X Y 12.39999962 11.19999981 14.30000019 12.5 14.5 12.69999981 14.89999962 13.10000038 16.10000038 14.10000038 16.89999962 14.80000019 16.5 14.39999962 15.39999962 13.39999962 17 14.89999962 17.89999962 15.60000038 18.79999924 16.39999962 20.29999924 17.70000076 22.39999962 19.60000038 19.39999962 16.89999962 15.5 14 16.70000076 14.60000038 17.29999924 15.10000038 18.39999962 16.10000038 19.20000076 16.79999924 17.39999962 15.19999981 19.5 17 19.70000076 17.20000076 21.20000076 18.60000038

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Question

please use python
Following is a small dataset of list price vs. best price for a new GMC pickup truck in  $1000s. The x represents the list price, whereas the y represents the best price values (please see picture). 

a. Determine the Pearson’s correlation coefficient between the list price and best price.  

b. Establish a linear regression relationship between list price and best price.  

c. Based on the relationship you found, determine the best price of a pickup whose list price is 25.2 in $1000s. 

 

X
Y
12.39999962 11.19999981
14.30000019
12.5
14.5 12.69999981
14.89999962 13.10000038
16.10000038 14.10000038
16.89999962 14.80000019
16.5 14.39999962
15.39999962 13.39999962
17 14.89999962
17.89999962 15.60000038
18.79999924 16.39999962
20.29999924 17.70000076
22.39999962 19.60000038
19.39999962 16.89999962
15.5
14
16.70000076 14.60000038
17.29999924 15.10000038
18.39999962 16.10000038
19.20000076 16.79999924
17.39999962 15.19999981
19.5
17
19.70000076 17.20000076
21.20000076 18.60000038
Transcribed Image Text:X Y 12.39999962 11.19999981 14.30000019 12.5 14.5 12.69999981 14.89999962 13.10000038 16.10000038 14.10000038 16.89999962 14.80000019 16.5 14.39999962 15.39999962 13.39999962 17 14.89999962 17.89999962 15.60000038 18.79999924 16.39999962 20.29999924 17.70000076 22.39999962 19.60000038 19.39999962 16.89999962 15.5 14 16.70000076 14.60000038 17.29999924 15.10000038 18.39999962 16.10000038 19.20000076 16.79999924 17.39999962 15.19999981 19.5 17 19.70000076 17.20000076 21.20000076 18.60000038
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