# Quantitative Data Analysis

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• Quantitative Data Analysis
• Measures of Central Tendency
• Mean
• It makes us of the values of all the data
• Calculated by adding all the numbers and dividing by the number of numbers
• It can be misrepresentative of the data as a whole if there are extreme values
• It cannot be used with nominal data
• Median
• Not affected by extreme scores
• Not as 'sensitive' as the mean because not all values are reflected in the median
• The middle value in an ordered list
• Mode
• Useful when the data are in categories i.e. nominal data
• Not useful way of describing data when there are several modes
• The value that is most common
• Measures of Dispersion
• Range
• Provides you with direct information
• Easy to calculate
• Take the smallest number away from the largest number
• Affected by extreme values
• Doesn't take into account the number of observations in the data set
• Standard Deviation
• This is a measure of the spread of the data around the mean
• More precise measure of dispersion because all values taken into account
• May hide some of the characteristics of the data set (e.g. extreme values)
• Visual Data
• Scatter
• A kind of graph used when doing correlational analysis
• Bar
• The height of the bar represents frequency. Shows data in categories but also suitable for numbers.
• Line
• As with a bar chart , the y axis represents frequency but, in this case, the values along the x axis must be continuous i.e. data that have some implicit order such as numerical data but not categories of things such as favourite football teams
• Tables
• The numbers you collect are referred to as 'raw data' - numbers that haven't been treated in any way. These data can be set out in a table or summarised using measures of central tendency and range