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About this document
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- 11 pages
- Price
- $11 one-time, yours to keep
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- Included in every plan
- Format
- Subject
- Business Intelligence, Economics, Statistics
- Module
- SL IT 609
This document is an assignment on multiple regression analysis of copper prices. It covers the fundamentals affecting copper prices, the variables used in the analysis, and the process of data partition and transformation. It is for the Business Intelligence course.
What's inside
- Fundamentals affecting copper prices
- Variables used in the analysis
- Data partition process
- Transformation of data for normality
- Analysis of dollar index, China imports, USA imports, world consumption, and copper prices
Who it is for: For students of Business Intelligence, and anyone interested in the factors affecting copper prices. (Postgraduate)
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Summary
Copper prices are influenced by several factors. Multiple regression analysis is used to study these factors. Students learn about the process and results of this analysis.
Factors affecting copper prices
Copper prices are influenced by many factors. These include the dollar index, copper consumption, housing index, industrial production, and copper stock. China and the USA are the largest importers of copper. Their imports also affect copper prices.
Multiple regression analysis process
The data is divided into training, testing, and validating sets. The data is checked for assumptions of linear regression. These include normality, outliers, and variable transformation. The dependent variable is copper price. Independent variables are selected based on correlation analysis.
Regression analysis results
The regression model explains close to 90% of variations in copper prices. Dollar index and China's imports have the most impact. Total consumption of copper does not pass the t-test. Time and total stock of copper have less impact.
Key terms
- Dependent variable
- The variable that is being predicted or explained.
- Independent variables
- The variables that are used to predict or explain the dependent variable.
- Correlation analysis
- A statistical method used to measure the strength and direction of a relationship between two variables.
- t-test
- A statistical test used to determine if there is a significant difference between the means of two groups.
- F-test
- A statistical test used to determine if there is a significant difference between the variances of two groups.
- R-squared
- A statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model.
Questions this document helps with
- What factors influence copper prices?
- How is multiple regression analysis used to study copper prices?
- Which variables have the most impact on copper prices?
Summary written with AI from the document's text.
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