1
00:00:06,660 --> 00:00:07,713
Welcome to statistics.

2
00:00:07,713 --> 00:00:14,544
In this module, we will explain how
statistics surround our daily lives.

3
00:00:14,544 --> 00:00:21,010
All we have to do is to think of the
conversations we have on a regular basis.

4
00:00:21,010 --> 00:00:26,501
A day start with this concern about
rain or snow, returned to the weather

5
00:00:26,501 --> 00:00:31,196
channel to see whether it will rain or
snow today or tomorrow.

6
00:00:31,196 --> 00:00:36,361
When the weather channel informs you
that the chance of rain is 35% or

7
00:00:36,361 --> 00:00:40,923
60%, you are essentially relying
on statistical tools and

8
00:00:40,923 --> 00:00:44,366
technologies to come up
with those forecasts so

9
00:00:44,366 --> 00:00:48,430
that you may be better prepared for
either rain or snow.

10
00:00:48,430 --> 00:00:52,053
If you happen to live in a large
city in North America, or

11
00:00:52,053 --> 00:00:56,848
Europe, in East Asia, housing
affordability is likely to be a concern.

12
00:00:56,848 --> 00:01:01,720
And when you hear in the news media
that housing is becoming more expensive

13
00:01:01,720 --> 00:01:06,133
overtime, this analysis is coming
out of statistical analysis.

14
00:01:06,133 --> 00:01:10,852
At the same time you will hear if the
unemployment rate has fallen or has risen

15
00:01:10,852 --> 00:01:15,666
overtime, or how millennials are looking
for jobs that may not be full time.

16
00:01:15,666 --> 00:01:20,627
And when we track those numbers overtime,
we realize we are using statistics.

17
00:01:20,627 --> 00:01:24,321
Statistics are not just
confined to economics.

18
00:01:24,321 --> 00:01:29,317
We appreciate players based on their
performance in which they performance

19
00:01:29,317 --> 00:01:30,652
using statistics.

20
00:01:30,652 --> 00:01:35,292
Millennials and the sharing economy
is perhaps redefining the way we

21
00:01:35,292 --> 00:01:38,580
understand economics and
business these days.

22
00:01:38,580 --> 00:01:42,628
The way millennials have defined their
preferences different from previous

23
00:01:42,628 --> 00:01:44,753
generations is something of interest.

24
00:01:44,753 --> 00:01:47,794
Many say that they would like
to rent than to own a house,

25
00:01:47,794 --> 00:01:51,756
that didn't used to be the case in
the past, but the norms are changing.

26
00:01:51,756 --> 00:01:55,303
Who gets paid, how much?

27
00:01:55,303 --> 00:01:56,939
And not just at your work, but

28
00:01:56,939 --> 00:01:59,833
in the Hollywood is again
coming out of statistics.

29
00:01:59,833 --> 00:02:05,100
Similarly, if you're thinking of pursuing
business analytics as a career, you may be

30
00:02:05,100 --> 00:02:10,029
interested in knowing what is the average
salary of a starting business analyst?

31
00:02:10,029 --> 00:02:13,411
And again, this comes out of statistics.

32
00:02:13,411 --> 00:02:17,377
Any comparison of salary
between two professions,

33
00:02:17,377 --> 00:02:22,161
such as an engineer or
an economist would require statistics.

34
00:02:22,161 --> 00:02:23,816
And if you happen to be in Chicago,

35
00:02:23,816 --> 00:02:27,080
you probably have not missed that
crime has spiked in recent years.

36
00:02:27,080 --> 00:02:31,721
Similarly, we compare crime,
especially violent crime over years, and

37
00:02:31,721 --> 00:02:34,754
this comparison requires
us to use statistics.

38
00:02:34,754 --> 00:02:38,954
So when we say average income,
average age, average height,

39
00:02:38,954 --> 00:02:43,250
we're relying on average,
which is a statistical parameter.

40
00:02:43,250 --> 00:02:49,153
Highest paid athlete,
we're looking at the maximum salary.

41
00:02:49,153 --> 00:02:51,916
Fastest sprinter,
you're looking at the maximum speed.

42
00:02:51,916 --> 00:02:55,865
Lowest unemployment rate
of all the OECD countries,

43
00:02:55,865 --> 00:02:58,595
you're looking at a minimum value.

44
00:02:58,595 --> 00:03:02,870
Percentage of females who study
engineering requires us to compute

45
00:03:02,870 --> 00:03:03,851
percentages.

46
00:03:03,851 --> 00:03:07,394
The chance for
rain tomorrow is in fact likelihood,

47
00:03:07,394 --> 00:03:12,159
and how consistent is a stock
performance over the past three months?

48
00:03:12,159 --> 00:03:16,102
We're concerned about variance,
which again is a statistical parameter.

49
00:03:16,102 --> 00:03:20,910
And then the question of on average,
do men spend more on clothes than women?

50
00:03:20,910 --> 00:03:25,157
We probably would use a T test to
determine this difference, again,

51
00:03:25,157 --> 00:03:26,704
relying on statistics.

52
00:03:26,704 --> 00:03:31,380
If you were to recall your conversations
in a given day, you probably realize now

53
00:03:31,380 --> 00:03:35,396
that you have been using the language
of statistics on a daily basis.

54
00:03:35,396 --> 00:03:36,561
At the same time,

55
00:03:36,561 --> 00:03:42,008
the news media use statistics all the time
to demonstrate how trends are changing.

56
00:03:42,008 --> 00:03:46,717
2016 was the year American
presidential elections were held,

57
00:03:46,717 --> 00:03:52,349
big surprises there between what the polls
forecasted and what the outcome was.

58
00:03:52,349 --> 00:03:57,000
But again, you see these numbers
portrayed in the newspapers.

59
00:03:57,000 --> 00:04:01,938
At the same time, you have other
publications that show you how housing

60
00:04:01,938 --> 00:04:06,891
prices or other development related
statistics vary over countries.

61
00:04:06,891 --> 00:04:10,700
In a nutshell,
the information we consume and

62
00:04:10,700 --> 00:04:16,170
the conversations we have every
day involves a lot of statistics,

63
00:04:16,170 --> 00:04:19,506
so it pays one to learn some statistics.

64
00:04:19,506 --> 00:04:20,006
[MUSIC]