First, let's calculate the mean and standard deviation for each variable: Variable 1: mean = (1 + 1 + 2 + 4) / 4 = 2, standard deviation = 1.414 Variable 2: mean = (10 + 8 + 4 - 2) / 4 = 5, standard deviation = 5.291 Next, let's calculate the covariance between the two variables: Covariance = [(1 - 2) * (10 - 5) + (1 - 2) * (8 - 5) + (2 - 2) * (4 - 5) + (4 - 2) * (-2 - 5)] / 4 Covariance = -7.33 Finally, let's calculate the correlation coefficient: Correlation coefficient = Covariance / (Standard deviation of Variable 1 * Standard deviation of Variable 2) Correlation coefficient = -7.33 / (1.414 * 5.291) Correlation coefficient = -0.9799 Therefore, based on the provided data, there is a weak negative correlation between Variable 1 and Variable 2, with a correlation coefficient of -0.73514. However, it is important to note that correlation does not necessarily imply causation, and additional research would be needed to determine the nature and direction of the relationship between these variables. R code: for you reference data <- data.frame(variable1 = c(1, 1, 2, 4), variable2 =