CP2403/CP3413 Information Processing and Visualisation Assignment 2 Part - JCU

CP2403/CP3413 Scenario and Task:

The California Cooperative Oceanic Fisheries Investigations (CalCOFI) was formed in 1949 to study the ecological aspects of the sardine population collapse off California. CalCOFI conducts quarterly cruises off southern & central California, collecting a suite of hydrographic and biological data on station and underway. The CalCOFI data set represents the longest (1949-present) and most complete (more than 50,000 sampling stations) time series of oceanographic in the world.

The physical, chemical, and biological data collected at regular time and space intervals quickly became valuable for documenting climatic cycles in the California Current and a range of biological responses to them. Data collected at depths down to 500 m include: temperature, salinity, oxygen, phosphate, silicate, nitrate and nitrite, chlorophyll, trans missometer, PAR and C14 primary productivity.

Task 1 of CP2403/CP3413:

Select one categorical variable and one quantitative variable from the dataset to perform ANOVA analysis. What conclusion can you draw from the ANOVA analysis?

(Note: for the selection of a categorical variable, you can either select an existing categorical variable directly or generate a new categorical variable by transforming an existing non-categorical variable).

Hint: Refer to Module 5 and Practical 5 for help

Task 2 of CP2403/CP3413:

Select two categorical variables from the dataset to perform Chi-Squared Test. What conclusion can you draw from the Chi-Squared Test?

(Note: for the selection of a categorical variable, you can either select an existing categorical variable directly or generate a new categorical variable by transforming an existing non-categorical variable.)

(Note: for this task, be careful not to select (or generate) a categorical variable having more than ten categories. Having too many categories may cause the post-hoc test (if necessary) not to make meaningful results.)

Hint: Refer to Module 6 and Practical 6 for help

Task 3 of CP2403/CP3413:

Select two quantitative variables from the dataset to perform linear regression. What is conclusion you draw from the linear regression analysis?

Hint: Refer to Module 7 and Practical 7 for help

Task 4 of CP2403/CP3413:

Select four quantitative variables (one respond variable and three explanatory variables) from the dataset to perform multiple regression. What is conclusion can you draw from the regression analysis?

For this task, you must perform pre-testing by performing several different multiple regression models by selecting various combinations of individual regression functions. You are required to demonstrate at least three different combinations to compose the multiple regression model. Finally select one best multiple regression model based on your justification if possible.

Hint: Refer to Module 7 and 8 and Practical 7 and 8 for help