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Introduction- Using Measures to Describe Data

Course video 10 of 59

This week we will describe and summarize the information in the data using numerical values or measures that are able to summarise information. This is a crucial extension to the analysis of the previous week. While graphs are informative it is usually crucial for improved understanding of the data at hand to discuss their numerical properties. In this week, we will look at a range of measures, such as measures of central tendency, the range, variance, standard deviation, and so on.

Statistics for International Business

University of London

Course 4 of 6 in the International Business Essentials Specialization

This course introduces core areas of statistics that will be useful in business and for several MBA modules. It covers a variety of ways to present data, probability, and statistical estimation. You can test your understanding as you progress, while more advanced content is available if you want to push yourself. This course forms part of a specialisation from the University of London designed to help you develop and build the essential business, academic, and cultural skills necessary to succeed in international business, or in further study. If completed successfully, your certificate from this specialisation can also be used as part of the application process for the University of London Global MBA programme, particularly for early career applicants. If you would like more information about the Global MBA, please visit https://mba.london.ac.uk/. This course is endorsed by CMI

Taught By

George Kapetanios
Professor of Finance and Econometrics

Transcript

[MUSIC]

Hello everybody and welcome to the second week of the statistics for

international business mooc.

This week, we're going to introduce you

to data description using statistics and numbers.

In week one we have discussed how the information in the data

may be presented using graphs.

And also discussed the fact that there are many different kinds of graphs

that can in principle be used.

Examples include bar charts, pie charts and frequency distribution.

This week we described and summarized the information

in the data in our sample using numerical values or measures.

These are also able to summarize information.

This is a crucial extension to the analysis of the previous week.

While graphs are informative, it is usually crucial for

improved understanding of the data in hand to discuss their numerical properties.

Policymakers and business leaders are keen to have measures

that describe the current state of the economy or their business.

A major issue for policymakers, business leaders, researchers, and economists,

is whether data in their sample is located near a particular value.

So this week, we're going to present how to calculate such numerical measures.

These measures are the mean, the median and the mode.

They help us,

in answering questions relating to the location of the center of the data.

Now, apart from these measures known as measures of central tendency, sometimes.

We also ask questions that concern the position

of a particular data point relative to the entire set of our data.

We can answer this kind of question by looking at measures of locations such as

the z scores and the percentiles.

Now these examine the location or

position of value in our data set relative to the mean of the distribution.

This discussion makes clear that measures of central tendency on their own, may not

be able to provide a full and sufficient description of the data in our sample.

Hence, in this lecture we are also going to present measures that concern

the very ability or the spread of the data with respect to the mean value.

These include the range, the variance, the standard deviation, and

finally the coefficient of variation.

Thank you very much.

[MUSIC]

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