Question

Use the following two datasets to answer the questions:

Datasets: tempr , part .
1- Introduce the two datasets following the way our lectures used. (use library(astsa), data(part), data(tempr))
2- Create two research questions that the two datasets can support.
3- Visualize the dataset tempr using the codes from lectures. Examine and explain the four main features in the plot.
4- Visualize the dataset part using the codes from lectures. Examine and explain the four main features in the plot.
5- Combine the two dataset together and generate the two time series datasets in one plot graph.
6- State four key features from the new plot and the finding. Explain the finding and the possible causes.

Answer 1

The datasets tempr and part can be introduced and visualized using R, with various features such as trend, seasonality, and noise analyzed. Visualizing both datasets together on a dual time series graph can reveal correlations and relationships. Discussions around these plots provide insight into data interpretation and the importance of considering comprehensive datasets.

Step-by-step explanation:

To introduce the two datasets tempr and part within R, one would typically first load the required package by running library(astsa), and then load each dataset using data(part) and data(tempr) respectively. These datasets can support different research questions. For instance, one could examine the relationship between temperature and participation in a certain activity over time. Another possible research question could be the seasonal trends in temperature and activity participation.

To visualize the tempr dataset, one would use plotting functions from lectures or R's native plotting functions. Four main features to examine in the plot could include the general trend, seasonality, cyclicity, and noise (random variation). The same process applies to visualizing the part dataset, with similar features to be interpreted from the time series plot.

Combining the two datasets and generating a dual time series plot allows for comparison of both time series on one graph. Key features from this new plot might include correlation between the two series, coinciding peaks or troughs, relative variability, and any lag between series. The findings may suggest direct or indirect relationships and could be caused by common influencing factors or causative effects from one time series on the other.

A collaborative exercise involves plotting sports data and discussing probable probability distributions that might fit the collected data.

To construct a time series graph, a Cartesian coordinate system is used with the time as the horizontal axis and the measured variable on the vertical axis. The primary purpose is to visualize how the measured variable changes over time.

Finally, discussing the potential misinterpretations of data, especially in the context of climate change, helps emphasize the importance of considering a comprehensive dataset and avoiding conclusions based on limited information.