Welcome to statistics.
In this module, we will explain how statistics surround our daily lives.
All we have to do is to think of the conversations we have on a regular basis.
A day start with this concern about rain or snow, returned to the weather
channel to see whether it will rain or snow today or tomorrow.
When the weather channel informs you that the chance of rain is 35% or
60%, you are essentially relying on statistical tools and
technologies to come up with those forecasts so
that you may be better prepared for either rain or snow.
If you happen to live in a large city in North America, or
Europe, in East Asia, housing affordability is likely to be a concern.
And when you hear in the news media that housing is becoming more expensive
overtime, this analysis is coming out of statistical analysis.
At the same time you will hear if the unemployment rate has fallen or has risen
overtime, or how millennials are looking for jobs that may not be full time.
And when we track those numbers overtime, we realize we are using statistics.
Statistics are not just confined to economics.
We appreciate players based on their performance in which they performance
using statistics.
Millennials and the sharing economy is perhaps redefining the way we
understand economics and business these days.
The way millennials have defined their preferences different from previous
generations is something of interest.
Many say that they would like to rent than to own a house,
that didn't used to be the case in the past, but the norms are changing.
Who gets paid, how much?
And not just at your work, but
in the Hollywood is again coming out of statistics.
Similarly, if you're thinking of pursuing business analytics as a career, you may be
interested in knowing what is the average salary of a starting business analyst?
And again, this comes out of statistics.
Any comparison of salary between two professions,
such as an engineer or an economist would require statistics.
And if you happen to be in Chicago,
you probably have not missed that crime has spiked in recent years.
Similarly, we compare crime, especially violent crime over years, and
this comparison requires us to use statistics.
So when we say average income, average age, average height,
we're relying on average, which is a statistical parameter.
Highest paid athlete, we're looking at the maximum salary.
Fastest sprinter, you're looking at the maximum speed.
Lowest unemployment rate of all the OECD countries,
you're looking at a minimum value.
Percentage of females who study engineering requires us to compute
percentages.
The chance for rain tomorrow is in fact likelihood,
and how consistent is a stock performance over the past three months?
We're concerned about variance, which again is a statistical parameter.
And then the question of on average, do men spend more on clothes than women?
We probably would use a T test to determine this difference, again,
relying on statistics.
If you were to recall your conversations in a given day, you probably realize now
that you have been using the language of statistics on a daily basis.
At the same time,
the news media use statistics all the time to demonstrate how trends are changing.
2016 was the year American presidential elections were held,
big surprises there between what the polls forecasted and what the outcome was.
But again, you see these numbers portrayed in the newspapers.
At the same time, you have other publications that show you how housing
prices or other development related statistics vary over countries.
In a nutshell, the information we consume and
the conversations we have every day involves a lot of statistics,
so it pays one to learn some statistics.
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