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  • Final Project

    Congratulations on reaching the last step of your Final Project! The Final Project is a stepwise process, and its goal is to leave you well-prepared by the end of this course to execute on your qualitative research study plan. By now, you have completed the steps of this course that are good practice for developing a high-quality qualitative research study.

    The Final Project: Building a Qualitative Research Plan document in the Learning Resources contain the instructions for organizing the work you produced in Steps I through V. Be sure to incorporate revisions to your work based on feedback from your Instructor and classmates.

    Below is the appropriate heading that are needed.

    Title Page (heading not required)

    Introduction (add introductory paragraph)

    Problem Statement (in-text citations required to support that there is an issue)

    Purpose Statement (in-text citations required to support that the purpose aligns with the problem)

    Literature Review (this is a synthesis of your sources that were used to identify the problem, indicated the need for the research, indicated the gap in the current research, and discussed and justified the chosen framework)

    Annotated Bibliography

    NOTE

    • 10+ scholarly articles related to your research topic
    • Same guidelines used in your Intro to Qual course in a prior semester
    • Do not copy and paste the article abstract
    • This is not a references list but is a critical assessment of each article.

    Summary of the article (not the abstract)

    Relevance to your study

    Credibility of the Author(s) and Publisher

    • Include the three subheadings listed above and repeat them for each article)

    Framework (theoretical or conceptual, detailed) (in-text citations required to support alignment of the framework to the research problem)

    Research Questions (keep alert to the use of qualitative phrasing)

    Research Design (include introductory paragraph) (all below subheadings must contain in-text citations that align with the best practices of the chosen methodology)

    Methodology (indicate and justify the specific qualitative approach, e.g. phenomenology, case study, narrative, etc)

    Justification

    Researcher Role

    Participant Selection (do not use your two class participants – indicate the ideal participants instead but use your class transcripts for the coding)

    Target Population (identify using exclusion and inclusion criteria for your ideal participants)

    Sampling Strategy (how will participants from the above target population be selected – do not use the class process, indicate how you would do it for the dissertation)

    Sample Size (justify according to chosen method approach) (e.g. if phenomenology, indicate acceptable sample size according to best practices for phenomenology)

    Participant Recruitment (indicate the ideal process for recruiting participants for your dissertation study; do not use recruitment for the class interviews)

    Invitation Protocol

    Instrumentation (in what ways and what tools, materials, and/or assessments will be used to attract, verify, and assess potential participants)

    Interview Guide Overview (submit final interview guide as an appendix to this document) (Keep alert to interview question qualitative phrasing, especially focusing on open-ended questions) (label it Appendix A) (Subsequent appendices B, C, etc, if any, can also be added. This would be for longer content, such as extensive code and category lists, charts, etc)

    Rigor and Credibility

    Other Data Sources

    Data Collection (do not refer to the class process. Instead, create the ideal data collection process as if you collected data for your dissertation)

    Where collected

    How Collected

    Duration

    Data Recording

    Debriefing (debriefing of the research design by peers; use classmate debriefing of your collection and analysis materials)

    Ethical Procedures

    Data Analysis (compile and aggregate all data sources (interviews, videos, Walden Social Change website, memos, journaling/field notes, observations, etc as part of the analysis, not just the two class interviews)

    Summarize Analysis Plan

    Manual or Software Coding (describe the options, indicate the chosen option, and justify its choice for your chosen study)

    Indicate Codes and Categories (include quoted content to support choices of codes and categories)

    Codes (list each code in a table and add representative quotes from the data sources for each code)

    Categories (create various code groupings in a table that have similar meanings and then create a representative category name for each grouping, e.g. Codes belongingness, comfortable, and caring could be grouped under a category name of ‘sense of community’) (justify the choice of category label with quoted content from the sources)

    Trustworthiness (of your project) ((summary paragraph that introduces the below items) for each of the below, describe the item and then demonstrate how your ideal project could meet or not meet the definition. Use in-text citations)

    Credibility

    Transferability

    Dependability

    Confirmability

    Social Change Alignment (a short essay in which you describe the connection between your project and social change)

    Major Learning Points

    Dissertation Next Steps

    Course Reflection

    References

    I have attached all assignments that have lead up to this one. Please go through and add/fix what needs to be by following the headings above. In the file will have the feedback under each assignment that was completed if there was any feedback. The topic needs to be followed. If you have any questions please reach out.

    Attached Files (PDF/DOCX): Final Project info.docx

    Note: Content extraction from these files is restricted, please review them manually.

  • Discussion post 2 – global Logitics

    see attachments let me know if more info is needed

  • Opioids Overdose Death in New York City

    the instructions have been uploaded also I have attached two articles I will be using for this paper

    Attached Files (PDF/DOCX): Public_Health_Detailing-A_Succ.pdf, Effect_of_the_Communities_That (1).pdf

    Note: Content extraction from these files is restricted, please review them manually.

  • Case analysis 2 – energy transformation

    Watch this TED Talk, titled “Let’s transform energy with natural gas” by T. Boone Pickens. Read the two articles at the link below: Summarize the video and the three articles Relate to Chapters 9, 11, and 12 and then discuss whether Natural Gas could be the next major energy source in the coming 20, 30 or 50 years. Expected Length: 1.5-2 pages.

  • Module 2: Weekly Written Assignment

    You can use the attached .docx file below to include all answers.

    Question. Conduct your own GAPS analysis.

    Please see the attached .doc file for the below questions. You can use the attached document file to complete both questions below.

    1. Conduct a GAPS analysis for yourself (see textbook pp. 91-95)

    • Goal: Clearly identify what you want to do or where you want to go with your career over the next year or so.
    • Abilities: What are your strengths to help you succeed in your current/previous jobs?
    • Perceptions: Feedback from others. What are others reactions to your strengths and your development needs?
    • Standards: What does your boss (or your organization) expect for your career objective?

    2. Identify gaps in your GAPS analysis.

    • What are your biggest development needs?
    • How should these development needs be prioritized?

    Format: Use the attached document to answer fully – all four sections on page 1 and what to do to remove gaps on page 2. The expected length is approximately 350400 words total. Substantially shorter submissions will lose points under the personal insight criteria. No citation required unless the student did deep-level research on the next steps for his/her career.

    Please use this time for your own benefit. Before you start, please think about your career goal first. A detailed career goal will help you work on this assignment.

  • What is Macro Economics?

    A macro is an automated set of instructions or commands that performs a series of tasks in a program or system with minimal user input. It is designed to simplify repetitive, time-consuming, or complex processes by executing multiple actions at once through a single trigger, such as a button, shortcut key, or command.

    In professional or technical contexts, macros are commonly used to increase efficiency, accuracy, and productivity, especially in software like Microsoft Excel, Word, programming environments, and data processing tools.

    Example:
    In Excel, a macro can automatically format a report, calculate totals, and organize data with one click instead of doing each step manually.

    Requirements:

  • Touchstone 6

    A. Analysis of TechGear Inc.

    Step 1: Read the Scenario

    SCENARIO: As a data analyst at TechGear Inc., a company specializing in electronic gadgets and accessories, your task is to analyze historical sales data, build predictive models, and use prescriptive analytical methods to provide actionable insights for improving decision-making. The company has been experiencing fluctuating sales and aims to optimize its marketing strategies and production processes to maximize profits and enhance customer satisfaction. Your analysis will help TechGear Inc. understand the factors influencing its sales, forecast future sales trends, assess financial risks associated with different business scenarios, and determine the optimal allocation of its marketing budget and production resources. Ultimately, your work will enable the company to make data-driven decisions, enhancing its sales and marketing strategies, and leading to improved profitability and customer satisfaction.

    Step 2: Look Over the Data

    • Questions 1-5 (Linear Regression) and 7 (Machine Learning): Use the data in the techgear_sales_data.xlsx Excel file, which can be found at the following GitHub link:
    • Question 6 (Forecasting): Use the data in the techgear_sales_data_monthly.xlsx Excel file, which is available at this GitHub link:
    • This file contains the same data as techgear_sales_data.xlsx, but the last row only includes a date with missing values for all other columns. These missing values are intended for you to apply forecasting methods for the upcoming time period.
    • Questions 8 and 9: Since Question 8 focuses on Monte Carlo simulations and Question 9 focuses on linear programming, all necessary data is provided in the problem statement.

    This dataset contains monthly sales and advertising spend data for TechGear from January 2020 to December 2024. It includes the following columns:

    Column NameDescriptionUnit/FormatDateThe month and year for each data entryMM/DD/YYYYSalesThe total sales generated in that monthNumber of SalesAd_Spend_FacebookThe amount of money spent on Facebook advertising in that monthDollarsAd_Spend_InstagramThe amount of money spent on Instagram advertising in that monthDollarsDiscount_RateThe discount rate applied to sales in that monthPercentage

    A snapshot of the first few rows of the dataset is provided below:

    Step 3: Read TechGear Inc. Questions

    Question 1: Exploring Data Structures and Averages in Advertising Spend and Discounts

    Before conducting an analysis, use Python to create a pandas DataFrame named sales from the dataset.

    • What key features of the dataset can you summarize, such as the number of rows and columns?
    • What is the average amount spent on advertising for each social media platform (Facebook and Instagram)?
    • What is the average discount provided to customers?
    • What insights can you draw from this summary regarding advertising spend and discount trends?

    Question 2: Visualizing Relationships

    • How can you visualize the relationships between sales and each advertising spend variable (Facebook and Instagram) as well as discount rates?
    • What types of plots (e.g., scatter plots, line plots, or histograms) would be most effective in identifying patterns or correlations between these variables?
    • What do these visualizations reveal about the impact of advertising spend and discount rates on sales?

    Question 3: Simple Linear RegressionTechGear wants to optimize its marketing strategy.

    • How can you develop a simple linear regression model in Python to predict sales based on Facebook ad spend?
    • What do the coefficients of the model indicate?
    • Specifically, how does the slope describe the relationship between Facebook ad spend and sales?
    • What does the R2 value tell you about how well the model explains the variability in sales?
    • How does the regression output from Python support your interpretation of the models performance?

    Question 4: Assessing the Fit of the Simple Linear Regression Model

    • How can you evaluate the performance of your simple linear regression model by analyzing residuals?
    • What insights do residual plots provide about the models accuracy?
    • Do they suggest any patterns, heteroscedasticity, or violations of linear regression assumptions?
    • How might these findings impact the reliability of the models predictions?

    Question 5: Multiple Linear Regression ModelThe simple linear regression model provides insights into Facebook ad spend.

    • How can you develop a multiple linear regression model to predict monthly sales using Facebook ad spend, Instagram ad spend, and discount rates?
    • How do the coefficients of this model compare to the simple linear regression model? What do they reveal about the combined influence of these factors on sales?
    • Which model performs better in predicting sales?
    • How can you compare the effectiveness using statistical metrics (such as R2 and RMSE)?
    • Based on this comparison, what recommendations can you provide to TechGear for optimizing its advertising strategy?

    Question 6: ForecastingUsing historical sales data, how can you construct:

    • A 3-month moving average forecast for January 2025?
    • An exponential smoothing forecast with a smoothing parameter of 0.80 for January 2025?

    Given TechGears preference for emphasizing recent sales trends:

    • Which forecasting method provides the most reliable prediction for January 2025?
    • What key differences exist between the two forecasting methods, and what do they imply for forecasting accuracy?

    Based on your analysis, consider:

    • What actionable recommendations can you provide to TechGear to improve its marketing strategies and production planning?

    Question 7: TechGear needs a reliable model to predict future sales.

    • How can you build and compare different predictive models to achieve this?
    • How can you develop a multiple linear regression model using 5-fold cross-validation to predict future sales?
    • How can you develop a decision tree model using 5-fold cross-validation to predict future sales?
    • How do the two models compare in terms of RMSE, and which model should TechGear choose?

    TechGear requires a minimum of $6,500 in sales each month to remain profitable.

    • If the best model predicts sales of $4,200, how can the RMSE value be used to determine the range within which actual sales may fall?
    • What are the implications of this for decision-making and risk assessment?

    Question 8: Monte Carlo SimulationsTechGear has experienced significant fluctuations in sales, making accurate predictions challenging.

    • How can you use Monte Carlo simulations to estimate future sales?
    • How can you estimate the average and median monthly sales by running 1,000 simulations?
    • What visuals (e.g., histograms or box plots) can you generate to summarize the results?
    • If daily sales are assumed to follow a uniform distribution between the minimum and maximum observed sales over the past 60 months, how does this impact the simulation results? You can assume that the value for minimum sales observed over 60 months is 2,299 and the maximum value is 7,702.
    • How can you interpret the standard deviation of simulated sales, and what does it reveal about TechGears sales variability?
    • How can TechGear use these insights to improve budgeting, sales forecasting, and operational decision-making?

    Question 9: Linear Programing

    TechGear wants to optimize its advertising spend across Facebook and Instagram to maximize its monthly sales. They have a fixed advertising budget and need to determine the optimal allocation of this budget to achieve the highest possible sales. The sales generated from advertising on each platform are influenced by the amount spent on that platform.

    TechGear has a monthly advertising budget of $10,000. The estimated sales generated from advertising on Facebook and Instagram are given by the following linear equations:

    • Sales from Facebook advertising: where F is the amount spent on Facebook advertising
    • Sales from Instagram advertising: where I is the amount spent on Instagram advertising

    TechGear has a monthly advertising budget of $10,000. They must spend at least $2,000 on Facebook advertising to maintain its presence on the platform. Additionally, they must spend a minimum of $1,000 and no more than $7,000 on Instagram advertising due to platform-specific constraints. The amount spent on Instagram advertising should be at least 50% of the amount spent on Facebook advertising to ensure balanced marketing efforts.

    • What is the optimal budget allocation for Facebook and Instagram, and what is the maximum sales revenue TechGear can achieve under these conditions?

    Step 4: Using the PowerPoint Template, Analyze Data for TechGear Inc.

    • Your task is to analyze historical sales data for TechGear Inc. using various analytical techniques.
    • Youll apply concepts from linear regression, forecasting, machine learning, and prescriptive analytics.
    • The goal is to provide actionable insights to help TechGear make data-driven decisions.
    • Include Python code snippets in your slides for data exploration, regression models, forecasting, machine learning, Monte Carlo simulation, and linear programming tasks.
    • Your Python code should be accurate and well-documented to demonstrate how each analysis step was performed.
    • Your findings will be presented in a PowerPoint presentation, with speaker notes explaining your approach and insights.

    Review each question and then follow the directions outlined on each slide to summarize and present your findings for each question.

    Step 5: Review the Grading Rubric to Ensure All Criteria are Met

    Review the rubric to ensure that you understand how you will be evaluated. Also review the requirements to ensure that your Touchstone is complete.

    Step 6: Submit Your Touchstone

    Submit your completed Touchstone (as a .pptx file) using the blue button at the top of this page.

    B. Rubric

    Advanced (100%)Proficient (85%)Acceptable (75%)Needs Improvement (50%)Non-Performance (0%)Python Analysis (Shown at Key Steps)

    The inclusion of well-documented, accurate Python code for data exploration, regression models, forecasting, machine learning, Monte Carlo simulation, and linear programming. (5%)

    Python code is shown for all major steps, including data exploration, visualization, regression models, forecasting, machine learning, Monte Carlo simulation, and linear programming. Code is well-documented and accurate.Python code is shown for most key steps. Minor issues with code documentation or accuracy.Python code is shown for some steps, but critical components are missing or incomplete.Python code is partially shown but lacks key analyses or is significantly incorrect.No Python code is provided.Data Exploration and Summary (Slide 2)

    Clear summary of data structure, accurate calculation of averages, and key insights from data exploration. Python analysis is included and well-integrated. (10%)

    There is a comprehensive summary of data structure with accurate calculation of averages and clear insights from the exploration. Python analysis is included and well-integrated.Data summary is mostly accurate, with minor errors or missing insights. Python analysis is included.Basic summary provided, but some key features are missing or inaccurate. Python analysis is incomplete.Minimal data exploration with several inaccuracies and no significant insights. Python analysis is missing or incorrect.No data exploration is provided.Visualizing Relationships (Slide 3)

    Accurate and clear visualizations showing relationships between sales, ad spend, and discount rate. Proper interpretation of patterns and correlations. (10%)

    Clear and accurate visualizations for all specified variables with detailed insights into patterns and correlations. Python-generated plots are used.Visualizations are mostly accurate and provide useful insights. Minor errors in interpretation or plot generation.Basic visualizations are provided, but significant patterns or correlations are overlooked. Python plots are incomplete.Visualizations are unclear or inaccurate with limited analysis. Missing Python plots.No visualizations are provided.Simple Linear Regression & Model Fit (Slides 4 & 5)

    Well-implemented regression model with correct interpretation of coefficients and R2 value. Assessment of model fit through residual analysis. (10%)

    Accurate regression model with clear interpretation of coefficients and R2 value. Residual plots are well-explained, and the fit is thoroughly assessed. Python output included.Regression model and assessment are mostly accurate, with minor errors or incomplete explanations.Basic model output provided, but interpretations and model fit assessments are incomplete or contain errors.Model is poorly developed, with incorrect interpretations and no reliable assessment of fit.No regression model or assessment is provided.Multiple Linear Regression (Slide 6)

    Complete multiple regression analysis, including variable interpretation and comparison to simple regression. Python output included. (10%)

    Complete and accurate multiple linear regression analysis, with well-explained coefficients and comparison to the simple linear regression model. Python output included.Multiple regression analysis is mostly accurate, with minor errors or incomplete comparisons.Basic multiple regression is provided, but interpretations and comparisons are incomplete or partially inaccurate.Incomplete or incorrect multiple regression model with minimal explanation.No multiple regression model is provided.Forecasting (Slide 7)

    Implementation of both forecasting methods, clear comparison, and justified selection of the best method based on business needs. (10%)

    Both forecasting methods are accurately implemented and compared. The recommendation is well-justified and aligned with TechGears preferences. Python output included.Forecasting analysis is mostly accurate, with minor errors or incomplete justification of the chosen method.Basic forecasting analysis is provided, but one method may be missing, or justification is unclear.Minimal forecasting analysis with significant errors and no clear recommendation.No forecasting analysis is provided.Machine Learning Models (Slide 8)

    Accurate implementation of multiple regression and decision tree models with RMSE comparison and well-supported model selection. (10%)

    Both models are accurately built and compared using RMSE. Clear model recommendation with actionable insights. Python output included.Machine learning analysis is mostly accurate, with minor errors in the comparison or recommendation.Basic models are provided, but the comparison and recommendation are incomplete or unclear.Models are incomplete or contain major errors. Limited or no comparison is provided.No machine learning analysis is provided.Monte Carlo Simulations (Slide 9)

    Simulation correctly executed with proper assumptions, visualizations, and interpretation of results. Actionable insights are provided. (10%)

    Simulation is well-executed with clear visualizations and interpretation of results. Actionable insights are provided. Python output included.Simulation is mostly accurate, with minor errors or incomplete insights.Basic simulation is provided, but interpretation is incomplete or unclear.Simulation is incomplete or incorrect with minimal explanation.No simulation is provided.Linear Programing (Slide 10)

    Accurate optimization model that meets constraints and clearly explains the best budget allocation for maximum sales. (10%)

    Linear programming solution is accurate and fully meets all constraints. Clear explanation of the optimal budget allocation and maximum achievable sales. Python output included.Solution is mostly accurate, with minor errors in constraints or explanation.Basic linear programming solution is provided but contains errors or incomplete explanations.Incomplete or incorrect solution with minimal explanation.No linear programming solution is provided.Presentation Quality & Speaker Notes

    Well-organized slides with readable formatting and professional layout. Speaker notes effectively explain analysis and insights. (15%)

    Slides are visually appealing and well-organized, with clear speaker notes that thoroughly explain the analysis and findings.Slides are mostly clear and organized. Speaker notes are informative but may lack detail.Basic slides with limited visual appeal. Speaker notes are incomplete or too brief.Poorly organized slides with missing or unclear speaker notes.No presentation or speaker notes provided.

    C. Requirements

    The following requirements must be met for your submission:

    • Hand in a .pptx file with slides listed above.
    • Use a readable 11- or 12-point font.
    • All writing must be appropriate for an academic context. Follow academic writing conventions (correct grammar, spelling, punctuation, and formatting).
    • Plagiarism of any kind is strictly prohibited.
    • Submission must include your name and the date (included in the template).

    This assignment provides a practical experience in business analytics, honing skills essential for data-driven decision-making in business environments. Your analysis and recommendations will help TechGear optimize its operations,

    Good luck, and enjoy uncovering insights for TechGear!

  • Lab report

    Using the lab data, please answer questions one through 12. Using the data make an excel graph for #10. Please attach graph as separate document. The questions you answered do not have to be long, just one or two sentences simplified. Also write a lab report using this data. Lab Report (separate document) Part I: a concise discussion on precision and accuracy using the data you collected. Remember that you need multiple data points to discuss precision and you need the true value of the data you collected regarding accuracy. Part Il: a concise decision on possible resources of experimental error. Remember that calculation errors are not experimental errors. – Use passive language when writing your report. Do Not use (1, We, our)

  • Module 1: Weekly Written Assignment

    ou have to complete ALL questions for the Module 1 Weekly Memo.

    Question. Apply the Interactional framework of leadership to yourself.

    1. Do you have any experience as a leader? How can you describe yourself as a leader? If you were not a leader yet, how was your leader? Describe his/her individual characteristics. Were you (or was s/he) an effective leader?

    • Example: School organizations, your workplace, community groups, etc.

    2. When you were a follower, how did you work with your leader? What type of follower were you?

    3. Was there any problem/issue in your organization?

    • What kind of situation was it?
    • Were you (or was s/he) able to handle the situation? If so, how?

    Note: This should be based on your own experience. Try to write your experience for all three questions, including all sub-questions.

    Format & Submission Instructions

    No cover page. No abstract. Do not type the full questions in your submission, as plagiarism detection software may flag them. Only include the question number(s) before each response.

    Formatting: Double-spaced, 12-point font, 1-inch margins, APA style for citations and references when sources are used.

    APA style guide :https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/index.html

    Length Guidelines: The expected length is approximately 350400 words total. Slightly longer submissions may earn additional credit only when they demonstrate exceptional insight, reflection, or application of course concepts. Submissions that are substantially shorter than the guidelines may lose points under the application and personal insightcriteria.

    Plagiarism & AI Use: A plagiarism score of 30% or higher will receive zero credit for the initial submission and require major revisions for credit. An AI score of 30% or higher will also receive zero credit and require resubmission. Grammar AI tools (e.g., Grammarly) are permitted. If you use a grammar AI tool, you must include the required authenticity acknowledgment. For example, “I pledge that all ideas and content in this assignment are my own. I have used (put the tool name that you used) solely for grammar-checking purposes to enhance the clarity and accuracy of my writing.”

  • How to solve the volume of sphere of radius 7cm

    How to solve the volume of sphere of radius 7cm

    Requirements: