Question

For this question you first need to download the following price data in monthly frequency: Global price of Hides. If you come across alternative data series for this product/material please choose the one with the longest time-series and no gaps.

a) Describe your product/material. What is its main use? Why do people buy it?
b) Describe your data source and why you think it is a reliable source.
c) Provide the full name of your variable and download link for the data.
d) Plot the price level over time.
e) Test if the price level has a significant time trend.
f) Test if the price level has a significant exponential trend.
g) Calculate the monthly growth rate of the price, let's call this as "return series". Provide the summary statistics for return series.
h) Plot the monthly return series over time.
i) Calculate the average return for each calendar month, i.e. average return in January, average return in February, etc., and report them. Do you see any pattern? j) Remove the seasonality from the return series and plot the seasonally adjusted returns together with the original return series in the same graph.

Answer 1

To work with a data collection, organize and describe the data using statistical methods and visually through a scatter plot. Calculate the least-squares line to find the best fit and determine the correlation coefficient to assess the relationship between variables.

Step-by-step explanation:

When presented with a data collection, the first step is to organize and describe the data. This can be achieved through various statistical methods and visual representations. Here's a step by step approach aligned with the questions provided:

• Understand which variable is independent (predictor) and which is dependent (response).
• Create a scatter plot to visually assess the relationship between the two variables.
• Calculate the least-squares line to find the best fit for the data points. The equation will be in the form ŷ = a + bx, where 'a' represents the y-intercept and 'b' the slope.
• Determine the correlation coefficient to measure the strength and direction of the linear relationship between the two variables.
• After establishing the relationships and trends, analyze specific cases as instructed, such as predicting values or identifying patterns.

For complex analyses such as identifying exponential trends, seasonality, or adjusting for seasonality, sophisticated statistical methods and software may be required, which will enable us to perform these analyses accurately. a. The house prices should be the dependent variable and the independent variable should be a factor such as the square footage or number of bedrooms.

b. After deciding on the variables, we can draw a scatter plot to visualize the relationship between them.

c. By observing the scatter plot, we can determine if there is a relationship between the variables. If there is a trend, such as a positive or negative slope, then there is likely a relationship.

d. To calculate the least-squares line, we use the method of least squares to find the line that minimizes the sum of the squared distances between the observed data points and the predicted line. The equation of the line can be written as ŷ = a + bx, where ŷ is the predicted value, a is the y-intercept, b is the slope, and x is the independent variable.

e. The correlation coefficient measures the strength and direction of the linear relationship between the variables. It ranges from -1 to 1, where -1 indicates a perfect negative correlation, 1 indicates a perfect positive correlation, and 0 indicates no correlation.

f. To find the average CPI for the year 1990, you would need to refer to the dataset or specific information provided.