Science

"Firstly, create the graph to visualize the data. Here, there are two groups (dance and control) so, in comparing the proportion of patients rated as slow in each group, a bar plot is suitable.

# Create a data frame with the given data

parkinsons_data <- data.frame(

  Group = c(""Dance"", ""Control""),

  Slow = c(38, 21),

  Fast = c(62, 79)

)

# Calculate the proportions of slow patients in each group

parkinsons_data$Proportion_Slow <- parkinsons_data$Slow / (parkinsons_data$Slow + parkinsons_data$Fast)

# Plotting the bar plot

barplot(parkinsons_data$Proportion_Slow, names.arg = parkinsons_data$Group, 

        ylim = c(0, 1), ylab = ""Proportion of Slow Patients"", 

        main = ""Proportion of Slow Patients in Dance vs Control Group"")

Next, let's perform a statistical analysis to test the hypothesis. We are interested in comparing the proportions of slow patients between the dance and control groups. We will use a chi-squared test for independence to assess whether there is a significant association between group (dance vs. control) and patient progression (slow vs. fast).

R

Copy code

# Perform chi-squared test for independence

chisq_result <- chisq.test(matrix(c(38, 21, 62, 79), nrow = 2, byrow = TRUE))

# Output the results

print(chisq_result)

The null hypothesis (H0) is that there is no association between group (dance vs. control) and patient progression (slow vs. fast). The alternative hypothesis (H1) is that there is an association between these variables.

The test statistic is the chi-squared statistic, and the degrees of freedom is (number of rows - 1) * (number of columns - 1) = (2-1) * (2-1) = 1."