(b) (c) Table Q4 shows sample of flower dataset. Flower Name Table Q4: Sample of Flower Dataset Age (years) Iris Cristata 4 Scaevola Aemula 6 Ficus lyrata 5 Mass (kg) 0.188 0.426 1.456 Height (cm) ): 16.3 49.2 61.7 Based on Table Q4, Sarah wants to understand what the age of the plants tends to be when they haven't yet sprouted and have a height of 0 cm. She decides to construct a PERCENT_CONF (% confidence interval) for the true intercept of the regression line between age and height by bootstrapping the regression line 5,000 times. To assist with this, she first creates a function called get intercept, which takes in a table and two column names as input and returns the intercept of the least squares regression line. She then writes the partially code in Figure Q4(b) to create the confidence interval for the intercept (as an array). Fill in the blanks with the appropriate codes. intercepts make_array() for i in np.arange ( BOOT_TBLNAME - plants.sample (with_replacement-True) boot_intercept get intercept (_ np.append(intercepts, boot intercept) left percentile ((100-PERCENT_CONF)/2, intercepts) right percentile ((100+PERCENT_CONF)/2, intercepts) make array( Explain THREE (3) Big Data challenges. Flower (Yes 1, No=0) T Figure Q4(b) 0 1 I 'Height')
(b) (c) Table Q4 shows sample of flower dataset. Flower Name Table Q4: Sample of Flower Dataset Age (years) Iris Cristata 4 Scaevola Aemula 6 Ficus lyrata 5 Mass (kg) 0.188 0.426 1.456 Height (cm) ): 16.3 49.2 61.7 Based on Table Q4, Sarah wants to understand what the age of the plants tends to be when they haven't yet sprouted and have a height of 0 cm. She decides to construct a PERCENT_CONF (% confidence interval) for the true intercept of the regression line between age and height by bootstrapping the regression line 5,000 times. To assist with this, she first creates a function called get intercept, which takes in a table and two column names as input and returns the intercept of the least squares regression line. She then writes the partially code in Figure Q4(b) to create the confidence interval for the intercept (as an array). Fill in the blanks with the appropriate codes. intercepts make_array() for i in np.arange ( BOOT_TBLNAME - plants.sample (with_replacement-True) boot_intercept get intercept (_ np.append(intercepts, boot intercept) left percentile ((100-PERCENT_CONF)/2, intercepts) right percentile ((100+PERCENT_CONF)/2, intercepts) make array( Explain THREE (3) Big Data challenges. Flower (Yes 1, No=0) T Figure Q4(b) 0 1 I 'Height')
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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Transcribed Image Text:(b)
(c)
Table Q4 shows sample of flower dataset.
Flower
Name
Table Q4: Sample of Flower Dataset
Age
(years)
Iris Cristata
4
Scaevola Aemula 6
Ficus lyrata
5
Mass
(kg)
0.188
0.426
1.456
Height
(cm)
):
16.3
49.2
61.7
intercepts make_array()
for i in np.arange (_
plants.sample (with_replacement=True)
boot intercept-get_intercept (
BOOT_TBLNAME
= np.append(intercepts, boot_intercept)
left percentile ((100-PERCENT CONF)/2, intercepts)
right percentile ((100+PERCENT_CONF)/2, intercepts)
make array(
Explain THREE (3) Big Data challenges.
Flower
(Yes 1, No=0)
Based on Table Q4, Sarah wants to understand what the age of the plants tends
to be when they haven't yet sprouted and have a height of 0 cm. She decides to
construct a PERCENT_CONF (% confidence interval) for the true intercept of the
regression line between age and height by bootstrapping the regression line
5,000 times. To assist with this, she first creates a function called get intercept,
which takes in a table and two column names as input and returns the intercept
of the least squares regression line. She then writes the partially code in Figure
Q4(b) to create the confidence interval for the intercept (as an array). Fill in the
blanks with the appropriate codes.
Figure Q4(b)
0
1
1
'Height')
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