According to 2025 survey data, approximately 40-50% of people aged 18-58 use a dating app to meet romantic partners.

Alex and Chris have known each other for many years.  They have shared a lot of secrets and supported each other through many ups and downs.  They talk about their dreams of the future and are committed to staying in each other’s lives.  Based on these characteristics, Sternberg would identify their connection as 

Which of the following refers to all monetary payments in addition to all goods or commodities used in lieu of money to reward employees?

Fill in the Blanks   An argument that starts with a fact (or observation) and ultimately concludes that the fact counts as evidence for the best explanation of that fact is known as a(n) __________________.

True/False   If the conclusion of an argument is true, then it is a good argument.

True/False   Modus ponens and modus tollens are forms of reasoning in which the premises entail the conclusion.

True/False   A good argument is one in which the premises make it rational to believe the conclusion with the same confidence that the person advancing the argument intends the conclusion to be accepted.

Fill in the Blanks   A(n) __________________ premise or conclusion is one that is indicated or suggested without being explicitly stated.

Complete the sentences with the correct preposition and form of the verb in parentheses.  Example: Are you worried about paying (pay) back your student loans? Here is some useful advice that can help you be successful (pay)  —–off your loans quickly. You should concentrate (make)  —–your monthly payments, rather than looking at the total amount you owe. The way he deals with money, he should be warned (hurt)  —–his credit score. If you are worried (forget)  —–to make a payment, set up automatic payments from your bank. Remember that you are responsible (let)  —–the lending bank know when you move. Start saving money now if you dream (retire)  —–early.

You have an integer column for a person’s preferred data science role with different numbers representing different roles (e.g., 1=Data scientists, 2=Data Analyst, 3=Data Engineer, and so on). To use such a column as a feature(s) in an MLlib prediction model (say salary prediction), what kind of transformation, if any, is needed?