A mail-order business selling personal computer supplies, software and hardware maintains a centralized warehouse. Management is currently examining the process of distribution from the warehouse and wants to study the factors that affect the warehouse distribution costs. Data collected over 24 random months contain the warehouse’s distribution cost (in thousands of Rands), the sales (in thousands of Rands) and the number of orders received. A multiple linear regression model was fitted to the data by using Stat1.2. Use the output to answer the questions that follow by typing only the letter of the correct option in the answer boxes.
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.
A mail-order business selling personal computer supplies, software and hardware maintains a centralized warehouse. Management is currently examining the process of distribution from the warehouse and wants to study the factors that affect the warehouse distribution costs.
Data collected over 24 random months contain the warehouse’s distribution cost (in thousands of Rands), the sales (in thousands of Rands) and the number of orders received. A multiple linear regression model was fitted to the data by using Stat1.2. Use the output to answer the questions that follow by typing only the letter of the correct option in the answer boxes.
Variables
y: Warehouse Distribution Cost
x1: Sales
x2: Number of Orders
Model Fitting Statistics
R2 = 0.8504
Adj R2: ?
Regression Coefficients
Beta Parameter Standard b Parameter Standard
Estimates Error of Beta Estimates Error of b t Statistic Prob > |t|
Intcpt -0.800 7.325 -0.109 0.914
x1 0.144 0.150 0.023 0.024 0.961 0.348
x2 0.791 0.150 0.018 0.003 5.266 0.001*
Contribution of the variables to R^2
Predictor Ri2 R2 - Ri2 f2
x1 0.8221 0.0075 0.0439
x2 0.6045 0.2251 1.3206
The estimated multiple regression line is given by?
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