In your own words, explain what it means if the correlation of 2 variables is positive, negative, or minimal (close to 0), and give an example of each.
What do you deduce from the correlations? Explain if you believe these to be short or long-term objectives and outcomes.
What are the implications for Big D Incorporated regarding its client in the outdoor sporting goods?
What are the implications for the penetration into the indoor sporting goods market?
Also, how can you use the correlation tools to identify the variables in the research toward the expansion into the indoor sporting goods market?
Full Answer Section
- Negative correlation: A negative correlation means that as one variable increases, the other variable decreases. For example, there is a negative correlation between ice cream sales and temperature. As the temperature increases, ice cream sales tend to decrease.
- Minimal correlation: A minimal correlation means that there is no linear relationship between the two variables. For example, there is a minimal correlation between shoe size and intelligence.
Examples of each
- Positive correlation:
- Height and weight
- Ice cream sales and temperature
- Number of hours studied and exam grades
- Negative correlation:
- Price of a product and demand for the product
- Time spent exercising and weight loss
- Number of cigarettes smoked per day and life expectancy
- Minimal correlation:
- Shoe size and intelligence
- Eye color and handedness
- Blood type and personality
Implications for Big D Incorporated
The correlations between different variables can have important implications for businesses like Big D Incorporated. For example, if Big D Incorporated knows that there is a positive correlation between income and spending on outdoor sporting goods, they can target their marketing campaigns at people with higher incomes.
Additionally, if Big D Incorporated is considering expanding into the indoor sporting goods market, they can use correlation tools to identify the variables that are most strongly correlated with spending on indoor sporting goods. This information can then be used to develop targeted marketing campaigns and sales strategies.
Short vs. long-term objectives and outcomes
The correlations between different variables can also be used to identify short-term and long-term objectives and outcomes. For example, if Big D Incorporated wants to increase sales of outdoor sporting goods in the short term, they can focus on marketing campaigns that target people who are planning to go on outdoor vacations or who have recently purchased new outdoor sporting equipment.
In the long term, Big D Incorporated may want to focus on marketing campaigns that promote the benefits of outdoor recreation and exercise. This could help to increase the overall demand for outdoor sporting goods and lead to sustained growth in sales over time.
How to use correlation tools to identify the variables in the research toward the expansion into the indoor sporting goods market
Big D Incorporated can use a variety of correlation tools to identify the variables that are most strongly correlated with spending on indoor sporting goods. Some common correlation tools include:
- Pearson correlation coefficient: This is the most common measure of correlation. It ranges from -1 to 1, with a higher value indicating a stronger correlation.
- Spearman's rank correlation coefficient: This is a non-parametric measure of correlation that is less sensitive to outliers than the Pearson correlation coefficient.
- Point-biserial correlation coefficient: This is used to measure the correlation between a continuous variable (e.g., spending on indoor sporting goods) and a dichotomous variable (e.g., whether or not a person participates in indoor sports).
Big D Incorporated can use these correlation tools to analyze data from a variety of sources, such as customer surveys, market research reports, and sales data. This information can then be used to identify the variables that are most strongly correlated with spending on indoor sporting goods.
Once Big D Incorporated has identified the most important variables, they can use this information to develop targeted marketing campaigns and sales strategies. For example, if they find that there is a strong correlation between income and spending on indoor sporting goods, they can target their marketing campaigns at people with higher incomes.
By using correlation tools to identify and understand the relationships between different variables, Big D Incorporated can make better decisions about how to market and sell their products. This can help them to increase sales and grow their business.
Sample Answer
Correlation of two variables
The correlation of two variables is a measure of the linear relationship between them. It can be positive, negative, or close to zero.
- Positive correlation: A positive correlation means that as one variable increases, the other variable also increases. For example, there is a positive correlation between height and weight. As people get taller, they tend to weigh more.