For the following paired data, (1) Calculate the correlation coefficient, r, and (2) at the 0.05 significance level, determine whether there exists a linear correlation. x | 2 4 5 6y | 6 9 8 10

Linear correlation exists

Step-by-step explanation:

Given the data :

X : | 2 4 5 6

Y : | 6 9 8 10

Using technology to fit the data and obtain the correlation Coefficient of the regression model,

The Correlation Coefficient, r is 0.886

To test if there exists a linear correlation :

Test statistic :

T = r / √(1 - r²) / (n - 2)

n = number of observations

T = 0.886 / √(1 - 0.886²) / (4 - 2)

T = 0.866 / 0.3535845

T = 2.449

Comparing Pvalue with α

If Pvalue < α ; Reject H0

Pvalue = 0.1143

α = 0.05

Pvalue > α ; We reject the null and conclude that linear correlation exists

idk im srry

step-by-step explanation:

geometric series converge the common ratio r has magnitude less than one.

which is equivalent to

for the example we need

|2-x| < 1

-1 < 2-x   and 2-x < 1

x < 3 and x > 1

we can write that

answer: 1 < x < 3

1a. common ratio r=250/25=10 > 1     diverges

1b.   r=50/100=1/2 converges

when these converge they converge to

where a is the first term.

answer: 200

1c. r=-1   so it's not the case that |r| < 1.     diverges

2a. x is the common ratio so we converge when |x|< 1

2b.   r=-10x / -2 = 5x.

we converge when

|5x| < 1

|x| < 1/5

2c.   r=3-2x

-1 < 3-2x and 3-2x < 1

-4 < -2x and 2 < 2x

x < 2 and 1 < x

1 < x < 2

2d. [/tex] r=-48x^3/12 = 4x^3[/tex]

|4x^3| < 1

|x^3| < \frac 1 4

|x| < 4^{-1/3}