Demand for oil changes at Garcia’s Garage has been asfollows:Month Number of Oil ChangesJanuary 41February 46March 57April 52May 59June 51July 60August 62a. Use simple linear regression analysis to develop a fore-casting model for monthly demand. In this application,the dependent variable, Y, is monthly demand and theindependent variable, X, is the month. For January, letX = 1; for February, let X = 2; and so on.b. Use the model to forecast demand for September,October, and November. Here, X = 9, 10, and 11,respectively.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
Demand for oil changes at Garcia’s Garage has been as
follows:
Month Number of Oil Changes
January 41
February 46
March 57
April 52
May 59
June 51
July 60
August 62
a. Use simple linear
casting model for monthly demand. In this application,
the dependent variable, Y, is monthly demand and the
independent variable, X, is the month. For January, let
X = 1; for February, let X = 2; and so on.
b. Use the model to forecast demand for September,
October, and November. Here, X = 9, 10, and 11,
respectively.
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