Bacteria Was Put In A Petri Dish With Antibacterial Soap

Bacteria Was Put In A Petri Dish With Antibacterial Soap Determine Th

Bacteria was put in a petri dish with antibacterial soap. Determine the logarithmic regression of the bacteria. Use either a calculator or spreadsheet program. f(x)=253.8−75.94ln(x) f(x)=253.8+75.94ln(x) f(x)=−253.8+75.94ln(x) f(x)=253.8−98.2ln(x) · 1 POINT Using the data below, find the exponential regression relating the value of a car over 10 years using either a calculator or spreadsheet program. f(x)=88.5·0.797x f(x)=88.5·0.797x f(x)=−88.5·0.797x f(x)=88.5·(−0.797)x QUESTION 3 · Determine the exponential regression of the data below using either a calculator or spreadsheet program. (60,5),(61,14),(62,23),(63,49),(64,67),(65,78) Select the correct answer below: f(x)=−3.6×10−14·0.17x f(x)=0.036·1.7x f(x)=3.6×10−14·1.7x f(x)=−3.6×10−14·1.7x CSE 578: Data Visualization Individual Contribution Report This is Milestone 4 of your course project. You will write a 2-3 page report detailing your individual contribution to your team project. Directions Your report should include the following: 1. Reflection: What was your overall role in the team development process? What did you specifically work on and contribute to? 2. Lessons Learned: What wisdom would you share with others regarding design methods and how best to apply them, and/or suggested "design practices" to keep in mind for future design projects? 3. Assessment/Grading: Was an honest effort made to learn from experience and to identify how the lessons learned extend beyond this project? 4. Future Application: What skills have you learned in this course that you will apply in the future in other MCS courses, or in the workplace? Submission Directions for Checkpoint Deliverables Upload your Individual Contribution Report as a file to the submission space in the wrap-up section of the week it is due. This is an individual submission. Grading Criteria 0 1 2 Reflection There is no reflection included. The reflection attempts to demonstrate thinking about learning but is vague and/or unclear about the personal learning process. The reflection explains the student’s own thinking and learning processes, as well as implications for future learning. 1 Lessons Learned No lessons were learned about the design methods or visualizations used in this project. Some lessons were learned about the design methods or visualizations used in this project, but they are poorly defined or lack understanding of application. Lessons were learned about the design methods and visualizations used in this project, and they are clearly defined and demonstrate understanding of application. 2

Paper For Above instruction

This paper aims to address and analyze the various mathematical modeling and data visualization tasks presented in the assignment instructions, as well as reflect on individual contributions within a team project context. The assignment includes conducting logarithmic and exponential regressions based on hypothetical biological and economic data, as well as preparing a personal contribution report reflecting on team dynamics, lessons learned, and future applications of acquired skills.

Analysis of Bacterial Growth and Logarithmic Regression

The initial task involves analyzing bacterial growth data after exposure to antibacterial soap. The key goal is to determine the appropriate logarithmic regression model representing bacterial reduction over time or dosage. Given the options provided, the most suitable model to describe bacterial decline typically involves a logarithmic function reflecting decreasing bacterial counts with increased exposure to antibacterial agents.

Among the options, the regression model f(x) = 253.8 - 75.94 ln(x) appears appropriate, as it suggests a decreasing bacterial count with logarithmic dependence. The additional options, such as positive or different constants, are less aligned with typical bacterial reduction models, where a negative coefficient on the logarithmic term indicates a decline.

To derive the actual regression, one would input observational data points—such as bacterial colony counts at various soap concentrations or time intervals—into a calculator or spreadsheet, then perform logarithmic regression analysis. Using software like Excel, the Regression tool or the LOGEST function can facilitate this process to determine coefficients that best fit observed data, thus confirming the selected model.

Exponential Regression for Car Value Depreciation

The second modeling task involves understanding the depreciation of a car over ten years using exponential regression. The suggested functional form, f(x) = 88.5 · 0.797^x, indicates a model where the initial value diminishes exponentially over time, consistent with typical depreciation patterns.

In practice, data points representing car value at each year are entered into a calculator or spreadsheet, then exponential regression analysis is performed, often by fitting the model y = a · r^x. Here, a value of 88.5 may represent the initial value, and r = 0.797 signifies the yearly depreciation rate. The choice of an exponential decay model makes sense in economic contexts, where assets depreciate at a percentage rate annually.

The regression process involves logarithmic transformation of the data or direct application of exponential fitting functions in software like Excel, R, or Python, which minimize residual errors and provide coefficients that best describe depreciation. The selected model effectively predicts future values and aids decision-making regarding asset management.

Regression Analysis of a Data Set

The third task involves fitting an exponential regression model to a set of data points: (60,5), (61,14), (62,23), (63,49), (64,67), and (65,78). Analysis of this data reveals rapid increases, implying an exponential growth trend.

Among the options, f(x) = 3.6×10⁻¹⁴ · 1.7^x effectively captures the growth pattern, with the base 1.7 indicating exponential increase over x - the time or sequence index. The extremely small coefficient (on the order of 10⁻¹⁴) suggests normalization or scaling factors to fit the data appropriately.

Using suitable software, one can perform exponential regression analysis to derive parameters that align with observed data, revealing insights about underlying growth processes—potentially biological proliferation or other exponential phenomena.

Reflections on Team Contribution and Lessons Learned

The final component involves preparing a personal contribution report reflecting on individual roles, emphasis on lessons learned about data visualization and design practices, and outlining future applications of course skills. This task underscores the importance of self-awareness, effective communication, and applying analytical skills learned in MCS coursework to real-world situations.

Effective team contributions typically involve clear communication, collaboration, and leveraging individual strengths to achieve collective goals. Reflecting on these processes enhances understanding of project management and technical skills, such as data analysis, regression modeling, and visualization, which are crucial in academic and professional contexts.

Lessons learned often include the significance of selecting appropriate models based on data patterns, understanding the assumptions underlying various regressions, and the utility of software tools in simplifying complex calculations. Future applications involve using these modeling techniques in other coursework, research projects, or workplace decision-making scenarios, emphasizing the transferability of statistical and analytical skills.

In conclusion, this assignment integrates practical data analysis with personal reflection, fostering a comprehensive understanding of statistical modeling, visualization, and collaborative project work, which are essential competencies in data science and analytics fields.

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