what can we add as comments on these 2 descriptions

1. As an example, linear regression could be used by a restaurant to study how the number of people seated affects the amount of money made that night. The regression could help the restaurant then predict how much it will make based on how many people they have sealed or will be seating, being productive as it helps the restaurant 2. Another way that linear regression could be used is to find a relationship between the amount of hours studied for a test and the grade you end up getting on the test. By understanding how much study time correlates to a certain grade, you can plan your study time more productively 3. Finally, another way linear regression may be used, is by athletes. They can use it to compare the amount of calories they burned the the duration of their exercise. Then, the athletes can better plan the lengths of their routines according to how many calories they can burn. Linear regression is an attempt to model the relationship between two variables. With these variables there is a dependent variable and an independent variable. One example the linear regression can be used is to analyze risk. A health insurance company can conduct a linear regression by looking at the number of claims per customer and then look at the age of each customer who reports the claim. The company can then determine of older customers report more claims of if younger people do. The next example linear regression can analyze the impact of price changes. If a company changes the price of certain products on several different occasions, it will be able to record the quantity it sells at each price. The company can then perform a linear regression with the quantity sold as the dependent variable and the price the product is sold at as the independent variable. The variables do not always correlate with each other. That means that just because of one variable does not mean the next one is going to match the standards. For an example very high SAT scores do not always mean that someone will always receive very high grades in college. Another example is a persons height and weight. Just because someone weighs a lot does not meant they are going to be tall and vice versa. 1. As an example, linear regression could be used by a restaurant to study how the number of people seated affects the amount of money made that night. The regression could help the restaurant then predict how much it will make based on how many people they have sealed or will be seating, being productive as it helps the restaurant 2. Another way that linear regression could be used is to find a relationship between the amount of hours studied for a test and the grade you end up getting on the test. By understanding how much study time correlates to a certain grade, you can plan your study time more productively 3. Finally, another way linear regression may be used, is by athletes. They can use it to compare the amount of calories they burned the the duration of their exercise. Then, the athletes can better plan the lengths of their routines according to how many calories they can burn. Linear regression is an attempt to model the relationship between two variables. With these variables there is a dependent variable and an independent variable. One example the linear regression can be used is to analyze risk. A health insurance company can conduct a linear regression by looking at the number of claims per customer and then look at the age of each customer who reports the claim. The company can then determine of older customers report more claims of if younger people do. The next example linear regression can analyze the impact of price changes. If a company changes the price of certain products on several different occasions, it will be able to record the quantity it sells at each price. The company can then perform a linear regression with the quantity sold as the dependent variable and the price the product is sold at as the independent variable. The variables do not always correlate with each other. That means that just because of one variable does not mean the next one is going to match the standards. For an example very high SAT scores do not always mean that someone will always receive very high grades in college. Another example is a persons height and weight. Just because someone weighs a lot does not meant they are going to be tall and vice versa