All categories

3 years ago

In which of the following situations is having a good credit score important?

1 answer:

8 0

<span>The correct answer is to be able to loan from banks. Having a good credit score, impacts one's approval for bank loans. It will enable a person to have a higher chance of getting his or her loan, approved by the banks, than those who have low credit scores. </span>

You might be interested in

Select the correct answer.

Softa [21]

Answer:

D. smart phone

explanation;

a. what the heck is a radio going to do?

b. an HD television? is his job watching movies?

d. he already has a form of communication he uses, he needs something that can to both things at the same time

c. a smart phone can do everything he needs to do, likely faster than a landline phone or laptop.

5 0

2 years ago

You are part of the team to implement new software at XYZ Inc. The employees at XYZ Inc. trust the results of the old software p

sasho [114]

Answer:

Option (D) i.e., Parallel start up is correct option to the following question.

Explanation:

Parallel startup is also an application or software or a way of using the application of the other Operating System in others just like that the following XYZ inc. has trust on older platform but their system give them cautions for update then they use parallel set up by which they also use old software, as well as they, use the Mac's software in Windows. This strategy provides both the option of usage.

8 0

3 years ago

In most programming languages, the compiler carries a preprocessing step to determine if certain statements will compile. For in

MakcuM [25]

Answer:

See explaination for program code

Explanation:

package com.company;

import java.io.IOException;

import java.nio.file.Files;

import java.nio.file.Paths;

import java.util.Stack;

* A program which reads input from given text filename and prints

* whether it passed the pre-processing stage or not

* Sample input

* int A = (a + b);static void display(int x)

* {

* //A sample input file

* }

* */

public class MyPreprocessor {

* Reads input from filename and returns a string

* containing whole text

* */

public String readInput(String filename) {

String content = "";

try {

content = new String(Files.readAllBytes(Paths.get(filename)));

} catch (IOException e) {

e.printStackTrace();

}

return content;

}

/**

* Filters input since we are only concerned with

* a subset of input = '{}[]()/*'

public String filterText(String text) {

String output = "";

for(int i = 0;i<text.length();++i) {

switch (text.charAt(i)) {

case '[':

case ']':

case '{':

case '}':

case '(':

case ')':

case '*':

case '/':

output += text.charAt(i);

}

return output;

}

* Uses stack to determine if input is valid or not

* */

public boolean parse(String input) {

Stack<Character> St = new Stack<Character>();

// '$' is special symbol to represent bottom of stack

St.push('$');

for(int i = 0;i<input.length();++i) {

Character symbol = input.charAt(i);

if(St.peek().equals(getOpenSymbol(symbol)))

St.pop();

else St.push(symbol);

}

return St.peek() == '$';

}

private static Character getOpenSymbol(Character symbol) {

switch (symbol) {

case '}': return '{';

case ']': return '[';

case ')': return '(';

case '*': return '*';

case '/': return '/';

default: return ' ';

}

public static void main(String[] args) {

MyPreprocessor preprocessor = new MyPreprocessor();

String inputText = preprocessor.readInput("src/input.txt");

String filteredInput = preprocessor.filterText(inputText);

if(preprocessor.parse(filteredInput))

System.out.println("preprocessing passed.");

else System.out.println("preprocessing failed!");

}

6 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

2 years ago

For what reason can security risks never be fully eliminated?

raketka [301]

Answer:

A vulnerability level of ZERO can never be obtained since all countermeasures have vulnerabilities themselves. For this reason, vulnerability can never be zero, and thus risk can never be totally eliminated.

Explanation:

also we need to be protected from scams and stuff

7 0

2 years ago