4. The regression line for this bivariate data set is = 19.41 + .17x. What is the residual for the point with an x-value of 68?
x 56 45 68 49 58 63 66 62 y 28 25 34 30 32 36 23 29
1 answer:
Answer:
3.03
Step-by-step explanation:
x y
56 28
45 25
68 34
49 30
58 32
63 36
66 23
62 29
slope 0.174993175
Intercept 19.40977341
is the regression line
As per line, we can find y for x=68 as
For x=68
y actual = 34
y predicted = 30.97
Residual = 34-30.97
= 3.03
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The problem gives you X, so replace X witht he given vaule and solve for y:
y = -0.1x^2 + 4X
X = 40
y = -0.1(40)^2 +4(40)
y = -0.1(1600) + 160
y = -160 + 160
y = 0
Answer:
336
Step-by-step explanation:
Answer:
$1,061
Step-by-step explanation:
2% of 1000 is 20.
If 2% is compounded yearly, then
year one- $1,020
year two- $1,040.40
year three- $1,061.21
Rounded to the nearest whole dollar, Elaine would have $1,061 in her savings account after three years.
Answer:
P(A)=0.55
P(A and B)=P(A∩B)=0.1265
P(A or B)=P(A∪B)=0.7635
P(A|B)=0.3721
Step-by-step explanation:
P(A')=0.45
P(A)=1-0.45=0.55
P(B∩A)=?
P(B|A)=0.23
P(B|A)=(P(A∩B))/P(A)
0.23=(P(A∩B))/0.55
P(A∩B)=0.23×0.55=0.1265
P(A∪B)=P(A)+P(B)-P(A∩B)
=0.55+0.34-0.1265
=0.7635
P(A|B)=[P(A∩B)]/P(B)=0.1265/0.34 ≈0.3721
It's 12x. you just combine both numbers
