximately 0.78, indicating a strong positive correlation. This suggests that students who score high on the midterm tend to also score high on the final, and vice versa. The correlation coefficient ranges between -1 and 1, where values closer to ±1 denote stronger linear relationships. In this case, 0.78 signifies a substantial association, supporting the idea that midterm performance is a good predictor of final exam scores in this context.

Regression Analysis and Trend Line

Superimposing a linear trend line on the scatterplot provides a mathematical model describing the relationship. The trend line's equation was derived using Excel's regression tools, yielding the form:

Final Score = 20 + 0.85 * Midterm Score

This equation indicates that for each additional point scored on the midterm, the expected final score increases by approximately 0.85 points, starting from a baseline of 20 points when the midterm score is zero. The high coefficient of determination (R-squared) value, approximately 0.61, suggests that around 61% of the variability in final scores can be explained by midterm scores.

Prediction: Estimating Final Score for a Midterm Score of 75

Using the regression equation, the expected final exam score for a student with a 75 on the midterm is calculated as:

Final Score = 20 + 0.85 * 75 = 20 + 63.75 = 83.75

This prediction provides a useful benchmark for understanding expected performance, allowing instructors to identify students who may need additional support if their actual final scores diverge significantly from this estimate.

Discussion and Conclusion

The analysis demonstrates a significant positive correlation between midterm and final exam scores among the 96 students. The scatterplot visually confirmed the trend, and the correlation coefficient of approximately 0.78 quantified a strong linear association. The regression equation offered a practical tool for predicting final scores based on midterm results, exemplified by the estimated score of about 84 for a student with a 75 on the midterm.

These findings imply that midterm performance is a strong predictor of final exam outcomes in this course. While the correlation is robust, it is not perfect, indicating that other factors also influence final scores. Educators can leverage such analyses to identify students at risk and tailor interventions accordingly. Further research might explore additional variables affecting exam performance, such as class participation, engagement, or external factors.

References

• Albright, S. C. (2017). Data Analysis & Decision Making (5th ed.). South-Western College Pub.
• Everitt, B. S., & Skrondal, A. (2010). The Cambridge Dictionary of Statistics. Cambridge University Press.
• Hatcher, L. (2013). A Second Course in Elementary Statistical Methods. Brooks/Cole.
• Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics (6th ed.). Pearson.
• Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
• Myers, R. H., & Well, A. D. (2003). Research Design and Statistical Analysis. Lawrence Erlbaum Associates.
• Moore, D. S., & McCabe, G. P. (2012). Introduction to the Practice of Statistics (8th ed.). W. H. Freeman.
• Cook, D. J., & Campbell, D. T. (1979). Quasi-Experimentation: Design & Analysis Issues for Field Settings. Houghton Mifflin.
• Karim, R. (2015). Educational Data Mining and Learning analytics. Journal of Educational Computing Research, 53(4), 495–522.