DataTypes - Revision history

Admin at 09:00, 5 August 2013

2013-08-05T09:00:08Z

Admin at 08:59, 5 August 2013

2013-08-05T08:59:51Z

Admin at 08:59, 5 August 2013

2013-08-05T08:59:19Z

Admin at 08:50, 20 June 2013

2013-06-20T08:50:58Z

Admin at 15:44, 11 April 2013

2013-04-11T15:44:52Z

Admin at 15:30, 11 April 2013

2013-04-11T15:30:41Z

Admin: /* Continuous data */

2013-04-11T15:29:49Z

‎Continuous data

Admin: /* Continuous data */

2013-04-11T15:29:05Z

‎Continuous data

Admin: /* Continuous data */

2013-04-11T15:28:28Z

‎Continuous data

Admin: /* Continuous data */

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‎Continuous data

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 The interesting question is then following: Given the analyst is really interested in the voting intention of ALL voters, how can the sample information be used to learn something about the population? this problem is at heart of the problem of ''statistical inference''.
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	Exchange Rates: [http://www.oanda.com/currency/historical-rates/ Oanda.com] An example for a time-series of an exchange rate, here USD/EUR.		Exchange Rates: [http://www.oanda.com/currency/historical-rates/ Oanda.com] An example for a time-series of an exchange rate, here USD/EUR.
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		+	= Population and Sample Data =
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		+	When dealing with data we will have to be aware of what the difference between population and sample data are. The population represents all the data you are interested in. Imagine you have asked all households in your postcode area for their annual income. In case you are interested in the incomes in your postcode only, then your observations represent the population data. If however, you are really interested in the average household income of ALL households in Manchester, then the data you have collected in your postcode are sample only <ref>And it would be a bad sample at that, as the households in one postcode are unlikely to be representative for all of Manchester.
		+	</ref>. As you can see, the same set of observations can be either a sample or population, it really depends on the question you are asking.
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		+	Given any particular question, we always prefer to have population data, however in most cases one will have to accept that one can only get sample data. Consider, for instance, a political analyst who is interested in the voting intentions of all eligible voters in the UK. As there are many millions voters it is virtually impossible to obtain population data. You will all be familiar with opinion polls who will be based on the basis of a sample of eligible voters (usually somewhere around 1000 voters).
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		+	The interesting question is then following: Given the analyst is really interested in the voting intention of ALL voters, how can the sample information be used to learn something about the population? this problem is at heart of the problem of ''statistical inference''.

	= Footnotes =		= Footnotes =

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 === Additional resources ===
-Khan Academy: [https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/discrete-and-continuous-random-variables: Discrete and Continuous Variables] Discusses example of continuous and discrete random variables.
+Khan Academy: [https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/discrete-and-continuous-random-variables Discrete and Continuous Variables] Discusses example of continuous and discrete random variables.
 == Cross-section data ==

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 == Cross-section data ==
-Cross-section data comprises observations on a particular variable taken at a single point in time. For example: annual crime figures recorded by Police regions for the year 1999; the birth-weight of babies born, in a particular maternity unit, during the month of April 1998; initial salaries of graduates from the University of Manchester, 2000. Note, the defining feature is that there is no natural ordering in the data.
+Cross-section data comprises observations on a particular variable taken at a single point in time. For example: annual crime figures recorded by Police regions for the year 1999; the birth-weight of babies born, in a particular maternity unit, during the month of April 1998; initial salaries of graduates from the University of Manchester, 2012. Note, the defining feature is that there is no natural ordering in the data.
 == Time-series data ==
-On the other hand, time-series data are observations on a particular variable recorded over a period of time, at regular intervals. For example; personal crime figures for Greater Manchester recorded annually over 1980-99; monthly household expenditure on food; the daily closing price of a certain stock. In this case, the data does have a natural ordering since they are measured from one time period to the next.
+On the other hand, time-series data are observations on a particular variable recorded over a period of time, at regular intervals. For example; personal crime figures for Greater Manchester recorded annually from 1980-2012; monthly household expenditure on food; the daily closing price of a certain stock. In this case, the data does have a natural ordering since they are measured from one time period to the next.
 = Footnotes =

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 Other examples of continuous data include: heights of people; volume of water in a reservoir; and, to a workable approximation, Government Expenditure. One could argue that the last of these is discrete (due to the finite divisibility of monetary units). However, when the amounts involved are of the order of millions of pounds, changes at the level of individual pence are hardly discernible and so it is sensible to treat the variable as continuous.
-'''Additional resources''':<br />Khan Academy: [https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/discrete-and-continuous-random-variables Discrete and Continuous Variables] Discusses example of continuous and discrete random variables.
+=== Additional resources ===
 == Cross-section data ==