Assignment -
Needs to be done in code in different steps. This is the scenario to do:
Imagine you are a quantitative analyst working in an investment fund that is looking to invest in an agricultural companies that is involved in rice production. Your primary job is to analyze the production levels of four candidate companies. Your analysis will help identify companies with rice production levels that are significantly different than others. This will help your fund make a data based decision on their investment.
You are now given a task to analyze historical data on rice production for four agricultural companies. The data consist of monthly rice production in cwt (Centum Weight - measure of weight) for four companies for the past 21 years. You will be using this data to finish this task.
Use the following table as a reference guide for the variables of interest for this project.
Variable Definition
Company1 Total rice production, in cwt, for Company 1.
Company2 Total rice production, in cwt, for Company 2.
Company3 Total rice production, in cwt, for Company 3.
Company4 Total rice production, in cwt, for Company 4.
Month The Month of the year reported as integers (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12).
Final Project Parts I and II Guidelines -
Overview - It can easily be argued that applied statistics has never been more relevant than it is today. It plays critical roles in fields such as data science, healthcare, business intelligence, government administration, and even machine learning, to name just a few! To reflect the many practical applications for statistics in our twenty-first-century world, the final project for this course has been designed to apply to various contexts. To that end, you will be assigned a final project scenario by your instructor in Module One. In this scenario, you will assume the role of a data analyst working to analyze and interpret the provided data for your organization. Specifically, you will execute, evaluate, and report on the outputs of statistical tests that you will create using the Python programming language in your Codio environment.
This project has three parts; the first two are listed in the prompt below. Each part consists of two components: 1) the Python script that you will develop to perform the required statistical calculations of the prompt, and 2) an accompanying report in which you will interpret and communicate your results and your understanding of the underlying statistical concepts. You will have opportunities to practice each part in Milestones One, Two, and Three, which must be submitted in Modules Four, Five, and Six, respectively. However, in order for you to receive feedback on each part before delivering your final product, your final submission is divided into two delivery events: Final Project Parts I and II (i.e., the revised version of Milestones One and Two) will be submitted as a package in Module Seven. Final Project Part Three (i.e., the revised version of Milestone Three) will be submitted in Module Eight so that you have time to receive and incorporate your instructor's feedback. See the MAT 243 Course Introduction infographic for a helpful visualization of the milestones and final project delivery schedule.
In this assignment, you will demonstrate your mastery of the following course outcomes:
- Illustrate the conceptual foundations of applied statistics using concrete examples
- Perform appropriate statistical calculations on provided data sets using advanced programming language
- Interpret the results of various statistical techniques for their significance using data sets with real-world relevancy
- Communicate statistical results and their significance clearly using proper statistical terminology
Prompt - Specifically, the following critical elements must be addressed. Most of the critical elements align with a particular course outcome (shown in brackets).
Part I: Statistical Analysis of One Sample
I. Python Script: To complete the tasks listed below, navigate to the correct modules in Codio according to your scenario assignment and follow the step-by-step instructions embedded within.
A. Calculate descriptive statistics by creating and executing the appropriate functions in your programming environment.
B. Construct confidence intervals by creating and executing the appropriate functions in your programming environment.
C. Perform hypothesis tests by creating and executing the appropriate functions in your programming environment.
II. Summary Report: Use the provided template to create your report.
A. Illustrate the critical parameters that you employed in creating your Python script in Codio. Specify each one and explain their importance for constructing the confidence intervals and performing the hypothesis tests. Specifically, address each of the following:
1. The level of confidence
2. The null hypothesis, alternative hypothesis, level of significance, and associated critical value(s) for the hypothesis test
B. Interpret the results of your statistical analyses in terms of their statistical significance. Specifically, be sure to address each of the following:
1. The measures of center and spread
2. The lower limit and the upper limit of the confidence intervals
3. The test statistic and the probability value of the hypothesis tests
C. Summarize the results of your statistical analyses and clearly communicate the ideas by translating relevant course concepts and terminology into plain language.
Part II: Statistical Analysis of Two Samples
III. Python Script: Navigate to the correct modules in Codio according to your scenario assignment and follow the step-by-step instructions embedded within. Specifically, you should perform hypothesis tests by creating and executing the appropriate functions in your programming environment.
IV. Summary Report: Use the provided template to create your report.
A. Illustrate the critical parameters that you employed in creating your Python script in Codio. Specify each one and explain their importance for performing the hypothesis tests. Specifically, address the null hypothesis, alternative hypothesis, level of significance, and associated critical value(s) for the hypothesis test.
B. Interpret the results of your statistical analyses in terms of their statistical significance. Specifically, be sure to address the test statistic and the probability value of the hypothesis tests.
C. Summarize the results of your statistical analyses and clearly communicate the ideas by translating relevant course concepts and terminology into plain language.
Final Project Part III Guidelines -
Overview - It can easily be argued that applied statistics has never been more relevant than it is today. It plays critical roles in fields such as data science, healthcare, business intelligence, government administration, and even machine learning, to name just a few! To reflect the many practical applications for statistics in our twenty-first-century world, the final project for this course has been designed to apply to various contexts. To that end, you will be assigned a final project scenario by your instructor in Module One. In this scenario, you will assume the role of a data analyst working to analyze and interpret the provided data for your organization. Specifically, you will execute, evaluate, and report on the outputs of statistical tests that you will create using the Python programming language in your Codio environment.
This project is made up of three parts; the third and final part is listed in the prompt below. Each part consists of two components: 1) the Python script that you will develop to perform the required statistical calculations of the prompt, and 2) an accompanying report in which you will interpret and communicate your results and your understanding of the underlying statistical concepts. You will have opportunities to practice each part in Milestones One, Two, and Three, which must be submitted in Modules Four, Five, and Six, respectively. However, in order for you to receive feedback on each part before delivering your final product, your final submission is divided into two delivery events: Final Project Parts I and II (i.e., the revised version of Milestones One and Two) will be submitted as a package in Module Seven. Final Project Part Three (i.e., the revised version of Milestone Three) will be submitted in Module Eight so that you have time to receive and incorporate your instructor's feedback. See the MAT 243 Course Introduction infographic for a helpful visualization of the milestones and final project delivery schedule.
In this assignment, you will demonstrate your mastery of the following course outcomes:
- Illustrate the conceptual foundations of applied statistics using concrete examples
- Perform appropriate statistical calculations on provided data sets using advanced programming language
- Interpret the results of various statistical techniques for their significance using data sets with real-world relevancy
- Communicate statistical results and their significance clearly using proper statistical terminology
Prompt - Specifically, the following critical elements must be addressed. Most of the critical elements align with a particular course outcome (shown in brackets).
Part III: Statistical Analysis of Three or More Samples
I. Python Script: To complete the tasks listed below, navigate to the correct modules in Codio according to your scenario assignment and follow the step- by-step instructions embedded within.
A. Perform one-way ANOVA for three or more population means. Calculate the test statistic and probability value by creating and executing the appropriate functions in your programming environment.
B. Plot boxplots for the means to identify which of the means is significantly different from the others.
II. Summary Report: Use the provided template to create your report.
A. Illustrate the critical parameters that you employed in creating your Python script in Codio. Specify each one and explain their importance for performing one-way ANOVA and plotting the boxplots. Specifically, address the null hypothesis, alternative hypothesis, level of significance, associated critical value, and probability value for one-way ANOVA.
B. Interpret the results of your statistical analyses in terms of their statistical significance. Specifically, be sure to address both one-way ANOVA and the outputs of your boxplots.
C. Summarize the results of your statistical analyses and clearly communicate the ideas by translating relevant course concepts and terminology into plain language.
Attachment:- Assignment Files.rar