To open R with the dataset preloaded, right-click here and choose "Save Target As" to download the file to your computer. Then find the downloaded file and double-click it to open it in R.The data have been loaded into the data frame a. Enter the command a to see the data. The variables in a are animal, gestation, and longevity.animal: the name of the animal speciesgestation: the average gestation period of the species, in dayslongevity: the average longevity of the species, in yearsNotice that the correlation between gestation and longevity has changed.Remember that the correlation is only an appropriate measure of the linear relationship between two quantitative variables. First produce a scatterplot to verify that gestation and longevity are nearly linear in their relationship.To do this in R, copy the entire command below:plot(a$longevity,a$gestation,xlab="Average Longevity of Species (years)", ylab="Average Gestation Period of Species (days)")Observe that the relationship between gestation period and longevity is linear and positive. Now we will compute the correlation between gestation period and longevity.To do that in R, copy the command:cor(a$longevity,a$gestation)Now return to the scatterplot that you created earlier. Notice that there is an outlier in both longevity (40 years) and gestation (645 days). Note: This outlier corresponds to the longevity and gestation period of the elephant.Report the correlation between gestation and longevity and comment on the strength and direction of the relationship. Interpret your findings in context.

To open R with the dataset preloaded, right-click here and choose "Save Target As" to download the file to your computer. Then find the downloaded file and double-click it to open it in R.The data have been loaded into the data frame h. Enter the command h to see the data. There are three variables in h: gender, height, and weight.The variables are identified as follows:gender: 0 = male, 1 = female.height: in inches.weight: in pounds.First we will create a scatterplot to examine how weight is related to height, ignoring gender.To do that in R, copy the following command to R:plot(h$height,h$weight)Again, a good graphic should have labels so lets add x and y-axis labels:plot(h$height,h$weight, xlab="Height (inches)", ylab="Weight (lbs)")Describe the relationship between the height and weight of the subjects suggested by the data. Consider the pattern of the data—mainly direction and form—and any deviations from this pattern, such as outliers.

Now return to the scatterplot that you created earlier. Notice that there is an outlier in both longevity (40 years) and gestation (645 days). Note: This outlier corresponds to the longevity and gestation period of the elephant.What do you think will happen to the correlation if we remove this outlier?To do this in R, copy the following command:cor(a$longevity[a$animal!="elephant"],a$gestation[a$animal!="elephant"])Notice that the correlation between gestation and longevity has changed.Report the new value for the correlation between gestation and longevity and compare it to the value you found earlier when the outlier was included. What is it about this outlier that results in the fact that its inclusion in the data causes the correlation to increase? (Hint: look at the scatterplot.)

Use R data frames to study and analyze real-world datasets, perform basic data manipulations, and generate descriptive statistics using R functions.

## 1. Load data set(s) and libraries ```{r} load("C:\\Users\\local_9\\OneDrive\\Desktop\\Assignments\\Second Year\\Statistics\\Statistics final project\\High_School_Alcoholism") vars <- c("X1st_Semester_Grade", "Alcohol_Weekdays", "Alcohol_Weekends", "Desire_Graduate_Education") my_data <- data.frame(vars) library(descr) library(stats) save.image("myProject.RDATA") ```

Use the packages  “survival”  and  "survminer" in RStudio to answer the following three questions.  See Question 3 in Practice sheet 4 for details on how to use the above packages to analyse the data. You need to setup up the above data in a csv file (suggest that enter the data in an Excel file in the correct format (with column 1 name as  “status” and column 2 name as “time”) and save it as “csv” file.The following data gives the lifetimes (in operating hours) of 23 identical components.  The measurements with `*’ indicate suspensions.8.0, 21.3, 25.5, 8.1, 4.5, 4.2, 13.2, 9.9, 11.9*, 27.0, 25.7, 25.8, 28.6, 12.5, 6.0, 19.9, 8.0*, 8.6, 14.5*, 32.9, 15.5, 3.4, 18.2Calculate the Kaplan-Meier estimate of R(20). Give your answer to 3 decimal place accuracy.