I am very confused about this question for my AP stats homework. Please help and thank you so so much

Questions 7 through 9 involve the following data set. Increase in Life Expectancy in the United States during the 20th Century Expected Expected Year Life Span Year Life Span 1920 54.1 1975 72.6 1930 59.7 1980 73.7 1940 62.9 1985 74.7 1950 68.2 1990 75.4 1960 69.7 1995 75.8 1970 70.8 Source: National Center for Health Statistics, published in the 1998 Worl'd Almanac 7. Make a scatterplot of the untransformed data and tell which kind of relationship the points seem to follow. Also name the best type of transformation needed to \"straighten" the plot. (3 points) Note: Part of the transformation should involve subtracting 1900 from the year so you're working with more manageable numbers. Now try some transformations to get the data as close to linear as possible. (Use your calculator to transform the data, and try scatterplots of the different transformations). Then find the regression line, r, and r2. (4 points) Tell which transformation worked the best and back it up by showing: - A scatterplot of the transformed data with the least-squares regression equation and line, r, and rsquared. - A plot of the original data with the regression equation converted to a non-linear equation (similar to what you did for question 6) (You'll get a chance to do a residual plot in the next question.) Here are some hints: - Look at the curve and think about what kind of relationship (equation) could have made such a curve (this is what you already did in question 7). - Then try the kind of transformation that should work for that kind of curve. (See the Summary of Common Transformations at the beginning of this document.) If your rst guess doesn't work, try others. - More than one transformation will yield a good model; choose the one with the strongest value of r. - If your transformation (the one that seems to work the best) doesn't match your answer for question 7, you may want to revise your answer for question 7! Type of transformation: Linear regression equation for transformed data: r and r2: Scatterplot of the transformed data with the least-squares regression equation and line, r, and :3. Plot of the original data with the regression equation converted to a non-linear equation (similar to what you did for question 6). 9. Using the transformed data and the regression equation for it, create a plot of residuals vs. x-values. Sketch the plot and interpret it. (4 points) 10. The data below represents Medicare expenditures from 1970 to 1996, in billions of dollars. (4 points) Year Medicare Expenditures {billions of dollars 1970 7.6 1980 37.5 1985 72.1 1990 112.1 1991 124.4 1992 141.4 1993 153 1994 169.8 1995 187.9 1996 203.1 For this data set, use your TI-83/TI-84 to: A. Create a scatterplot of the data. B. Assume that the relationship of this data is exponential. Transform the data, nd the regression equation, :3. and r2. Based only on the value of r would you consider this a good model for extrapolating increases in Medicare spending? C. Create a residual plot of the transformed data. Does the residual plot change your mind about the usefulness of this model to extrapolate increases in Medicare spending? (Note: Remember that many trends don't have a perfect mathematical model to predict them because there are too many complicating factors to yield a consistent curve. Sometimes in cases like these, a rough model will work as a rough estimator when used with appropriate caution.)