The following table shows data concerning the number of instances of influenza (3 different strains of the flu) and Respiratory Syncytial Virus in the United States for different seasons from 1976 - 1999. For each year, calculate the percent of positive tests for each of the flu virus (use only the Total Positive tests for Influenza). Create your own table comparing the year to the percent positive tests. Once this is completed, construct a scatter plot comparing the year (starting year of the season can be used) and the percent of positive test results. Draw a line of best fit for each scatter plot. Determine the equation of the trend line you sketched (keep in mind you have a different scale for the xand y-axis, so you need to use the slope formula rather than counting boxes). Give the equation of these lines in slope-intercept format. Use this equation to predict what percent of people that should have been diagnosed with the flu in the year 2014 if this trend continued. Outside research: How accurate was your result? See if you can find data about last year's flu season and see how accurate your results were. You can use data from the Center for Disease Control to find last year's data. Explanation of your findings: Was your estimate using the trend line to calculate last year's data accurate to last year's actual data? If the results are not accurate, give a possible explanation as to why this would be the case. What you will be turning in: Information from the article: Your table with cold season and percent of positive flu tests. Your scatter plot with trend line for this data. Your equation for the trend line (and all accompanying calculations). In a paragraph, discuss this trend. Is it a positive, negative, or no trend? In words, describe what these data representation shows you? What does this mean to you? Your calculations using your trend line to estimate the percent for the last cold season (2014). The percent you found doing your outside research and how these numbers compared. In words, give a possible explanation of your accuracy or why you think your data was not accurate