Cars' Second Hand Value Experiment
The aim of this experiment is:
§ To observe how differences from car to car effects the second hand
value e.g. colour, make, mileage, engine size length of MOT and the
number of seats.
§ To do this I will display the data in a wide range of graphs and
charts from which I will make comparisons.
§ To select the data in the first place I will use a range of sampling
methods. Systematic sampling an example of this is selecting 10% of
the data by taking every tenth value etc. For this method to work the
data must be arranged in an unbiased way in no particular order
(random). Attribute sampling this is were the data chosen would depend
on a completely different factor e.g. if I want to select the data for
mileage I may use red and blue cars, as this doesn't affect the data
in any way. This has one set back as sometimes the other variable may
have an effect on the data without you knowing but this is a good
sampling method to use as I have lots of sets of data which otherwise
would not be used. Stratified sampling this is were the data is put
into sub groups for example if there are 3 times more cars that are
diesel than petrol there should be 3 times more in the sample. Random
sampling in a random sample every set of data has a chance of being
used to do this data values could be drawn out of a hat or given a
number and select a number at random. Quota sampling this is were the
data used has to be from a certain sub group i.e. Vauxhall. Cluster
sampling the population is divided into small groups called clusters
then one or more of these clusters are selected. Stratified random
sampling this is obtained by separating the data in to appropriate
categories called stratas e.g. by mileage. Then find out what
percentage of all the data Is in each strata then selecting a random
The data we gathered from our analysis are presented in a formal way on the following page.
(Total the number of observations. Summarise the observations (risk and prioritise them in a list due to the final figures )
test. I will test these using a given data set. If I need to take more
cars. I am going to sample all the cars that are two years of age and
A sample of children ranging from 4 to 13 years old are going to be asked to watch a Rainbow Brite video. The children will be randomly picked from a childcare center. To ensure that the children are going to be randomly assigned, the children will range in different age groups. The first group will consist of 4, 6, and 8 year olds. The second group will consist of 10,12, and 14 year olds. It would have to be a field experiment because you have to go out and collect the data.
I will need to take a sample from the population, which is a list of
In the example above, the survey needed not only to be expanded but diversified. By including the women and other workers, you make the statistics more accurate because it represents the TV watching habits of ALL the company’s employees. However, if the company is very large, it would be difficult to interview every single employee. The solution to this problem is called random representative sampling.
Below is a list of all the variables I can consider to use in my
iNZight’s ‘Visual Inferencing Tool’ will be what I use to display using the data. It will present this as a box and whisker graph. I will then analysis the data distribution discussing skews, inter-quartile range, range, shape etc. I will make a first judgement based on what I see being presented. From there I will create a difference between medians bootstrapping confidence interval, this is so I can produce reliable intervals that will potentially provide evidence for my question. iNZight will also be used for all statistics and
To get my primary data I will be using a random sampling. To do this I
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will be able to draw a graph of my results and then use this to work
61/100´30 = 18, so 18 cars out of 100 cars of medium engine cars will
Often uses random sampling to select a large statistically representative sample from which generalizations can be drawn.
3. Selecting a Sampling Technique: Selecting a sampling technique may require a little bit more time and may also involve several decisions, such as whether to use a Bayesian or traditional sampling approach, sample with or without replacement, and use non-probability or probability sampling