1. a) b) c) d) e) f) The scores for players' first and second games at a bowling tournament are shown in the table. Answer the following questions. First Game 169 150 202 230 187 177 164 Create a scatter plot for the data. Determine the correlation coefficient. Determine an equation to model the data. Identify any outliers. Second Game 175 162 195 241 185 235 171 If there are any outliers, remove them and repeat parts b and c. A player scores 250 in the first game. Use both models (from part c and e) to predict this player's score in the second game. How far apart are the two predictions?
1. a) b) c) d) e) f) The scores for players' first and second games at a bowling tournament are shown in the table. Answer the following questions. First Game 169 150 202 230 187 177 164 Create a scatter plot for the data. Determine the correlation coefficient. Determine an equation to model the data. Identify any outliers. Second Game 175 162 195 241 185 235 171 If there are any outliers, remove them and repeat parts b and c. A player scores 250 in the first game. Use both models (from part c and e) to predict this player's score in the second game. How far apart are the two predictions?
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Transcribed Image Text:1.
a)
b)
c)
d)
e)
f)
g)
The scores for players' first and second games at a bowling tournament are shown in the
table. Answer the following questions.
First Game
169
150
202
230
187
177
164
Create a scatter plot for the data.
Determine the correlation coefficient.
Determine an equation to model the data.
Identify any outliers.
Second Game
175
162
195
241
185
235
171
If there are any outliers, remove them and repeat parts b and c.
A player scores 250 in the first game. Use both models (from part c and e) to predict this
player's score in the second game. How far apart are the two predictions?
Explain why identifying outliers is important when determining relationships in data.
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