STATISTICS FERTILITY PROJECT

This project will cover topics from chapters 1 through 4 of your textbook. All papers will need to be submitted on IvyLearn.

You will be performing an analysis on a dataset that contains data on fertility and life expectancy for 184 different countries. All data is from the year 2018. The fertility numbers are the average number of children per woman in each of the countries. The life expectancy numbers are the average life expectancy in each of the countries.

You will be turning in a paper that should include section headings, graphics and tables when appropriate and complete sentences which explain all analysis that was done in addition to all conclusions and results. There is not a specified length, however it is important that you follow all steps below and grade yourself using the rubric provided since it is the rubric that I will be using to grade your submissions. All work should be your own. Plagiarism will result in a project score of 0.

Steps (all statistical analysis to be done in Excel and/or StatCrunch):

  1. Watch the TED talk by Hans Roling titled “The best stats you’ve ever seen”.  You will need to include comments on this in your paper. Here is a link: http://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen?language=en
  2. Download the Excel data from IvyLearn
  3. Create histograms of each of the variables (one histogram for fertility, one for life expectancy). Use the histograms to identify the shapes of the distribution. StatCrunch will be the easier tool to use for this particular task. If you use Excel, you are required to use a histogram plug-in or bin the data yourself (not an easy task).
  4. Calculate some descriptive statistics for each of the variables, including but not limited to the mean, median and standard deviation. Organize these numbers nicely in a table.
  5. Using fertility as the predictor variable and life expectancy as the response variable, create a scatter diagram, come up with the least-squares regression line (you need to state the actual equation for the least-squares regression line) and calculate the linear correlation coefficient as well as the coefficient of determination. Make sure that you understand all interpretations and include them in your paper. Please carefully review the rubric below to see the full list of required interpretations.
  6. Use the regression line to predict life expectancy for the United States given fertility and then compare this to the actual value in the United States. Hint: you will have to plug the actual fertility value for the US into your equation.
  7. Name some possible lurking variables that may be at work here.
  8. Explain the difference between correlation and causation and why we cannot say that there is a cause and effect relationship in this situation.
  9. Explain why we cannot use our regression model to predict the life expectancy of one particular individual.
  10. Take a look at the website where this data was pulled from and comment on how the model might have been different if we used the data from 20, 40 or 60 years ago. Navigate to http://gapminder.org and click on “Tools”. You should see a bubble chart appear. Use the x-axis and y-axis dropdown menus to ensure that ‘life expectancy (years)’ is selected on the y-axis and ‘children per woman (total fertility)’ is selected on the x-axis.
  11. Put everything together into an organized paper and submit on IvyLearn.
Graded ItemPoints PossiblePoints Earned
Organization/Formatting  
Paper is well organized with clear section headings5 
Paper contains data in tables and displayed in graphics when appropriate5 
Graphical Representations/Descriptive Statistics  
Histograms for each variable10 
Comments on shape of distribution for each variable5 
Descriptive statistics for each variable, well organized10 
Regression  
Scatterplot5 
Least-squares regression line10 
Linear correlation coefficient5 
Coefficient of determination and interpretation5 
Interpretation of slope and y-intercept10 
US prediction and comparison to actual value5 
Additional Discussion/Thoughts  
Lurking variables5 
Correlation/causation5 
Why we cannot use for individual predictions5 
Commentary on how model would have been different in the past5 
Tie-in of Hans Rosling TED talk5 
TOTAL100 
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