Relative Frequency: Example
Example 1
- Weather data: Relative frequencies of cloud cover
- The frequency table for the cloud cover from the weather data
- Add margins to calculate the relative frequency.
- Identify the total number of possible events.
- Note that the sum of their probabilities is 1.
'data.frame': 168 obs. of 9 variables:
$ day : int 1 1 1 1 1 1 1 1 1 1 ...
$ hour : int 0 1 2 3 4 5 6 7 8 9 ...
$ cloud : int 1 6 3 4 1 1 4 7 6 6 ...
$ rad : int 0 0 0 0 6 56 130 166 300 527 ...
$ temp : num 3.7 3.5 2.6 2.9 2.5 2 2.9 3.1 1 2.8 ...
$ humidity: int 80 78 82 74 68 69 69 76 96 89 ...
$ rain : chr "Dry" "Dry" "Dry" "Dry" ...
$ windsp : int 11 11 8 13 15 14 16 16 9 12 ...
$ winddir : chr "West" "West" "West" "West" ...
cloud
0 1 2 3 4 5 6 7 8
2 16 10 17 11 7 18 77 10
cloud
0 1 2 3 4 5 6 7 8 Sum
2 16 10 17 11 7 18 77 10 168
cloud
0 1 2 3 4 5 6 7 8
0.0119 0.0952 0.0595 0.1012 0.0655 0.0417 0.1071 0.4583 0.0595
Example 2
- Weather data: Relative frequencies for wind direction and rain
- The frequency table for wind direction and rain from the weather data
- Add margins to calculate the relative frequency.
- Identify the total number of possible ‘events’.
- Note that the sum of their probabilities is 1.
'data.frame': 168 obs. of 9 variables:
$ day : int 1 1 1 1 1 1 1 1 1 1 ...
$ hour : int 0 1 2 3 4 5 6 7 8 9 ...
$ cloud : int 1 6 3 4 1 1 4 7 6 6 ...
$ rad : int 0 0 0 0 6 56 130 166 300 527 ...
$ temp : num 3.7 3.5 2.6 2.9 2.5 2 2.9 3.1 1 2.8 ...
$ humidity: int 80 78 82 74 68 69 69 76 96 89 ...
$ rain : chr "Dry" "Dry" "Dry" "Dry" ...
$ windsp : int 11 11 8 13 15 14 16 16 9 12 ...
$ winddir : chr "West" "West" "West" "West" ...
rain
winddir Dry Wet
Calm 13 1
East 14 6
North 18 5
South 15 2
West 77 17
rain
winddir Dry Wet Sum
Calm 13 1 14
East 14 6 20
North 18 5 23
South 15 2 17
West 77 17 94
Sum 137 31 168
rain
winddir Dry Wet
Calm 0.0774 0.0060
East 0.0833 0.0357
North 0.1071 0.0298
South 0.0893 0.0119
West 0.4583 0.1012
Example 3
- Weather data: Temperature
- Consider the temperature values in the weather data
- Partition these into groups (or categories or bins or ranges): c(-4,-2,0,2,4,6,8,12) (Hint: Use
cut function)
- Find how many values fall into each category?
- The frequency table for wind direction and rain from the weather data
- Add margins to calculate the relative frequency.
- Identify the total number of possible ‘events’.
- Note that the sum of their probabilities is 1.
'data.frame': 168 obs. of 9 variables:
$ day : int 1 1 1 1 1 1 1 1 1 1 ...
$ hour : int 0 1 2 3 4 5 6 7 8 9 ...
$ cloud : int 1 6 3 4 1 1 4 7 6 6 ...
$ rad : int 0 0 0 0 6 56 130 166 300 527 ...
$ temp : num 3.7 3.5 2.6 2.9 2.5 2 2.9 3.1 1 2.8 ...
$ humidity: int 80 78 82 74 68 69 69 76 96 89 ...
$ rain : chr "Dry" "Dry" "Dry" "Dry" ...
$ windsp : int 11 11 8 13 15 14 16 16 9 12 ...
$ winddir : chr "West" "West" "West" "West" ...
tempbin
(-4,-2] (-2,0] (0,2] (2,4] (4,6] (6,8] (8,12]
3 7 26 54 44 27 7
tempbin
(-4,-2] (-2,0] (0,2] (2,4] (4,6] (6,8] (8,12]
0.0179 0.0417 0.1548 0.3214 0.2619 0.1607 0.0417