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guys deadline is tomorrow and i have no clue how to evaluate spearmans rank and error bars any help PLEASEEEE!!!!
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#2
(Original post by nickymobydicky)
guys deadline is tomorrow and i have no clue how to evaluate spearmans rank and error bars any help PLEASEEEE!!!!
guys deadline is tomorrow and i have no clue how to evaluate spearmans rank and error bars any help PLEASEEEE!!!!
Spearman's rank:
1. It goes from -1 to +1
2. Zero means there is no correlation between two variables I.e. On a graph, the line of best fit is flat; the data points are scattered without a trend
3. A value close to -1 means a negative correlation, so as a variable goes up, the other goes down; on a graph, a line of best fit goes down from left to right; the closer to -1 the value, for example -0.8, the steeper the line and the stronger the correlation; -0.6 is a stronger correlation than -0.3
4. Same goes for +1 which is a positive correlation; as one variable goes up, so does the other; the line on a graph goes up from left to right
5. Error bars represent the range of values that must be considered as potentially true. For example you measure a pear as 9 cm but the ruler's error is 0.1 cm; the error would mean the "real" size could be anything in the 8.9-9.1 cm range.
6. When comparing two data sets with error bars, you want the error bars to be non-overlapping for the data sets to be considered truly different. If they overlap, there isn't a significant difference in values.
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(Original post by Flying Cookie)
Chillax....!!!!
Spearman's rank:
1. It goes from -1 to +1
2. Zero means there is no correlation between two variables I.e. On a graph, the line of best fit is flat; the data points are scattered without a trend
3. A value close to -1 means a negative correlation, so as a variable goes up, the other goes down; on a graph, a line of best fit goes down from left to right; the closer to -1 the value, for example -0.8, the steeper the line and the stronger the correlation; -0.6 is a stronger correlation than -0.3
4. Same goes for +1 which is a positive correlation; as one variable goes up, so does the other; the line on a graph goes up from left to right
5. Error bars represent the range of values that must be considered as potentially true. For example you measure a pear as 9 cm but the ruler's error is 0.1 cm; the error would mean the "real" size could be anything in the 8.9-9.1 cm range.
6. When comparing two data sets with error bars, you want the error bars to be non-overlapping for the data sets to be considered truly different. If they overlap, there isn't a significant difference in values.
Chillax....!!!!
Spearman's rank:
1. It goes from -1 to +1
2. Zero means there is no correlation between two variables I.e. On a graph, the line of best fit is flat; the data points are scattered without a trend
3. A value close to -1 means a negative correlation, so as a variable goes up, the other goes down; on a graph, a line of best fit goes down from left to right; the closer to -1 the value, for example -0.8, the steeper the line and the stronger the correlation; -0.6 is a stronger correlation than -0.3
4. Same goes for +1 which is a positive correlation; as one variable goes up, so does the other; the line on a graph goes up from left to right
5. Error bars represent the range of values that must be considered as potentially true. For example you measure a pear as 9 cm but the ruler's error is 0.1 cm; the error would mean the "real" size could be anything in the 8.9-9.1 cm range.
6. When comparing two data sets with error bars, you want the error bars to be non-overlapping for the data sets to be considered truly different. If they overlap, there isn't a significant difference in values.
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#4
(Original post by nickymobydicky)
Thanks for replying helped me a bit though i am still quite confused
Thanks for replying helped me a bit though i am still quite confused
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