Lizzy has a total of x dollars in her checking account. She makes a deposit of b dollars cash and

Which r-value represents the strongest correlation?<br> –0.83<br> –0.67<br> 0.48<br> 0.79

maks197457 [2]

Answer:

–0.83

Step-by-step explanation:

An r-value, or correlation coefficient, tells us the strength of the correlation in a linear regression. This number ranges from -1 to 1; -1 is a perfect linear fit for a decreasing set of data, while 1 is a perfect linear fit for an increasing set of data.

The closer the r-value is to either -1 or 1, the stronger the correlation is.

The two negative numbers we have are -0.83 and -0.67. The first one, -0.83, is 0.17 away from -1. -0.67, on the other hand, is 0.33 away from -1. The two positive numbers we have are 0.48 and 0.79. The first one, 0.48, is 0.52 away from 1. The second one, 0.79, is 0.21 away from 1. The one that is closest to the perfect fit is -0.83, since it is only 0.17 away from a perfect fit.

8 0

3 years ago

Read 2 more answers

Cat face x+4/5=45 sa il aflu pe x

love history [14]

Given:

The equation is

$x+\dfrac{4}{5}=45$

To find:

The value of x.

Solution:

We have,

$x+\dfrac{4}{5}=45$

Isolating the variable, we get

$x=45-\dfrac{4}{5}$

$x=\dfrac{225-4}{5}$

$x=\dfrac{221}{5}$

$x=44.2$

Therefore, the value of x is 44.2.

6 0

3 years ago

Given a population with a mean of muμequals=100100 and a variance of sigma squaredσ2equals=3636, the central limit theorem appl

lakkis [162]

Answer:

a) $\bar X \sim N(100,\frac{6}{\sqrt{25}}=1.2)$

$\mu_{\bar X}=100$ $\sigma^2_{\bar X}=1$

b) $P(\bar X >101)=1-P(\bar X$

c) $P(\bar X$

Step-by-step explanation:

Previous concepts

Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".

The Z-score is "a numerical measurement used in statistics of a value's relationship to the mean (average) of a group of values, measured in terms of standard deviations from the mean".

The central limit theorem states that "if we have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population with replacement, then the distribution of the sample means will be approximately normally distributed. This will hold true regardless of whether the source population is normal or skewed, provided the sample size is sufficiently large".

The complement rule is a theorem that provides a connection between the probability of an event and the probability of the complement of the event. Lat A the event of interest and A' the complement. The rule is defined by: $P(A)+P(A') =1$