can I get help with my math 215 assignment asap?

DhrraA
timer Asked: May 1st, 2018

Question Description

Create at least two visuals using your data from the data you chose in Week 2.

  1. Create one scatter plot of the data, and apply a linear model (also known as a regression) in Excel®. Include the equation, R2 value, and prediction value on the visual.
  2. Create one scatter plot of the data, and apply an exponential model in Excel®. Include the equation, R2 value, and prediction value on the visual.
  3. Determine whether the linear or the exponential model is a better representation of your data to base your prediction on. Explain why the model you chose is a better representation of your data.

Hints for Making an Effective Chart:

  • Decide why you are making a chart from this data.
  • Title each chart so that it aligns with the data and selected model.
  • Create descriptive labels for both the x- and y axes.
  • Resize the chart as needed so it can be viewed easily.

Unformatted Attachment Preview

Health Services and Nursing Scenario Topic 1 Scenario 1 Year 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Predicting the Number of Babies Born Review the data involving the number of babies born in Humboldt County from 2006-2015. Predict the n be born in 2018. Babies Born in Hombolt County 275 280 320 366 358 336 375 390 455 487 ing Scenario nty from 2006-2015. Predict the number of babies who will Security and Criminal Justice Scenario Topic 2 Scenario 2 Year 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Predicting the Number of People Arrested for Drug Possession Review the data involving the number of people arrested for drug possession from 2006-2015. Predict the people who will be arrested for drug possession in 2018. People Arrested for Drug Possession 1,519,760 1,361,658 1,321,824 1,387,915 1,179,728 1,143,931 1,237,708 1,203,323 982,169 801,560 Scenario n from 2006-2015. Predict the number of Humanities and Sciences Scenario Topic 3 Scenario 3 Year 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Predicting Student Smartphone Usage Review the data involving the average number of hours students spent on their smartphones from 2002number of hours students will spend on their smartphones in 2018. Average Number of Hours Students Spend on their Smartphones 0.1 0.75 1 1.5 1.75 5.5 6 9.3 9.5 8.9 9 11 12.5 10.6 Scenario n their smartphones from 2002-2015. Predict the Social Sciences Scenario Topic 4 Scenario 4 Hours of Sleep 4 4.25 4.5 4.75 5 5.25 5.5 5.75 6 6.25 6.5 6.75 7 7.25 7.5 7.75 8 8.25 8.5 8.75 9 9.25 9.5 9.75 10 Predicting Test Performance Based on Sleep Review the data involving the number of hours students sleep and their average score on a test they take Predict the optimal hours of sleep students need the night before a test to achieve the highest score on t Average Score on Test 55 40 53 59 60 63 66 75 70 72 80 77 85 90 100 95 88 85 74 88 90 87 86 95 82 rio average score on a test they take the next day. o achieve the highest score on the test. SUMMARY OUTPUT 5 Regression Statistics Multiple R 0.777042 R Square 0.603795 Adjusted R Square 0.554269 Standard Error 2.021353 Observations 10 ANOVA df Regression Residual Total SS MS F Significance F 1 49.81306 49.81306 12.19155 0.008179 8 32.68694 4.085867 9 82.5 Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Lower 95.0% Upper 95.0% Intercept -5.3827 3.462696 -1.55448 0.158677 -13.3677 2.602289 -13.3677 2.602289 5325 0.001334 0.000382 3.49164 0.008179 0.000453 0.002215 0.000453 0.002215 RESIDUAL OUTPUT ObservationPredicted 1 1 2.151453 2 2.451592 3 7.746044 4 5.60105 5 7.18445 6 8.120884 7 7.668674 8 8.547748 9 7.375205 10 8.152899 PROBABILITY OUTPUT Residuals Standard Residuals -0.15145 -0.07947 0.548408 0.287765 -3.74604 -1.96565 -0.60105 -0.31539 -1.18445 -0.62151 -1.12088 -0.58816 0.331326 0.173856 0.452252 0.237309 2.624795 1.377303 2.847101 1.493953 Percentile 5 15 25 35 45 55 65 75 85 95 1 2 3 4 5 6 7 8 9 10 11 5325 Residual Plot 2 0 -2 - 2,000 -4 4,000 15 -6 10 1 Residuals 4 Normal Probability Plot 8,000 10,000 12,000 6,000 5325 5 Series1 0 5 15 25 35 45 55 65 75 85 95 Sample Percentile Business Scenario Predicting Fuji Apple Purchases Topic 5 Review the monthly data involving Fuji apples purchased at a large grocery store. Predict how many Fu have available for the customers in December (month 12)? Scenario 5 Fuji Apples Purchased 5,325 5,648 5,873 9,842 8,234 9,421 10,123 9,784 10,443 9,564 10,147 Month 1 2 3 4 5 6 7 8 9 10 11 Hint: When determining the solution to this question remember that am up around holidays. Seasonality is a characteristic of a time series in wh and predictable changes that recur every calendar year. Any predictable series that recurs or repeats over a one-year period can be said to be se Linear Regression 12,000 Fruit Purchased 10,000 8,000 6,000 y = 502.34x + 5568.2 R² = 0.6998 4,000 2,000 0 2 4 6 Month 8 10 12 rio ry store. Predict how many Fuji apples will need to be in stock to his question remember that amounts needed in a store will go acteristic of a time series in which the data experiences regular calendar year. Any predictable change or pattern in a time ear period can be said to be seasonal. FUJI APPLE PREDICTED(12) Fuji apples in December (y)=502.5x+5568 y=502.5(12)+5568 11596 Education Scenario Topic 6 Elementary Education: Math Skills Scenario 6 Review the data involving elementary students in Apache County who passed the AZ Merit Test fr pass the AZ Merit test in 2018. Fiscal Year Students in Apache County who Pass the AZ Merit Test 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 12 7 14 18 25 37 33 39 45 42 cenario passed the AZ Merit Test from 2006-2015. Predict the number of students who will
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