You will have to explain what conclusions you draw after completing the different data analytics
In Project Part 2, you will required to apply appropriate data management, data visualization and data analytic techniques for a given scenario. The techniques for Project Part 2 will be for covered in Module 1 – Module 10 of the subject. You will have to explain what conclusions you draw after completing the different data analytics.
Scenario 1
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, transmissometer, PAR and C14 primary productivity.
1. a categorical variable and quantitative variable from the dataset to perform ANOVA analysis. (if you want, convert one quantitative variable into a categorical variable and then perform ANOVA analysis). What is conclusion can you draw from the ANOVA analysis?
Hint: Refer to Module 5 and Practical 5 for help
2. 2 categorical variables from the dataset to perform Chi-Squared Test. (if you want, convert one or more quantitative variable into categorical variables and then perform Chi-Squared Test). What is conclusion can you draw from the Chi-Squared Test?
Hint: Refer to Module 6 and Practical 6 for help
3. 3 or more variables from the dataset to perform multiple regression. What is conclusion can you draw from the regression analysis?
Hint: Refer to Modules 7-8 and Practicals 7-8 for help
Scenario 2
You are provided with the Iowa Lottery Weekly Sales by Game Type Dataset. Select one game type and perform time series analysis (ARIMA). What conclusion can you draw from the ARIM analysis?
Hint: Refer to Module 10 and Practicals 10 for help
Ensure you complete, zip and submit all the files below to LearnJCU. Ensure you add your FirstName and LastName inside the files and to the file names.
‘CP2403 - Project – Part 2 – ANOVA - FirstNameLastName.docx’
‘Project-Part2-ANOVA- FirstNameLastName.ipynb’
‘CP2403 - Project – Part 2 – Chi_Squared - FirstNameLastName.docx’
‘CP2403 - Project – Part 2 – TS - FirstNameLastName.docx’
‘Project-Part2-TS- FirstNameLastName.ipynb’
Hint
Business "ANOVA test is a way to find out if experiment or survey results are significant, i.e. it helps to figure out if the rejection of null hypothesis or acceptance of alternate hypothesis is needed. There is One-Way or Two-Way ANOVA:One-way or two-way refers to the number of independent variables (IVs) in the ANOVA test. The One-way has one independent variable with the 2 levels and...
"ANOVA test is a way to find out if experiment or survey results are significant, i.e. it helps to figure out if the rejection of null hypothesis or acceptance of alternate hypothesis is needed.
There is One-Way or Two-Way ANOVA:
One-way or two-way refers to the number of independent variables (IVs) in the ANOVA test. The One-way has one independent variable with the 2 levels and two-way has two independent variables which can have multiple levels.
ANOVA is also a collection of statistical models and their associated estimation procedures which is used to analyze the differences among group means in a sample. In the ANOVA setting, the observed variance in a particular variable is partitioned into components attributable to different sources of variation, i.e. in its simplest form, it provides a statistical test of whether the population means of several groups are equal, and therefore generalizes the t-test to more than two groups. The test is also useful for comparing three or more group means for statistical significance. "