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Answer:

Sports figures and celebrities have long been viewed as positive role models for many people. On the one hand, they have the power to inspire and motivate others to achieve their goals, as well as provide a sense of belonging to those who look up to them. For example, athletes can demonstrate the importance of hard work, dedication, and discipline needed to succeed in any field. Furthermore, celebrities can show that one can become successful despite their background or circumstances. On the other hand, there are some drawbacks to viewing sports figures and celebrities as role models. For example, they often live extravagant lifestyles and may not be the best examples of how to handle money responsibly. Additionally, their behavior may not be the best representation of the values that should be instilled in young people. Ultimately, while sports figures and celebrities can be inspiring, it is important to remember that they should not be viewed as role models in all aspects of life.

Explanation:

Answer:

Once a year, a college goes on an expedition trip to do charity work abroad. Last year’s trip was to help set up a community garden in South America. This year’s trip will be to Kenya. The trips are expensive, but students can save a lot of money by working together and doing fundraising events.

The first indication for Franz that a major conflict is developing include the following: C) Prussian soldiers are drilling along his route to school.

A conflict can be defined as any form of misunderstanding, disagreement, or struggle that arises between two (2) or more parties such as the members of a team, employees, etc., especially due to any of the following reasons;

In this context, we can reasonably infer and logically deduce that the first indication for Franz to know that a major conflict is developing would be an information which states that the Prussian soldiers are drilling along the route to Franz's school.

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The error variance can be Illustrated as σ^2 = E(μ^2) = Var(μ) = (1/n) * ∑(y - (b0 + b1x))^2

The simple linear regression model is of the form:

y = b₀ + b₁x + μ

y = the dependent variable

x = the independent variable

b₀ and b₁ = the parameters of the model, and μ is the error term.

The error term represents the difference between the observed value of the dependent variable and the value predicted by the model. In other words, it is the deviation of the observed y from the expected value of y, given the value of x.

The error variance, denoted by σ^2, is a measure of the spread of the error term. It is calculated as the average squared deviation of the error term from its mean value, which is zero:

σ^2 = E(μ^2) = Var(μ) = (1/n) * ∑(y - (b₀ + b₁x))^2

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Complete question

Given the simple linear regression model y=b0+b1xi+µ, derive the error variance.