Discussion Topic: Project Outline And Themes Personal Assign

Discussion Topic Project Outline And Themes Personal Assignmentsproje

As you may read in the following lines to this announcement you will find an outline for the activities and analysis you need to complete in order to receive the credit for the course project in the moments of the term that are indicated. Also your assignment is followed by your last name in bold.

PROJECT OUTLINE This is an outline to guide students in the preparation of the STA2023 Course Project Base Learning Themes by Under-Graduate Research Rationale: The project is a term paper oriented in week II, controlled by the end of each course module and completed in week before to the final week of the term and shows the integrative result of research by collecting, organizing, displaying and understanding data.

The project has partial checks in each module and an expected commitment to produce a presentation during a scientific event, with concentration in the descriptive statistics of the data collected, which may be a partial presentation of the project at certain point of its execution. The work includes a final technical report in writing about the characteristics of data in both, the descriptive and the inferential procedures, as the description of the methods necessary to provide evidence and how the models inference approach the data collected, and therefore producing inferences for the variable in the targeted population, using quantitative and qualitative facts via the exploratory data and probability distribution function analysis method.

The Topics for the projects are selected based on the professional interests of the state of the art in research. The Report is an academic term paper that is structured in Title; Author; Abstract; Introduction (following the previous explanations in above paragraphs); methods, results, conclusions, and bibliographic references.

PROCEDURE: Initial interview: This activity is conducted over the Module 1 Week II ( VIII mini ) and includes: a) Theme definition b) Preliminary search for main information to validate the theme, using Microsoft copilot. This is what is evaluated as Project submission in Module Assessment Score 1, as per the system of evaluation. Data Collection: collect meaningful raw data according to the principles explained in class.

Size must be according to the case, maintaining the principle that data must be as representative as possible of the population under analysis, follow principles explained in chapter 1, about necessary sample size. Using data from the office of the Census website, or the Department of Labor Statistics, or the Department of Health, the CIA World Fact book websites, The Bureau of Economic Analysis, The United Nations, and the corresponding organizations that poll the population of different countries. Consult Dr. Bestard's Assignment - Mathematics - Library Resources for Students - LibGuides at Miami Dade College Learning Resources (mdc.edu)Links to an external site. and /or Online Weather Center, powered by Earth NetworksLinks to an external site.

If the project is developed in reference to the comparison of two populations with respect to a specific variable, collect the corresponding evolution of the structure of the population in each group (gender and ethnicity) and the average family income, for example to dispose the circumstances where the comparison takes place. You may also be supported by the Learning Resources at to an external site. . While by the end of WEEK VI (week IX mini) you will submit the well-structured results found in the research of data collection in an email sent to my mdc.edu account. If necessary, plan a meeting with the professor to interchange info and details in the progress. Continue and complete the data collection and show your progress in that topic compared to the first checkup you did in the past Week IV.

Present data collection and discussion of bias and the sources used to collect data Produce the stem and leaf for the main quantitative variable; histogram and box plots; bar graph for collateral categorical variable. Write the discussion of the displays to present as descriptive statistics evidence, based on the five characteristics of data (center, variability and shape of distribution, presence of outliers and evolution of data. Present the descriptors panel with a statement analyzing every descriptor. in addition to the previous, Assess the presence of outliers always using several methods: fences, Z-scores and probability. Write the discussion of the presence of outliers Present the Normality Test and the Chi-Square Goodness of fit, that verifies the validity of the sample data with respect to the population.

Write the discussion of such tests. Produce Chi-square tests to state inferential criteria test independence and /or goodness of fit for qualitative variables, explain the rationale for selecting hypotheses and significance level, as well as the conclusion and interpretation, intended to assist in describing the targeted population. Explain the normality of data using technology (using technology as a summary of the other remaining descriptive methods). Then for the quantitative variables use stem and leaf displays, box plots and histograms, to facilitate the explanation of the characteristics of each variable, according to examples conducted in class. By the WEEK VII( week X mini) The student must be able to document the descriptive statistics, explaining the sample technique that was used proficiently, identify sources of bias, produce and discuss a stem and leaves, box plot, histogram, full descriptors panel discussing each of the descriptors and its meaning for the data collected.

The Normality Test and a collateral variable using Chi Square test of Goodness of fit to validate such data collected. Produce customs side by side stem and leaf displays; frequency distribution tables ( if necessary); Side by Side Relative Frequency Polygons; Side by Side Box plots; Bar Graphs for the variables involved in the study. Use same intervals in all the displays for each of the quantitative variables, as a logical comparative method. Produce Normality Tests for the quantitative variables, explain the rationale for selecting hypotheses and significance level, as well as the conclusion and interpretation of the test, the full Descriptive Statistics (use MINITAB), Control Chart for the main quantitative variable, Time Series if necessary.

By the Week VIII( week X mini) : The student must incorporate the early inferential techniques of Correlation -Regression and Normality test, for the main quantitative variables that show potential level of association or correlation described before; Chi-Square Goodness of fit and Chi-Square test for Independence applied to categorical variables involved in the study. Presenting the partial results in a meeting with the professor. This will be the second grade in the Module Assessment Score

By the Week IX Week XI mini): The student will explain the interpretation of the confidence intervals, hypothesis testing, ANOVA, and get along with the literature that may have provided information for your data collection, explaining the validity of the data using Normality test Chi Square Goodness of fit with the models the Office of the Census provide for the reference variables.

Use Minitab software and request all the help you may need but never procrastinate, Tutors are available in Hialeah Campus Learning Resources; other technologies with different platforms can be used, but the corresponding interpretation is imperative. Week XI( week XII mini): Data Understanding: Produce Confidence Intervals, for the Mean, the Proportion (as appropriate) and the Variance. Develop Hypotheses tests to test one population Parameter and the difference of Means or Proportions. Develop the Correlation- Regression analysis for the association predictor response of two variables in the study or conduct ANOVA. It is critical every student understands that the execution of the project is not a unique last-minute activity, but a continuous one that is checked and graded in every module SUBMISSION DEADLINE: Week XIV Information for presenters: Following the guidelines of the poster presentations in Learning Resources, use the report to construct a brief power point presentation and from there the poster by registering for the SoS Symposium or Hialeah Campus Symposium from now, to present your results.

Paper For Above instruction

The course project outlined for STA2023 is an extensive, multi-phase research endeavor that emphasizes understanding statistical concepts through practical data collection, analysis, and interpretation. The project mandates students to integrate descriptive and inferential statistics, culminating in a comprehensive technical report and a professional presentation. This process begins with defining a research theme during week II, followed by preliminary data searching and collection from reliable sources such as government databases, international organizations, and statistical repositories. The dataset must be representative, with attention paid to sampling principles to ensure validity and reliability.

Throughout the project, students employ a variety of graphical tools such as histograms, box plots, stem-and-leaf displays, bar charts, and frequency polygons to visualize data distributions. These visualizations help analyze key characteristics including central tendency, variability, distribution shape, and outliers. Multiple methods, such as fences, Z-scores, and probability assessments, are used to detect outliers, with discussions on their implications for data integrity. Statistical tests for normality (e.g., Shapiro-Wilk, Kolmogorov-Smirnov) and goodness-of-fit (Chi-Square) are employed to verify whether data follow expected distributions, establishing the foundation for subsequent inferential analysis.

Inferential procedures include hypothesis testing for population parameters, tests of independence for categorical variables using Chi-Square, and correlation-regression analysis to explore relationships between quantitative variables. Students perform these tests using Minitab and other statistical software, rigorously explaining the rationale behind hypothesis choices, significance levels, and the interpretation of results. The process emphasizes continuous project management, with regular updates, partial assessments, and discussions with faculty to ensure progress aligns with academic standards.

In the later stages, students interpret confidence intervals, conduct ANOVA to compare group means, and analyze time series data if applicable. The project culminates in producing detailed report sections, including methodology, results, conclusions, and references. The final deliverable is a polished presentation and poster prepared for dissemination at academic symposiums. The entire project requires time management, consistent effort, and adherence to statistical best practices, preparing students for real-world data analysis roles.

References

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