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1 year ago

Which of the following statements about least squares regression analysis is true?

Mathematics

1 answer:

Lemur [1.5K]1 year ago

7 0

The statements about least squares regression analysis is true are I and III.

<h3>What is meant by regression analysis?</h3>

The statistical technique of regression analysis demonstrates the link between two or more variables. The method evaluates the relationship between a dependent variable and independent factors, and is frequently presented as a graph.

You can anticipate the effects of the independent variable on the dependent one by creating a regression analysis. For instance, we can claim that a linear regression model can adequately account for age and height.

Here,

From the given information in the question:

The statements I and III are correct.

And the statements are:

A point with a large residual is an outlier and the removal of an influential point from the data set could change the value of the correlation coefficient.

So, the statements I and III are correct for the given information in the question.

To know more about regression analysis, visit:

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kiruha [24]

Answer:

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Step-by-step explanation:

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3 years ago

How do you write 1.3 × 105 in standard form?

emmainna [20.7K]

Given:

The scientific notation of a number is $1.3\times 10^5$ .

To find:

The standard form of the given number.

Solution:

Standard form: The normal way of writing a number is called standard form. For example: 200, 25600, 1025000 etc.

We have,

$1.3\times 10^5$

It can be written as

$1.3\times 10^5=1.3\times 100,000$

$1.3\times 10^5=130,000$

Therefore, the standard form of the given number is 130,000.

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3 years ago

Simplify the expression below. W^2-9 ----------- w^2-4w-21

loris [4]

(W-3)(w+3)

——————-

(w-7)(w+3)

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3 years ago

The probability is 51% that the sample mean will be between what two values, symmetrically distributed around the population

Alisiya [41]

Answer:

The lower bound is, $-z=-0.69$ and the upper bound is $z=0.69$ .

Step-by-step explanation:

Let the random variable X follows a normal distribution with mean μ and standard deviation σ.

The the random variable Z, defined as $Z=\frac{X-\mu}{\sigma}$ is standardized random variable also known as a standard normal random variable. The random variable $Z\sim N(0, 1)$ .