Avio
Alice - Project Lead
Leo – Hotel manager

Working with Data: • Before starting any type of formal data analysis, we should try to get a preliminary sense of the data
• Identify trends, group data

Histograms
-A histogram shows us where the data tend to cluster
Skewd right – mean is higher than median
Outliers:
-Values that fall outside of the trend -Understand where the outlier comes from
-Leave it or remove it

Central Value Data
• Mean – Average o Most common measure used to describe the "center" or "central tendency" of a data set o Can be thrown off by outliers
• Median – Middle number o The median is the middle value of a data set whose values are arranged in numerical order. Half the values are higher than the median, and half are lower. o Example: Income o Outliers do not effect it o Better value to represent skwed data
• Mode – most frequent o Distribution with 2 peaks is a bimodal distribution

Standard Deviation
• Common measure for describing how much variability there is in a set of data. o The standard deviation measures how much a data set varies from its mean. o Large standard deviation indicates that the data are widely dispersed. o Smaller standard deviation tells us that the data points are more tightly clustered together. o Calculating
• 1) Calculate variance
• To find the variance, we first sum the squares of these differences.
• 2) The standard deviation is simply the square root of the variance

Coefficient Variation
• The ratio of the standard deviation to the mean.
• Percent of the mean
• The coefficient of variation describes the standard deviation as a fraction of the mean, giving you a standard measure of variability.

2 variables
• Scatter diagrams provide a visual summary of the relationship between two variables.
• Time series o Time series are extremely useful because they