Analysis of Distribution

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# Analysis of Distribution - PowerPoint PPT Presentation

Analysis of Distribution. If the sample is truly random and there is no bias in the sampling then the expected distribution would be a smooth bell-shaped curve. However, factors can enter the sampling to affect the shape of the distribution curve. Population. Sample. Random Sample

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Analysis of Distribution

If the sample is truly random and there is no bias in the sampling then the expected distribution would be a smooth bell-shaped curve. However, factors can enter the sampling to affect the shape of the distribution curve.

Population

Sample

Random Sample

Sample size > 30 for each sub-group

Each sub-group has

Equal numbers of individuals

In this topic you will be trying to compare the sample distributions of two subgroups taken randomly form a population to determine whether there is enough evidence to answer you question and whether the sample trends will occur in the population also!

Population

Sample

Random Sample

Sample size > 30 for each sub-group

Each sub-group has

Equal numbers of individuals

Mass Of Trout in South Taranaki Rivers

Kaupokanui River

Waingongoro River

F

R

E

Q

E

N

C

Y

%

F

R

E

Q

E

N

C

Y

%

Mass In Grams

Mass In Grams

Describing Feature of the Distribution
• Clusters: Concentration of data around specific

values

• Skewness: When the Median and Mean are

not aligned

• Outliers: Values that lie outside the

boundaries of the distribution

Summary Statistics
• Minimum
• Lower Quartile
• Median
• Upper Quartile
• Maximum
• Mean
• Standard Deviation
Outliers
• An outlier is an observation that lies an abnormal distance from other values in a random sample from a population. In a sense, this definition leaves it up to the analyst (or a consensus process) to decide what will be considered abnormal. Before abnormal observations can be singled out, it is necessary to characterize normal observations.
Outliers
• An outlier is an observation that lies an abnormal distance from other values in a random sample from a population. In a sense, this definition leaves it up to the analyst (or a consensus process) to decide what will be considered abnormal. Before abnormal observations can be singled out, it is necessary to characterize normal observations.