I’ve attached my questions. I still need 2 complex questions to ask. Please help. Please ready instruction carefully. You can organize by each of the 7 questions. Ask the question then break down how you analyze to awnser the question.
The dataset: Natural Resources Revenue Dataset
https://www.kaggle.com/datasets/sujaykapadnis/natural-resources-revenue-dataset
You will need to create 7 questions. For each of the 7 questions, state the
following: 1) The question you are going to explore in your analysis; 2) State how
you will analyze the data to answer the question; 3) State whether it is a
simple or complex analysis.
For you to get an A on your Step 2 project grade (the major part of the
project grade), you will need to have at least two questions requiring a
complex analysis. To be considered a
complex analysis, you will need to use Excel tools such as correlation, pivot
tables, ANOVA, and regression to analyze the data (there are plenty of online
videos and websites describing how to do these analyses). If you are only answering questions using a
simple analysis, such as answering the questions with sums and totals, then
state that it is a simple analysis. There’s
no need to do a complex analysis for all of your questions, but a student who
performs a more complex analysis to answer 2-3 of the questions will get more
points on Step 2 of the project than a student who only has a simple analysis
to answer questions.
As an example, below are questions that a
student may write up if examining a dataset on NBA Player salaries:
a.
What has the
percent growth been in NBA salaries each year? – I will sum the totals of each
year’s salaries and then calculate percent growth per year to answer this. (SIMPLE)
b.
Which are the
teams that had the highest salaries? – I will sum the total salaries per team
and then find the top 5 teams that pay the most in salaries. (SIMPLE)
c.
Does the amount
of salaries affect the amount a wins that a team has? – I will see if there is
a statistically significant correlation between the total salary that a team
pays and the amount of wins the team has.
(COMPLEX)
d.
Does the
player salary correlate with a higher percentage of shots made? – I will see if
there is a statistically significant correlation between the salaries of
players and their percentage of shots made.
(COMPLEX)
On
some of the datasets, kaggle.com has descriptions about the data in the dataset
and some questions that could be explored.
If the dataset you are using has some questions, you can either choose
to explore questions that kaggle.com has listed or you can create your own
questions. Note that it is important to
come up with questions that you will be able to answer by analyzing the data. Don’t create a question that you won’t be
able to answer from your dataset. You
will need to include an Excel graphical chart as a part of each of your answers,
so they will need to be questions that are comparing data, looking at trends,
etc.
1) Data Analytics Project Overview
a. Your Name
b. Data Analytics Project Title
c. Name of Dataset used
d. Description of Dataset (3-4
sentences describing the content of your dataset)
2) Analysis of Data – State each of the 7 questions you are answering and then your answer
to each question. Type your answer to
your question and use a graphical chart to support every answer. For example, to answer the percent growth in NBA
salaries each year, I could state the answer and then include an Excel bar
chart to support the answer. To answer
the question about the teams that had the highest salaries, I could list the teams
that had the highest level and then include an Excel bar chart. Also, as stated in Step 1 of the project,
remember that the complexity of your analysis will affect your grade and that
you will need to have at least two complex analyses in order to get an A.
3) Conclusion – should have 4-6 paragraphs addressing the following questions
a. What have you learned from
the data analysis?
b. How could a manager of an
organization use your analysis to improve their organization?
c. What additional data would
have been useful in the dataset? (For
example, for the NBA salaries dataset, if more data was provided in the dataset
on each player’s performance, then more useful reports could be generated to determine
the performance of each player and his value to the team based on his salary)
Other Specifications:
Writing Style: As business students, you are expected to
produce a professionally-worded and structured document.
