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Question: How does the Single Linear Regression differ from Multiple Linear Regression.

27 Oct 2022,5:40 PM

 

  1. Imagine a study for a simple two-group study where you compare elderly and younger participants on their speed of visual judgements. The task involves presenting a block of letters that are either all the same (XXXXX) spread around the screen, or they have one non-match (XYXXX). Describe the study briefly. Answer the questions about it below.

What is your Independent Variable?

What is your Dependent Variable?

Make a general description of the data in this study (what would it mean) if you had a:

Type I error -

Type II error -

  1. Most of the studies in psychology use the p < .05 level. One study of the effectiveness of AIDS drugs decided to use .10.  What would be the advantage and disadvantage of using the .10 level?
  2. When people commonly refer to a correlation procedure, they usually are referring to the Pearson’s Correlation. How does it differ from Spearman’s Correlation procedure in terms of what it is used for and how to interpret the results?
  3. What are the advantages and disadvantages of using the Between Subject and the Within Subject Analysis of Variance?
  4. Replace the question marks with the numbers that belong in this ANOVA summary table for a 1-way, Within Subjects analysis. If you know what you are doing, the math is pretty simple.

 

Source of Variation SS df MS F
Between Groups  ? 3 98.33  ?

Within Groups

 

 40  ?  ?  

Total

 

  ? 19    

 

  1. How does the Single Linear Regression differ from Multiple Linear Regression.
  2. What statistical procedure do you use when you are using an analysis of variance but want to remove the effect of a confounding variable?

Expert answer

 

Single linear regression is a statistical technique that can be used to predict the value of a dependent variable based on the value of a single independent variable. Multiple linear regression is a similar technique that can be used to predict the value of a dependent variable based on the values of multiple independent variables.

 

The main difference between single and multiple linear regression is the number of independent variables that are used in the analysis. Single linear regression uses only one independent variable, while multiple linear regression uses two or more independent variables.

 

Multiple linear regression is generally more accurate than single linear regression, but it is also more complex and requires more data. In some cases, single linear regression may be sufficient for prediction purposes.

 

The Single Linear Regression equation is different from the Multiple Linear Regression equation in a few key ways. For one, the Single Linear Regression equation only has one predictor variable, while the Multiple Linear Regression equation has multiple predictor variables. Additionally, the Single Linear Regression equation does not allow for interaction effects between predictor variables, while the Multiple Linear Regression equation does. Finally, the Single Linear Regression equation is less flexible than the Multiple Linear Regression equation and therefore may not be able to accurately model more complex data sets.

 

As the name suggests, multiple linear regression is a technique that allows us to model and predict relationships between multiple variables. Single linear regression, on the other hand, only allows us to consider one predictor variable at a time.

 

Multiple linear regression is generally more accurate than single linear regression, but it can be more difficult to interpret the results. In some cases, you may find that a multiple linear regression model fits the data better than a single linear regression model.

 

It's important to remember that multiple linear regression is only suitable for predicting quantitative outcome variables. If you're interested in predicting a qualitative outcome variable (e.g. whether or not someone will vote for a particular candidate), you'll need to use a different approach altogether.

 

 

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