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Consider and discuss the required approach to fullanalysis of the data set provided.
As part of this explore also how you would test the hypothesis below and explain the reasons for your decisions. Hypothesis 1: Male children are taller than female children. Null hypothesis; There is no difference in height between male children and female children. Hypothesis 2: Taller children are heavier. Null hypothesis: There is no relationship between how tall children are and how much they weigh.

Analysis of data set
The data set is a list of 30 children's gender, age, height,weight, upper and lower limb lengths, eye colour, like of chocolate or not andIQ.
There are two main things to consider before analysing thedata. These are the types of data and the quality of the data as a sample.
Types of data could be nominal, ordinal, interval or ratio.Nominal is also know as categorical. Coolican (1990) gives more details of allof these and his definitions have been used to decide the types of data in thedata set.
It is also helpful to distinguish between continuousnumbers, which could be measured to any number of decimal places an discretenumbers such as integers which have finite jumps like 1,2 etc.
Gender
This variable can only distinguish between male or female.There is no order to this and so the data is nominal.
Age
This variable can take integer values. It could be measuredto decimal places, but is generally only recorded as integer. It is ratio databecause, for example, it would be meaningful to say that a 20 year old personis twice as old as a 10 year old.
In this data set, the ages range from 120 months to 156months. This needs to be consistent with the population being tested.
Height
This variable can take values to decimal places ifnecessary. Again it is ratio data because, for example, it would be meaningfulto say that a person who is 180 cm tall is 1.5 times as tall as someone 120cmtall. In this sample it is measured to the nearest cm.
Weight
Like height, this variable could take be measured to decimalplaces and is ratio data. In this sample it is measured to the nearest kg.
Upper and lower limb lengths
Again this variable is like height and weight and is ratiodata.
Eye colour
This variable can take a limited number of values which areeye colours. The order is not meaningful. This data is therefore nominal(categorical).
Like of chocolate or not
As with eye colour, this variable can take a limited numberof values which are the sample members pReferences. In distinguishing merelybetween liking and disliking, the order is not meaningful. This data istherefore nominal (categorical).
IQ
IQ is a scale measurement found by testing each samplemember. As such it is not a ratio scale because it would not be meaningful tosay, for example, that someone with a score of 125 is 25% more intelligent thansomeone with a score of 100.
There is another level of data mentioned by Cooligan intowhich none of the data set variables fit. That is Ordinal Data. This means thatthe data have an order or rank which makes sense. An example would be if 10students tried a test and you recorded who finished quickest, 2ndquickest etc, but not the actual time.
The data is intended to be a sample from a population aboutwhich we can make inferences. For example in the hyp本论文由英语论文网提供整理，提供论文代写，英语论文代写，代写论文，代写英语论文，代写留学生论文，代写英文论文，留学生论文代写相关核心关键词搜索。