Incidence of Measles in U.S. since the end of the WWII

Topic: Incidence of measles in U.S. since the end of the WWII.



Data Collection: (Week VI)

  1. Collect meaningful raw data, according to the principles explained in Chapter 1 in two – three quantitative variables and two –three qualitative variables, related to the theme.
  2. Sample Size must be according to guidelines, given and following further principles in chapter 7, about necessary sample size.
  3. Use data from the office of the Census website, Wolfram Alpha search engine, Google, MDC Fl state databases, the Department of Labor Statistics, the Department of Health, the CIA World Fact book websites, the lib guides for math and stat at MDC Learning Resources and the corresponding organizations that poll the population of different countries.
  4. Produce real data collection, using sampling techniques as per Chapter 1 principles in your domain. Imperative the input of collected data in software worksheets, and the mastery of the main procedures as explained in class.
  5. 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 declare the circumstances where the comparison takes place.
  6. Complete written report to discuss, using EDA for the five characteristics of data described in Chapters 2 and 3: full descriptors panel in MINITAB, Stem and leaf plots, Box plots, Normality test as in 6.5, Chi – Sq test of Goodness of fit and Chi- Sq test of Independence using MINITAB as in 11.1, 11.2) Correlation -Regression between two variables as 2.4 in MINITAB. Use Control charts for incorporate the analysis of outliers together the fences and Z-scores tools

(THE PREVIOUS POINTS ARE THE MAIN COURSE FOR PROJECT CHECK 1, IN EARLY WEEK VIII Submission by FEB 21, 2021, 11:59 PM via attachment to email)

Be able to document with several technical statistical arguments your data collection, explaining the sample technique that you used proficiently, identify sources of bias, and get along with the literature that may have provided information for the method of data collection. During the Week VI the lecture sessions are showing examples of previous projects and the execution of the different components


Data Organizing and Data Display: (Weeks VI – VIII)

  1. a) Construct the Stem and Leaves display (using the software) for the main quantitative variable under analysis. Identify the five numbers summary. Determine the inner, outer, lower and upper fences to assess the presence of outliers. Produce the bar graphs and/or pie charts for the categorical variables or attributes that may permit the characterization of the population under analysis.
  2. b) Produce the corresponding Chi-square tests to state the inferential criteria for independence and /or goodness of fit for qualitative variables, explain the rationale for selecting hypotheses and select the significance level, as well as write the conclusions and interpretations of such tests, intended to assist in describing the targeted population.
  3. c) Explain the normality of data using technology for the normality tests. Use previous procedure to build up the normality test.
  4. d) 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 examples conducted in class.
  5. e) Produce customs side by side stem and leaf displays; Frequency distribution tables; 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. 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 software), Control Chart for each variable, Time Series, to assess presence of outliers and evolution in time. Detail the incidents during data collection that are observed in the displays and produce substantive interpretation of trends.
  6. f) Complete written report to discuss



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