Mathematics, 14.01.2020 17:31 blake2001

Table below shows the age in years and the retail value in thousands of dollars of a random sample of ten automobiles of the same make and model.
x 2 3 3 3 4 4 5 5 5 6
y 28.7 24.8 26.0 30.5 23.8 24.6 23.8 20.4 21.6 22.1

a) construct the scatter diagram.
b) compute the linear correlation coefficient r. interpret its value in the context of the problem.
c) compute the least squares regression line. plot it on the scatter diagram.
interpret the meaning of the slope of the least squares regression line in the context of the problem.
d) suppose a four-year-old automobile of this make and model is selected at random. use the regression equation to predict its retail value.
e) suppose a 20 -year-old automobile of this make and model is selected at random. use the regression equation to predict its retail value. interpret the result.
f) comment on the validity of using the regression equation to predict the price of a brand new automobile of this make and model.

Answers: 2

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Answer from: hilzepesqtatiana

Step-by-step explanation:

Hello!

Given the study variables

X: Age of an automobile (years)

Y: Retail value of an automobile (thousands of dollars)

a) Construct a scatter diagram.

(attached image)

b) Compute the linear correlation coefficient r. Interpret its value in the context of the problem.

The formula for the correlation coefficient (Person) is:

$r= \frac{sum(XY)-\frac{sum(X)*sum(Y)}{n} }{[sum(X^2)-\frac{(sum(X))^2}{n}] * [sum(Y^2)-\frac{(sum(Y))^2}{n} ] }$

First you have to do some intermediate summatories:

∑x= 40

∑x²= 174

∑y= 246.30

∑y²= 6154.15

∑xy= 956.5

$r= \frac{956.5-\frac{40*246.3}{10} }{[147-\frac{(40)^2}{10}] * [6154.15-\frac{(246.3)^2}{10} ] }$

r= -0.818 ≅ 0.82

-0.82 indicates there is a strong negative linear correlation between the age of the automobiles and the retail price. Wich means that the older the car, the less it costs.

c) Compute the least squares regression line. Plot it on the scatter diagram.

Interpret the meaning of the slope of the least-squares regression line in the context of the problem.

The least squares regression line is Y= a + bX

To calculate the regression line you need to calculate the values of "a" and "b"

$a= (\frac{sum(Y)}{n} ) -b(\frac{sum(X)}{n} )$

$a= (\frac{246.3}{10} ) -(-2.05)(\frac{40}{10} )$

a= 32.83

$b= \frac{sum(XY)-\frac{sum(X)*sum(Y)}{n} }{sum(X^2)-(\frac{sum(X)^2}{n})}$

$b= \frac{956.5-\frac{40*246.3}{10} }{174-(\frac{40^2}{10})}$

b= -2.05

Y= 32.83 - 2.05X

Check attachment for graphic.

-2.05 $(\frac{years}{thousanfs of dollars})$ is the modification of the estimated mean of the retail value of automobiles every time their age increases one year. In other words, every time the automobiles increase their age in one year, the price decreases 2.05 thousands of dollars.

e) Suppose a 20 -year-old automobile of this make and model is selected at random. Use the regression equation to predict its retail value. Interpret the result.

You need to calculate the value of Y for a value of X of 20 years. To do so you replace the values in the regression equation:

Y= 32.83 - 2.05(20)

Y= -8.17 thousands of dollars.

Under the model Y= 32.83 - 2.05X, a 20-year-old automobile has a retail price of -8.17 thousands of dollars.

This is mathematically correct even if it doesn't make sense in context.

f) Comment on the validity of using the regression equation to predict the price of a brand new automobile of this make and model.

You can only use the regression equation to predict possible values of Y for values of X that are between the range of definition of the variable. In this case, the variable is defined in numbers from 2 to 6 years.

You can predict values outside the range of definition of X but the further these values are from the definition range, the less accurate and imprecise the prediction is.

For example in question e) I've predicted the value of Y for X= 20 years, although I've obtained a result truth is that this prediction is not accurate nor reliable.

I hope it helps!

Table below shows the age in years and the retail value in thousands of dollars of a random sample o

Answer from: Quest

step-by-step explanation:

4227473658779875563886538705958641728563470543621287463824132573847969p05749365273847906970680574936214

Iam stuck on this problem and am having trouble solving it. dies anyone know the steps to solving it

Answer from: Quest

step-by-step explanation:

i found the life tables for the year 2012.

life table of males at age 30. probability of dying between ages x and x+1

total population:

29–30 . . . . . . . . . . . . 0.001030

30–31 . . . . . . . . . . . . 0.001061

males:

29–30 . . . . . . . . . . . . 0.001420

30–31 . . . . . . . . . . . . 0.001448

white population

29–30 . . . . . . . . . . . . 0.001005

30–31 . . . . . . . . . . . . 0.001037

white males

29–30 . . . . . . . . . . . . 0.001372

30–31 . . . . . . . . . . . . 0.001399

non hispanic white population

29–30 . . . . . . . . . . . . 0.001083

30–31 . . . . . . . . . . . . 0.001116

non hispanic white males

29–30 . . . . . . . . . . . . 0.001477

30–31 . . . . . . . . . . . . 0.001503

jacob, non hispanic white male. 30 years old.

probability that jacob lives to be 30 years old.

since the given probabilities are probabilities of dying, i think we simply subtract it from 1 to get the probability of living. we will use the probability of non hispanic white male.

probability of living to be 30 y.o.

29 - 30 : 1 - 0.001477 = 0.998523

30 - 31 : 1 - 0.001503 = 0.998497