ŷ= 1.795x +2.195 is the equation for the line of best fit for the data
<h3>How to use regression to find the equation for the line of best fit?</h3>
Consider the table in the image attached:
∑x = 29, ∑y = 74, ∑x²= 125, ∑xy = 288, n = 10 (number data points)
The linear regression equation is of the form:
ŷ = ax + b
where a and b are the slope and y-intercept respectively
a = ( n∑xy -(∑x)(∑y) ) / ( n∑x² - (∑x)² )
a = (10×288 - 29×74) / ( 10×125-29² )
= 2880-2146 / 1250-841
= 734/409
= 1.795
x' = ∑x/n
x' = 29/10 = 2.9
y' = ∑y/n
y' = 74/10 = 7.4
b = y' - ax'
b = 7.4 - 1.795×2.9
= 7.4 - 5.2055
= 2.195
ŷ = ax + b
ŷ= 1.795x +2.195
Therefore, the equation for the line of best fit for the data is ŷ= 1.795x +2.195
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Answer:
the probability of an adult getting a NEG result and truly having tuberculosis is 0.0127
Step-by-step explanation:
S = the adult really has tuberculosis.
S' = complement of S = the adult does not has tuberculosis.
POS = the test gives a positive result
P(S)= 0.05
P(POS | S)=0.746
P(NEG | S')= 0.7653
this is an intersection because the "and" word
P(NEG ∩ S) = P(NEG| S)*P(S)=(1-P(POS | S))*P(S)=(1-0.746)*0.05=0.0127
Answer:
-0.3125
Step-by-step explanation:
Answer:
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Step-by-step explanation:
Answer:
27
Step-by-step explanation:
20=v-7
v=20+7
v=27
Hence, v equals to 27
