Analysis of distribution
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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

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Analysis of Distribution

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Analysis of distribution

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


Normal distribution curve

Normal Distribution Curve


Analysis of distribution

Task

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 t rout in south taranaki rivers

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

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

Summary Statistics

  • Minimum

  • Lower Quartile

  • Median

  • Upper Quartile

  • Maximum

  • Mean

  • Standard Deviation


Skewness

Skewness


Outliers

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.


Outliers1

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.


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