Question 4.
Use
simulate_several_key_strikes
1000 times, each time simulating the mon-
key striking 11 keys. Compute the proportion of times the monkey types
"datascience"
, calling
that proportion
datascience_proportion
.

Question 5.
Check the value your simulation computed for
datascience_proportion
. Is your
simulation a good way to estimate the chance that the monkey types
"datascience"
in 11 strikes
(the answer to question 1)? Why or why not?
Question 6.
Compute the chance that the monkey types the letter
"e"
at least once in the 11

strikes. Call it
e_chance
. Use algebra and type in an arithmetic equation that Python can evalute.

Question 7.
Do you think that a computer simulation is more or less effective to estimate
e_chance
compared to when we tried to estimate
datascience_chance
this way? Why or why
not? (You don’t need to write a simulation, but it is an interesting exercise.)

1.3
3. Sampling Basketball Players
This exercise uses salary data and game statistics for basketball players from the 2014-2015 NBA
season. The data was collected from
Basketball-Reference
and
Spotrac
.
Run the next cell to load the two datasets.
4

In [47]:
player_data
=
Table
.
read_table(
'
player_data.csv
'
)
salary_data
=
Table
.
read_table(
'
salary_data.csv
'
)
player_data
.
show(
3
)
salary_data
.
show(
3
)
<IPython.core.display.HTML object>
<IPython.core.display.HTML object>
We would like to relate players’ game statistics to their salaries, so we have computed a table
called
full_data
that includes one row for each player who is listed in both
player_data
and
salary_data
using the table
join
method.
It includes all the columns from
player_data
and
salary_data
, except the
"PlayerName"
column.
In [48]:
full_data
=
player_data
.
join(
"Name"
, salary_data,
"PlayerName"
)
full_data
Out[48]:
Name
| Age
| Team | Games | Rebounds | Assists | Steals | Blocks | Turnove
A.J. Price
| 28
| TOT
| 26
| 32
| 46
| 7
| 0
| 14
Aaron Brooks
| 30
| CHI
| 82
| 166
| 261
| 54
| 15
| 157
Aaron Gordon
| 19
| ORL
| 47
| 169
| 33
| 21
| 22
| 38
Adreian Payne
| 23
| TOT
| 32
| 162
| 30
| 19
| 9
| 44
Al Horford
| 28
| ATL
| 76
| 544
| 244
| 68
| 98
| 100
Al Jefferson
| 30
| CHO
| 65
| 548
| 113
| 47
| 84
| 68
Al-Farouq Aminu | 24
| DAL
| 74
| 342
| 59
| 70
| 62
| 55
Alan Anderson
| 32
| BRK
| 74
| 204
| 83
| 56
| 5
| 60
Alec Burks
| 23
| UTA
| 27
| 114
| 82
| 17
| 5
| 52
Alex Kirk
| 23
| CLE
| 5
| 1
| 1
| 0
| 0
| 0
... (482 rows omitted)
Basketball team managers would like to hire players who perform well but don’t command