Question 1 [5 pts] Look at the data set and answer the following questions. a) What are the cases and how many variables are recorded for each case? b} One possible research question that this data could answer is "Do the running times differ for male vs. female who enter these types of races? " Write at least two other research questions that could be answered by this data. Question 2 [10 pts] Every column of data in this data set can be recognized by nameofdatasetSnameofcolumn. 50, the data set containing all 8385 l5 PUHd3t32$Ase. The rst research question is "Are runners who enter such trail ultras on average over 40 years old?" To answer this question, do the following: a) bi d) State appropriate null and alternative hypotheses. First using statistical terms, then using the context of the problem. Check all conditions. Make sure to state how each condition is (or could be) met using the context of the problem. You will need to make a histogram and a boxplot of the ages in RStudio. That is, type hist(rundat32$Age, xlab = "Age") and boxplotlrundata2$Age, ylab = "Age"). and COW/PEStE' bOth graphs into your writeup. Note: you can use the arrow at the upper left of the graph window to go back and forth between the two graphs. To copy this graph, click export, then choose "save as image", then don't click save in RStudio, but instead right click and choose "save as" and name and save the image on your computer. Then open that file, copy the image, and paste it into the document. Calculate the test statistic and p-value using Rstudio. Do not calculate this by hand. You can do the test in RS'EUdiO bY typing t.test(rundataZ$Age, mu = 40, alternative = "greater"}- Then COPY/PBStE the TESUI'ES into your writeup. Note: If the alternative hypothesis has a ">" sign, you will set alternative = "greater", if the alternative hypothesis has a "