MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN: 9781119256830
Author: Amos Gilat
Publisher: John Wiley & Sons Inc
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Find the equation of the least-squares regression line for the data found in Table 1.2. Lastly,
are we able to find a line that outputs a smaller total of the sum of the residuals squared
than the least-squares regression line, why or why not?
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Transcribed Image Text:Find the equation of the least-squares regression line for the data found in Table 1.2. Lastly, are we able to find a line that outputs a smaller total of the sum of the residuals squared than the least-squares regression line, why or why not?
# of days running Weight loss (kg)
1
1
3
2
5
6
7
9
7
4
4
5
Metric
# of days mean
# of days
standard deviation
Weight loss mean
Weight loss
standard deviation
T
Result
5
3
4
2
0.62642
Table 1.2: The relationship between running 10km and losing weight in kg.
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Transcribed Image Text:# of days running Weight loss (kg) 1 1 3 2 5 6 7 9 7 4 4 5 Metric # of days mean # of days standard deviation Weight loss mean Weight loss standard deviation T Result 5 3 4 2 0.62642 Table 1.2: The relationship between running 10km and losing weight in kg.
Expert Solution
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Step 1: Determine the given information

The given paired observations of "days of running" and the "weight loss amount (in kg)" are:

# of days runningWeight loss (kg)
11
23
57
64
74
95

The summarized data is given as:

MetricResult
# of days mean5
# of days standard deviation3
Weight loss mean4
Weight loss standard deviation2
r0.62642

The objective is to obtain the regression equation.


Considering the "amount of weight loss" (in kg) (say Y) is determined as a result of the changes in the "number of running days" (say X), the regression equation is given as: Y=a+bX.



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