For This Request (MAT201 SLP) Please Separate Each As Such

FOR THIS REQUEST (MAT201 SLP) PLEASE SEPARATE EACH AS SUCH SLP01, SLP02, SLP03, SLP04 AND SLP05

Provide a comprehensive series of five session-long project (SLP) assignments for the course MAT201, covering data collection, analysis, and statistical testing related to a self-selected quantitative variable. Each SLP must follow the specified prompt, with no page or word limit, and be submitted by the designated deadline.

Paper For Above instruction

SLP01: Data Collection and Description

Select a single quantitative variable from your life to collect data on over at least 10 days. Examples include the time it takes to commute to work, daily TV viewing hours, or the number of phone calls received. Begin collecting data immediately, recording your chosen variable daily, aiming for more than 10 observations. Describe the variable, the reasons for choosing it, and your data collection process, including how you ensured accuracy and consistency. Explain the importance of this data within the context of basic statistical understanding. Include any initial thoughts or expectations about the data, referencing relevant course materials on probability and data collection methods. Submit this report via CourseNet. Use credible sources and cite all references to support your explanation.

SLP02: Measures of Central Tendency Analysis

Using the data collected in SLP01, calculate the mean, median, and mode. Analyze whether these measures are higher or lower than your initial expectations and discuss which measure most accurately describes your variable’s behavior. Write a concise paper elucidating your calculations, interpret the results, and justify your choice of the most representative measure of central tendency for your variable. Reflect on what these measures reveal about your data and lifestyle variable, citing relevant statistical concepts from course readings or other reliable sources. Submit your paper via CourseNet, ensuring proper citation and reference listing.

SLP03: Frequency Distribution and Standard Deviation

Construct a frequency distribution table from your collected data using Excel or similar tools. Calculate the standard deviation to quantify data variability. Evaluate whether the distribution approximates a normal distribution by examining the shape, skewness, and kurtosis of the data. Discuss the implications of these findings, considering the idea of data normality and the significance of the standard deviation in understanding data spread. Support your analysis with references from academic sources on normal distributions and statistical variability. Submit your paper to CourseNet for assessment.

SLP04: Further Data Collection and Sampling Evaluation

Continue collecting data for five additional days, then analyze whether the larger sample affects the descriptive statistics, such as the mean. Observe any trends, increases, or decreases in your central tendency measures. Critically evaluate whether your current sample size is sufficient to represent your variable accurately or if more data collection is necessary. Discuss sampling techniques and evaluation methods used for reliable data collection, referencing appropriate statistical literature. Submit your report via CourseNet, including citations and references.

SLP05: Statistical Testing Using ANOVA and Regression

Divide your total data set into two halves: the first 8 observations and the last 7 observations. Conduct an ANOVA test to determine if there is a significant difference between these two halves, utilizing online ANOVA calculators or statistical software. Additionally, create a time series from your data, labeling each observation sequentially from 1 to N. Perform a simple regression analysis with the original data set as the dependent variable and the time series as the independent variable. Interpret the regression output, assessing the relationship over time. Discuss scenarios where ANOVA and regression are applicable, guided by course concepts and reputable statistical resources. Submit your comprehensive results and interpretation to CourseNet for grading.

References

  • Currell, D., & Vickerman, R. (2008). Quantitative Geography: Perspectives on spatial data analysis. Springer.
  • Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
  • Goldstein, H. (2011). Multilevel Statistical Models. Routledge.
  • Montgomery, D. C., & Runger, G. C. (2014). Applied Statistics and Probability for Engineers. Wiley.
  • Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Pearson.
  • Triola, M. F. (2018). Elementary Statistics. Pearson.
  • Helsel, D. R., & Hirsch, R. M. (2002). Statistical Methods in Water Resources. Elsevier.
  • Wooldridge, J. M. (2015). Introductory Econometrics: A Modern Approach. Cengage Learning.
  • Agresti, A., & Franklin, C. (2009). Statistics: The Art and Science of Learning from Data. Pearson.
  • Verbeek, M. (2012). A Guide to Modern Econometrics. Wiley.

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