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src="/qa/attachment/10691221/" alt="Screen Shot 2019-10-01 at 2.40.55 PM.png" />Screen Shot 2019-10-01 at 2.41.28 PM.pngScreen Shot 2019-10-01 at 2.46.07 PM.pngScreen Shot 2019-10-01 at 2.46.19 PM.pngScreen Shot 2019-10-01 at 2.46.34 PM.pngScreen Shot 2019-10-01 at 2.46.45 PM.pngScreen Shot 2019-10-01 at 2.44.45 PM.pngScreen Shot 2019-10-01 at 2.45.01 PM.pngThese are R programming based data. How can I make an executive summary using these datas?

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A software company is developing a new computer game. The manager wants to see what effect
different strategy settings used by the computer opponent in the game have on the length of time
players survive in the game. Three strategies are programmed: Active hunting: where the player is
actively hunted by computer enemies; Patrol and pursue: where enemies patrol set areas but chase
players if they detect them at medium range; and Random where enemies just wander around at
random and only chase players while they are within a short range of them. This information could
then be used to develop settings so that consecutive levels of the computer game get more difficult.
A large group of play testers is available and each is randomly allocated to play a version of the
game with a different strategy setting. The resulting data is stored in the file "strategy. txt",
which contains the variables:
Time
The survival time for the player (in minutes).
Strategy
The type of strategy used by the computer opponent in the game (either
"ActiveHunt", "PatrolPursue" or "Random").
The manager particularly requested several questions answered:
- Did the different strategies tend to lead to a different survival time?
- Compared to the strategy which, in general, had the longest survival time how did long did the
players tend to survive in the other strategies?
- Compared to the strategy which, in general, had the shortest survival time how did long did the
players tend to survive in the other strategies?

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JU. LUTIV
JU . VJLJU
ITI . VJJTO
Within Groups
177 21. 63946
0. 12226
Total
179 57.74422
Numeric Summary :
Sample size
Mean Median Std Dev Midspread
All Data
180 3.41961 3.44041 0.56797
0. 83678
ActiveHunt
60 2.83860 2.76945 0.38320
0.54790
PatrolPursue
60 3.49174 3.52634 0.32458
0.50000
Random
60 3.92850 3.92683 0.33848
0. 46968
Table of Effects: (GrandMean and deviations from GM)
typ. val
ActiveHunt PatrolPursue
Random
3.41961
-0 .58102
0. 07213
0.50889
Estimate Tukey. L Tukey. U Tukey . p
ActiveHunt -
PatrolPursue -0.6531447 -0. 8040 -0.5023
ActiveHunt -
Random
-1. 0899010 -1.2408 -0.9390
PatrolPursue -
Random
-0.4367562 -0.5876 -0.2859
Tukey . L
Tukey . U
ActiveHunt -
PatrolPursue -55.24648 -39.48628
ActiveHunt
-
Random
-71.08472 -60. 89813
PatrolPursue - Random
-44.43407 -24.86623
Tukey. L
Tukey. U
ActiveHunt - PatrolPursue 123.4461 65.25177
ActiveHunt -
Random
245. 8379 155.74227
PatrolPursue
- Random
79.9664 33. 09594

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Plot of "log (Time)' by levels of Strategy',
with TUKEY intervals (95%, pooled SDs)
log(Time)
2.0 2.5 3.0 3.5 4.0 4.5
I
O
ActiveHunt
PatrolPursue
Random
Strategy

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