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Regression Analysis

Content type
User Generated
Subject
Statistics
School
Strayer Univeristy
Type
Homework
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Part 1
1. Explain the importance of the correlation coefficient in a multiple regression model. Support your
reasoning with an example.
The coefficient of determination also known as R squared is used to describe the nature of the
relationship between variables in a regression model. It measures the linear dependence between two
quantitative variables. If the coefficient of determination is equal to zero, then there is no relationship
between both variables. It also helps to identify multicollinearity in the independent variables. It helps
to identify which predictors are important. For instance, a researcher interested in investigating skin
cancer, uses the following as independent variables used current age, weight, height, profession, and
age of appearance. After developing models, the computed correlation coefficient suggests that there
are some collinearity issues between the variables. It is important to note that in this case of collinearity
in which two or more dependent variables are too related to each other, thereby reducing the validity of
the analysis.
Part 2
Multiple Regression Analysis
Amazon.com has become one of the most successful online merchants. Two measures of its success are sales and
net income/loss figures. The data can be found in the file, Amazon. (Pictured at bottom)
Use Excel to complete the following:
1. Construct a scatter plot for Amazon's net income/loss and sales figures for the period 1995–2015.
y = 240.9x - 1032.3
R² = 0.9321
-4000
-2000
0
2000
4000
6000
8000
10000
12000
0 10 20 30 40 50 60
Income
Sales
Amazon's net income/loss and sales figures for
the period 1995–2015

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Part 1 1. Explain the importance of the correlation coefficient in a multiple regression model. Support your reasoning with an example. The coefficient of determination also known as R squared is used to describe the nature of the relationship between variables in a regression model. It measures the linear dependence between two quantitative variables. If the coefficient of determination is equal to zero, then there is no relationship between both variables. It also helps to identify multicollinearity in the independent variables. It helps to identify which predictors are important. For insta ...
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