mestion not 'om the book: For this problem, you'll be answering the question "Did cloud seeding increase the amount of rain that fell on rainy days?" using an unpaired (two-sample) t-test. Use the data le called "Rainfall after cloud seeding" that is on D2L. Before using a two-sample t-test, you need to check whether the data for each category meet the assumptions of the test. Part 1: (4 pt for each histogram; total = 8 pt.) To check whether data for each category meet the assumption of normality, create two histograms of frequency distributions using the website for the Histogram Maker. Look in Content>>Handouts>>_\"Directions for Histogram Maker for graphing ea dist\". One of the histograms should show the frequency distribution of rainfall on unseeded days and one should show rainfall on seeded days. Label and attach each of the histograms to your assignment. For each graph, remember to include labels and units on axes, a gure legend describing what the graph shows, and source of data. You can write the labels by hand along the axes. Change the settings on the histogram to look at the data in different ways. Try changing the bin numbers and look at different views. Also try changing the lowest class value. Part 2: (4 pt each for 2 les; total = 8 pts) Turn in an Excel le with four labeled columns (unseeded rainfall, seeded rainfall, log unseeded rainfall, log seeded rainfall). Follow the directions in the le "Directions for Histogram Maker for graphing freq gust" to create the table in Excel. Print and attach the tables to the end of your assignment. Part 344 pt for each histogram; total = 8 pt.) To check whether log transforming the data for each category made the data meet the assumption of normality, create two histograms, one showing the 'equency distribution of log transformed rainfall on unseeded days and one showing log transformed rainfall on seeded days. Label and attach those two histograms to your assignment. Part 443 pt) Is log transformation necessary for the data and do the data meet the assumptions of the two-sample t-test afterwards? Explain. hm: you have to transform both groups of data (data for unseeded days and data for seeded days) in the same way before doing a t-test. For example, if the data for unseeded days needs to be log-transformed, then you also have to log- transform the data for seeded days so that the means are comparable in a t-test. However, if you're doing a correlation, you don't have to transform both variables (X and Y) before doing the analysis. Part 548 pt) Answer this question: Did cloud seeding increase the amount of rain that fell on rainy days? Perform a two-sample t-test using mm and data with the type of transformation (none or log) that you feel is the best choice to meet the assumptions of the test. Follow the directions above for statistical tests. In W5, copy data from unseeded days into one column and data from seeded days into the other column. Remember a two-sample t-test is called "Independent" in mm