data visualisation

Question 1

Statistical visualization

Answer 1

• A fancy way of saying graphs?
• Sort of, but visualisations we’ll learn about today draw on statistical calculations (median, IQR)
• So we are not just graphing raw data but visualising statistical summaries of data

Question 2

Why might we use graphs rather than numeric summaries (tables, numbers in text)?

Answer 2

• Visually appealing
• Can convey a ”story” without reader needing to know statistics
• Quickly identify trends or patterns in data
• Descriptive statistics can hide patterns in data

Question 3

Histogram

Answer 3

• Used for continuous data
• Bar charts are used for categorical data
• Divide up all the possible values into “bins” and then count the number of observations in each bin

Question 4

Tukey Boxplots

Answer 4

• Suited to continuous data
• Shows 5 descriptive statistics in one plot
• Minimum bound, Q1, median, Q3, maximum bound
• Plus outliers
• Allow you to say a lot about your data, as we will find out
• Each section contains 25% of the data
• The size of these sections tell us about the amount of variation
• The lower “whisker” is short, so data here are quite similar
• The upper whisker is long, so there is more variability in the data here
• Let’s see this in histogram form

Question 5

Minimum and Maximum Bounds

Answer 5

• Not true min and max
• Min and Max bounds are largest data points above and below the thresholds
• These thresholds are Q1 – (1.5 x IQR) and Q3 + (1.5 x IQR)
• Observations larger than this value are referred to as outliers, and plotted as dots

Question 6

(r studio) histogram

Answer 6

• Histogram(data, x = dataset, bins = 16, by = dataset, position =’dodge’)

Question 7

(r studio) boxplot

Answer 7

• Tukeyboxplot(data = data, y = dataset, x = dataset) + labs(x = ‘data’, y = ‘dataset, in seconds’

Question 8

(r studio) scatterplot

Answer 8

• scatterplot(data = data, x =dataset, y = dataset)