Math assignment (needs excel and word) needs to be finished in 3 hrs

Assignment Three
(30 points)

Part 1: Children In Poverty
(17 Points – each part is worth 2 points unless noted)

1. Open the file ChildrenBelowPovertyLevel.xls containing data from the Census Bureau.

a. Make an X-Y scatter plot of the data including the trendline and the R-squared value. Note that Excel will, in most cases, put a legend on your graph by default.  When there is only one data series (as here), you don’t need a legend, and it really should be removed.  It should include all the details discussed in the reading on graphs. Paste this chart in your Word document. (3 points)

b. Predict what percentage of children will be below the poverty level in the year 2012 using the trendline equation. Type your result in your Word document. 

c. How much confidence do you have in this prediction?  In 3-4 sentences write an argument that either supports or does not support your prediction of the percentage of children below the poverty level 2012.  (Important:  Use the language you learned in the reading for this week. There are three major components you must include in your argument to receive full credit.) (3 points)

d. Use the regression equation (the equation on the graph) to predict when 100% of children in the US will be below the poverty level. Show your workand type your answer into your Word document. (As long as you show how you set up the problem, that is enough. You do not need to show every step you used when solving.)  

e. How much confidence do you have in this prediction?

f. Predict the percentage of children below the poverty level in the year 2016 using the trendline equation. Type your result in your Word document.

g. The actual percentage of children below the poverty level in the year 2012 was 21.3%. In 2016 that percentage fell to 17.6%. Compare these facts to your answers from parts (b) and (f). Now, taking into account all your answers above, write a thoughtful analysis of the usefulness of linear modeling. (You may choose to comment on when and how it is most effective to use linear models and also what cautions we should take when using linear models.) To receive full points, you must truly analyze the work you have done above. A simple summary of your findings will receive zero points. (3 points)

 Part 2: Divorce in the US 
(13 Points – each part is worth 2 points unless noted)

2. Open the file DivorceRate.xls which contains data on the rate of divorce per 1,000 people in the U.S. from 1960 to 2015. 

a. Make an XY scatter graph of the years and the rate of divorce. Add an appropriate trendline to the data, along with an equation, R^2 value, and all other effective graphing components. (Hint: Be extremely thoughtful about the trendline you use in this case. Be sure to check the trendline guidelines to verify that the trendline you use is appropriate. You will lose points if your trendline is not appropriate.) It should include all the details discussed in the week 3 reading on graphs. Paste the resulting chart into your Word document. (3 points)

b. Use the regression equation to predict the rate of divorce in  2018. Type your answer in your Word document. (1 point)

c.  How much confidence do you have in your prediction from part b?  (Important:  Use the language you learned in the reading for this week. There are three major components you must include in your argument to receive ful credit.) (3 points)

d. Use your regression equation to predict when there will be no divorce in the US. Show your work and type your answer into your Word document. (As long as you show how you set up the problem, that is enough. You do not need to show every step you used when solving.) (1 point)

e. Do you have confidence in your prediction from part d?  Explain by writing a confidence argument.

f. Use your regression equation to predict what the rate of divorce will be in 2050.  Type your answer into your Word document – don’t forget to include units!  (1 point)

g. How much confidence do you have in your prediction from part f?  Explain by writing a confidence argument. 

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