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.

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.

Using the data() command, which of these is NOT a real dataset preloaded into R.Group of answer choicestigersChickWeightsleepiris

Define a function stat(gender, age, height, mass) that returns weight, meter, centimeter, and bmr, in which:Use the variables defined in the input (age, height, mass) and s_value, to implement the formula for BMR: bmr = 10.0 * mass + 6.25 * height - 5.0 * age + s_valueCreate a new variable weight that measures the mass in 斤. Use the conversion factor 1 kilogram = 2 斤.Create a new variables meter and centimeter each represented as integers. Those value should be computed by using one Python arithmetic operator on the variable heightExpected outputAssume mass = 55, height = 160, age = 22, s_value = -161, once you call the function,stat(gender, age, height, mass)The expected output should be:(110, 1, 60, 1279.0)

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.

Data was collected on 130 patients recording their percent of fat and Body Mass Index (BMI). What can you conclude from this scatterplot?