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Hatshy [7]
2 years ago
14

Which statement correctly describes the difference between an IP address and a MAC address for a

Computers and Technology
1 answer:
Citrus2011 [14]2 years ago
5 0

The statement that correctly describes the difference between an IP address and a MAC address for a  specific device is that the IP address can change, but the MAC address always stays the same.

For better understanding, let us explain what the  IP address and a MAC address means

  • IP address simply mean Internet protocol address , it is a distinct string of numbers that is often separated by full stops that shows each computer using the Internet Protocol to link or communicate over a network.
  • MAC address is simply regarded as a A distinct number also as it shows a device or computer that is linked or connected to the internet.
  • One of the major difference between a MAC address and an IP address? is that the IP address gives the location of a device on the internet but the Mac address identifies the device connected to the internet.  

from the above, we can therefore say that the answer The statement that correctly describes the difference between an IP address and a MAC address for a  specific device is that the IP address can change, but the MAC address always stays the same, is correct

learn more about  IP address and a MAC address  from:

brainly.com/question/6839231

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Using these Web sites for guidance, write a definition in your own words for five of the terms listed below.
Feliz [49]

Answer:

Gigabyte: Unit of measurement, is 10^9 bytes.

Intranet: Network that is used for sharing info, and other services.

Pixel: Creates a image, it is the smallest element on a display on a device.

Modem: Converts signals produced by a computer or another type of device.

Digital: Problem solving by any capable class device.

Explanation:

Short and simple.

7 0
3 years ago
Functions of barriers include (mark all that apply): A. Define boundaries B. Design layout C. Deny access D. Delay access
gayaneshka [121]

Answer:

A. Define boundaries

C. Deny access

D. Delay access

Explanation:

A barrier is a material or structure used to prevent or block access. Barriers can either be natural or structural and are used for many purposes usually for security reasons. The following are functions of barriers either natural or structural:

  1. Define areas of boundaries
  2. Delay or slow access. Example is the use of speed bumps to slow down vehicles.
  3. Provide access to entrances such as the use of gates
  4. Deny access to unauthorized personnel and allowing authorized personnel.
4 0
3 years ago
which is the quickest way to change the font color in multiple, randomly located cells in a worksheet?
insens350 [35]
First select or highlight the rows or columns that you want to change the font color<span>. You can hold the ctrl key while select </span>multiple cells<span>. After, selecting you can now </span>change the font color<span> of everything you selected.</span>
6 0
3 years ago
Read 2 more answers
Demote the "Arrive and leave at the same time each day." List item, and then promote the "Respect" list item.
garri49 [273]

Answer:

Dear

<h3>You should wear something professional. A tie, suit, classy dress, heels. You are more likely to be hired if you make it look like you take pride in your appearance. Also, depending on what kind of job you are applying for, the outfits can vary as well.</h3>

Explanation:

Black is too formal for interviews, and earth tones are too casual. Two-button suits are the professional standard.

7 0
3 years ago
In order to consolidate your theoretical knowledge into technique and skills with practical and applicational value, you will us
Arisa [49]

Answer:

Check the explanation

Explanation:

Lasso: R example

To run Lasso Regression you can re-use the glmnet() function, but with the alpha parameter set to 1.

# Perform 10-fold cross-validation to select lambda --------------------------- lambdas_to_try <- 10^seq(-3, 5, length.out = 100) # Setting alpha = 1 implements lasso regression lasso_cv <- cv.glmnet(X, y, alpha = 1, lambda = lambdas_to_try, standardize = TRUE, nfolds = 10) # Plot cross-validation results plot(lasso_cv)

Best cross-validated lambda lambda_cv <- lasso_cv$lambda.min # Fit final model, get its sum of squared residuals and multiple R-squared model_cv <- glmnet(X, y, alpha = 1, lambda = lambda_cv, standardize = TRUE) y_hat_cv <- predict(model_cv, X) ssr_cv <- t(y - y_hat_cv) %*% (y - y_hat_cv) rsq_lasso_cv <- cor(y, y_hat_cv)^2 # See how increasing lambda shrinks the coefficients -------------------------- # Each line shows coefficients for one variables, for different lambdas. # The higher the lambda, the more the coefficients are shrinked towards zero. res <- glmnet(X, y, alpha = 1, lambda = lambdas_to_try, standardize = FALSE) plot(res, xvar = "lambda") legend("bottomright", lwd = 1, col = 1:6, legend = colnames(X), cex = .7)

Kindly check the Image below.

5 0
3 years ago
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