Question

A regression was run to determine if there is a relationship
between the happiness index (y) and life expectancy in years of a
given country (x).

The results of the regression were:

y=a+bx a=-1.921 b=0.058 r^{2}=0.753424 r=0.868

(a) Write the equation of the Least Squares Regression line of the
form

y= + x

(b) If a country increases its life expectancy, the happiness index
will

- increase
- decrease

(c) If the life expectancy is increased by 3 years in a certain
country, how much will the happiness index change? Round to two
decimal places.

(d) Use the regression line to predict the happiness index of a
country with a life expectancy of 62 years. Round to two decimal
places.

Answer #1

A regression was run to determine if there is a relationship
between the happiness index (y) and life expectancy in years of a
given country (x).
The results of the regression were:
y=a+bx
a=0.331
b=0.11
r2=0.405769
r=0.637
(a) Write the equation of the Least Squares Regression line of the
form
y= + x
(b) If a country increases its life expectancy, the happiness index
will
increase
decrease
(c) If the life expectancy is increased by 1.5 years in a certain...

A regression was run to determine if there is a relationship
between the happiness index (y) and life expectancy in years of a
given country (x).
The results of the regression were:
y=a+bx
a=0.13
b=0.198
r2=0.436921
r=0.661
(a) Write the equation of the Least Squares Regression line of the
form
y= + x
(b) If a country increases its life expectancy, the happiness index
will
decrease
increase
(c) If the life expectancy is increased by 2.5 years in a certain...

A regression was run to determine if there is a relationship
between hours of TV watched per day (x) and number of situps a
person can do (y).
The results of the regression were:
y=ax+b
a=-1.276
b=24.302
r2=0.779689
r=-0.883
Use this to predict the number of situps a person who watches 3
hours of TV can do (to one decimal place)

A regression was run to determine if there is a relationship
between hours of TV watched per day (x) and number of situps a
person can do (y).
The results of the regression were:
y=ax+b
a=-0.623
b=37.26
r2=0.386884
r=-0.622
Use this to predict the number of situps a person who watches 11.5
hours of TV can do (to one decimal place)

Fit a regression line to the data shown in the chart, and find
the coefficient of correlation for the line.
year, X
0 (1900)
2 (1920)
4 (1940)
6 (1960)
8 (1980)
life expectancy, y
49.8 years
52.1 years
53.7 years
54.9 years
55.9 years
Use the regression line to predict life expectancy in the year
2020, where x is the number of decades after 1900.
Regression line y=0.750x + 50.28
The coefficient of correlation is ?
(Round to three...

19
A regression was run to determine if there is a relationship
between hours of TV watched per day (x) and number of situps a
person can do (y).
The results of the regression were:
y=ax+b
a=-1.082
b=36.749
r2=0.6889
r=-0.83
Use this to predict the number of situps a person who watches 7
hours of TV can do (to one decimal place)
18
The table below shows the number of state-registered automatic
weapons and the murder rate for several Northwestern...

Let x be the age in years of a licensed automobile
driver. Let y be the percentage of all fatal accidents
(for a given age) due to speeding. For example, the first data pair
indicates that 34% of all fatal accidents of 17-year-olds are due
to speeding.
Given: Σx = 329, Σy = 114,
Σx2 = 18,263,
Σy2 = 2582, Σxy =
3988, and r ≈ −0.961.
(c) Find x, and y. Then find the equation of
the least-squares line...

A random sample of nonindustrialized countries was selected, and
the life expectancy in years is listed for both men and women.
Men
45.2
54.3
62.8
53.2
68.4
64.7
Women
44.4
62.3
61.4
55.2
65.1
59.5
The correlation coefficient for the data is =r0.844 and =α0.05.
Should regression analysis be done?
Find the equation of the regression line. Round the coefficients
to at least three decimal places.
y'= a+bx
a=
b=
Find women's life expectancy in a country where men's life...

Let x be the age of a licensed driver in years. Let
y be the percentage of all fatal accidents (for a given
age) due to failure to yield the right of way. For example, the
first data pair states that 5% of all fatal accidents of
37-year-olds are due to failure to yield the right of way.
x
37
47
57
67
77
87
y
5
8
10
15
31
44
Complete parts (a) through (e), given Σx =...

prior to run a regression a least squares line for variables x
and y indicates the slope is 4 units at this point we can
conclude
variable x causes variable y to change
there is a positive relaationship between variables x and y
there may be a relationship between variables x and y
there is no relationship between variables x and y
x-n(100.3. P9x> 96) =
90.9 %
9.1%
80.2%
19.8%

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