Answer:
See explaination for how to manage her personal risk
Explanation:
Personal risks can be described as anything that exposes you to lose of money. It is often connection to financial investments and insurance.
The basic things She can do to manage her personal risks are:
1. Saving:
Savings in much ways drastically reduces the percentage of risks and help you build confidence. Savings can help Rhonda manage her personal risks as savings helps one become financially secure and provide safety in case of emergency.
2. Investing:
After savings comes the major process, which is investment. It is rightly said, savings without invested proper is vain. Investment not only gives you returns or generates more profits but also ensures present and future long term financial security.
3. Reduce expenses:
A common man's expenses can never finish except it is controlled. Reduction in daily expenses can give a hike in savings and increase return on investment. Prompt planning can help cut in expenses.
Answer:
zcat
Explanation:
Zcat is a command line utility for viewing the contents of a compressed file without literally uncompressing it. It expands a compressed file to standard output allowing you to have a look at its contents. In addition, zcat is identical to running gunzip -c command.
Reject Code 0503<span> indicates that the Spouse's Social Security Number and the first 4 letters of the spouse's last name </span>do<span> not match IRS records. The IRS uses data provided by the Social Security Administration to verify this information. Hope this helps.</span>
Answer:
a. This is an instance of overfitting.
Explanation:
In data modeling and machine learning practice, data modeling begins with model training whereby the training data is used to train and fit a prediction model. When a trained model performs well on training data and has low accuracy on the test data, then we say say the model is overfitting. This means that the model is memorizing rather Than learning and hence, model fits the data too well, hence, making the model unable to perform well on the test or validation set. A model which underfits will fail to perform well on both the training and validation set.
