Data science enhances people’s decision making. Doctors and researchers are making critical decisions every day. Therefore, it is absolutely necessary for those people to have some basic knowledge of data science. This series aims to help people that are around medical field to enhance their data science skills.

We will work with a health related database the famous “Pima Indians Diabetes Database”. It was generously donated by Vincent Sigillito from Johns Hopkins University. Please find further information regarding the dataset here.

This is the seventh part of the series and it aims to cover partially the subject of Inferential statistics.

Researchers rarely have the capability of testing many patients,or experimenting a new treatment to many patients, therefore making inferences out of a sample is a necessary skill to have. This is where inferential statistics comes into play.

In more detail, in this part we will go through the hypothesis testing for F-distribution (F-test), and Chi-squared distribution (Chi-squared test). If you are not aware of what are the mentioned distributions please go here to acquire the necessary background. The assumption of the t-test (we covered it last time here) is that the two population variances are equal. Such an assumption can serve as a null hypothesis for F-test. Moreover sometimes it happens that we want to test a hypothesis with respect to more than one probability, here is where Chi-Squared test comes into play.

Before proceeding, it might be helpful to look over the help pages for the `sd`

, `var`

, `var.test`

, `chisq.test`

.

Please run the code below in order to load the data set and transform it into a proper data frame format:

`url <- "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"`

`data <- read.table(url, fileEncoding="UTF-8", sep=",")`

`names <- c('preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class')`

`colnames(data) <- names`

`data <- data[-which(data$mass ==0),]`

Moreover run the chunk below in order to generate the samples that we will test on this set of exercises.

`f_1 <- rnorm(28,29,3)`

`f_2 <- rnorm(23,29,6)`

Answers to the exercises are available here.

If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your answer as a comment on that page.

Exercise 1

Compute the F-statistic. (test statistic for F-test)

Exercise 2

Compute the degrees of freedom for the numerator and denominator.

Exercise 3

Apply a two-sided F-test for the two samples

Exercise 4

Apply a one-sided F-test for the two samples with the alternative hypothesis to be that the standard deviation of the first sample is smaller than the second.

Exercise 5

Retrieve the p-value and the ratio of variances for both tests.

Exercise 6

Find the number of patients who show signs of diabetes and those who don’t.

Exercise 7

Assume that the hypothesis we made is that 10% of people show signs of diabetes. Is that a valid claim to make? Test it using the chi-squared test.

Exercise 8

Suppose that the `mass`

index affects whether the patients show signs of diabetes and we assume that the people who weight more than the average are more likely to have diabetes signs. Make a matrix that contains the true-positives, false-positives, true-negatives, and false-negatives of our hypothesis.

hint: True-positive: `data$class==1 & data$mass >= mean(data$mass)`

Exercise 9

Test the hypothesis we made at exercise 8 using chi-squared test.

Exercise 10

The hypothesis we made at exercise 8 cannot be validated, however we have noticed that the dataset contains outliers which affect the average. Therefore we make another assumption that patients who are heavier than the 25% lightest of the patients are more likely to have signs of diabetes. Test that hypothesis.

hint: it is similar to the process we did at exercises 8 and 9 but with different criteria.

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