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Multiple Linear Regression Model

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Statistics
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Grand Canyon University
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Running head: Multiple Linear Regression Model
Multiple Linear Regression Model
STUDENTS NAME
INSTITUTIONAL AFFILIATION
PROFESSORS NAME
DUE DATE

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Multiple Linear Regression Model
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Multiple Linear Regression Model
Multiple linear regression model is basically a statistical approach that make use of several
independent or explanatory variables to predict the outcome of the response or the dependent
variable, (Marill, K. A. (2004). The main aim of multiple linear regression (MLR) is to model
the relationship between the explanatory (independent) variables and response (dependent)
variable in a linear manner, (Islam, M. Q., & Tiku, M. L. (2005).
The general Multiple linear regression model is defined as;
yi=β0+β1xi1+β2xi2+...+βpxip+ϵ
where, for i=n observations:
yi=dependent or the response variable
xi=explanatory or independent variables
β0=y-intercept (constant term)
βp=slope coefficients for each explanatory variable
ϵ=the model’s error term (also known as the residuals)
We use The Johnny Ho Automotive Company as the name of our business in this case. The
Johnny Ho Automotive Company is a business organization dealing with the sales or distribution
of automotive inventory. To meet the market demand gap, The Johnny Ho Automotive Company
make use of Multiple Regression analysis to forecast future demand of the supplies,( Uyanık, G.
K., & Güler, N. (2013).
The business deal with supplies Auto body parts, Engine components and Suspensions.

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1 Running head: Multiple Linear Regression Model Multiple Linear Regression Model STUDENTS NAME INSTITUTIONAL AFFILIATION PROFESSORS NAME DUE DATE Multiple Linear Regression Model 2 Multiple Linear Regression Model Multiple linear regression model is basically a statistical approach that make use of several independent or explanatory variables to predict the outcome of the response or the dependent variable, (Marill, K. A. (2004). The main aim of multiple linear regression (MLR) is to model the relationship between the explanatory (independent) variables and response (dependent) variable in a linear manner, (Islam, M. Q., & Tiku, M. L. (2005). The general Multiple linear regression model is defined as; yi=β0+β1xi1+β2xi2+...+βpxip+ϵ where, for i=n observations: yi=dependent or the response variable xi=explanatory or independent variables β0=y-intercept (constant term) βp=slope coefficients for each explanatory variable ϵ=the model’s error term (also known as the residuals) We use The Johnny Ho Automotive Company as the name of our business in this case. The Johnny Ho Automotive Company is a business organization dealing with the sales or distribution of automotive inventory. To meet the market demand gap, The Johnny Ho Automotive Company make use of Multiple Regression analysis to forecast future demand of the supplies,( Uyanık, G. K., & Güler, N. (2013). The business deal with supplies Auto body parts, Engine components and Suspensions. Multiple Linear Reg ...
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