Answer the Following Questions:

1. Jackson, even-numbered Chapter Exercises, pp. 335-337.
2. Explain the difference between multiple independent variables and multiple levels of independent variables. Which is better?
3. What is blocking and how does it reduce “noise”? What is a disadvantage of blocking?
4. What is a factor? How can the use of factors benefit a      design?
5. Explain main effects and interaction effects.
6. How does a covariate reduce noise?
7. Describe and explain three trade-offs present in experiments.

Support your paper with a minimum of 5 resources. In addition to these specified resources, other appropriate scholarly resources, including older articles, may be included.

Length: 5-7 pages not including title and reference pages
References: Minimum of 5 scholarly resources.

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ADDITIONAL INFORMATION;

Explain the difference between multiple independent variables and multiple levels of independent variables.

Introduction

Multiple independent variables are also known as factors. Each factor is a unique characteristic or condition. Within each factor are specific levels or groups that define or describe the factor. Each level represents a set of conditions created by each level of the independent variable.

Multiple independent variables are also known as factors.

A factor is a condition or characteristic that can be used to explain the relationship between two or more variables. Factors are used to identify and explain relationships between variables in a statistical analysis.

Factors can be independent, dependent, discrete (quantitative), continuous (qualitative), categorical (ordinal) or any combination of these types of factors. For example: Gender would be an independent variable because it’s something you consider when deciding on your own course of action; Gender also has an effect on whether you’re going to buy clothes from Target or Walmart because these stores cater specifically towards women’s needs so if someone doesn’t like shopping at Target then they won’t go there either way unless they have no choice but shopping at Walmart which caters more towards men’s needs instead

Each factor is a unique characteristic or condition.

The concept of multiple independent variables is often confused with the idea of multiple levels. In this case, each factor is a unique characteristic or condition that can be manipulated independently. However, there are some important differences between the two concepts:

• Independent factors: These are variables that do not depend on each other in any way (e.g., gender and age).

• Dependent variables: These are variables whose values change when one or more independent variables change (e.g., if you were asked how much money you earn today, then today’s income would be a dependent variable).

Within each factor are specific levels or groups that define or describe the factor.

Within each factor are specific levels or groups that define or describe the factor. These levels can be numerical, non-numerical, qualitative and quantitative.

Numerical levels refer to a particular value on a scale (e.g., 0–100). A researcher would define these levels when they use statistical analysis to measure something like health status or age range in their study’s data set; for example: “Those participants with a BMI over 30 were more likely than those with a lower BMI” or “The average age of participants was 59 years old at baseline.”

Non-numerical levels refer to categorical data such as gender, race/ethnicity/culture etc., which may be unknown until after you’ve collected all your information from your sample group(s). Non-numerical classification also includes ordinal variables (e.g., 1st place > 2nd place) whereas numerical variables are nonordered—they don’t follow any specific pattern like this one does! For example: “If I had to rank my purchases today using only cash vs credit card then I’d rank them as follows…

Each level represents a set of conditions created by each level of the independent variable.

The levels of a variable are the different conditions created by each level of the independent variable. For example, in our study of the effects of gender on customer satisfaction and loyalty, we might have five levels: male, female, and so on. Each level represents a set of conditions created by each level of your independent variable (gender). In this case, you could say that men would be considered loyal customers while women would be considered less loyal customers because they have more choices than men do so they’re more likely to seek out new products or services when there are better deals available elsewhere.

The same goes for your independent variables themselves; if one factor has two subfactors labeled “male” and “female” respectively (and therefore has two levels), then those two subfactors will actually represent two different ways in which males vs females differ from one another: maybe we want to measure whether some people prefer spicy food over bland dishes; maybe we want to measure whether some people find it easier than others tasking themselves mentally when asked questions which require them focus intensely on what someone else is saying at maximum volume without interrupting him or herself during his/her speech- and so forth!

A difference between multiple independent variables and multiple levels of independent variables is the distinction between a factor and a level

A difference between multiple independent variables and multiple levels of independent variables is the distinction between a factor and a level.

A factor is any characteristic or condition that affects an outcome in your model. For example, if you have two factors (the weather and how much homework students do), then each one will affect how well your students perform on their assignments. In this case, “weather” is our first factor and “homework” is our second factor. The weather may influence whether or not it rains during the day when students need to finish their writing assignments—but it doesn’t affect how much homework they do themselves! So while both factors play an important role in determining whether students complete their work on time or not (in this case), only one of them has any direct effect over what happens next: raindrops falling from the sky onto desks outside classrooms all across America…

Conclusion

The key takeaway is that multiple independent variables and multiple levels of independent variables are not the same thing. In fact, they are quite different concepts with different purposes and requirements. We hope the information presented here will help you understand these differences better!