We are Team 2!!!!
Suppose you work in a real estate firm and your manager ask you to utilize the historical data
assigned to your team to evaluate the relationship between house price, beds, baths, sqft_home,
sqft_lot, and build year
, and then predict the house price based on given information. Please
following the order of the questions below for the analysis.
1.
Calculate and interpret the correlation coefficient between house price and beds, house
price and baths, house price and sqft_home, house price and sqft_lot, and house price and
build year (15
pts in total; 3 pts for each correlation).
2.
Among the five correlations your team calculated from Question 1, choose the one that has
the highest positive correlation with house price to conduct a hypothesis test. At the 5%
significance level, determine whether the correlation coefficient is significant. Please
clearly state the null and alternative hypothesis, show how your team test the hypothesis,
and provide a clear conclusion based on your hypothesis test results
(20 pts).3.
Using Sqft_home as explanatory variable to conduct a simple linear regression, and
interpret the results regarding the relationship between house sale price and Sqft_home
(15pts).4.
Using Sqft_home and Baths as explanatory variables
to conduct a multiple linear
regression. Interpreting the results regarding the relationship between house sale price,
Sqft_home, and baths and identifying which factor has more impacts on house sale price
(15 pts)
.
5.
Based on the regression results from Question 3 & 4, find the model that best predicts the
house sale price of a house. Use goodness-of-fit measures to find the appropriate
explanatory variables (15 pts).6.
Based on the model you choose from Question 5, predict the house sale price if a house
has 3000 Sqft within the house with 3 baths
(10 pts).7.
Please make sure that the finished project is professional, including a cover page with your
team and all team members’ name, course name and number, semester, submission date,
Excel dataset, etc.
(10 pts)
MGT 252 Business Statistics II
Semester Team Project
Due by 11:59 pm May 1, 2023
Ten bonus points will be given if submitted by 11:59 pm, April 24, 2023
This project is designed to apply the knowledge and Excel skills you learned in this course to the
real business issues and has two parts.
Please read the following instructions to ensure the finished project meet all requirements:
i. Use Microsoft Excel to complete this statistic project for the data assigned in the Excel.
The data assigned to each team can be found in the excel file named “Team Project_team
infos” uploaded to Canvas. Each team is assigned a data with 2500 observations – it is a
historical data with sales information regarding real estate market.
ii. Copy and paste all your Excel results and answers to each of the questions in a Word
document, upload the Word (or PDF) document to the required place in Canvas to get
graded (Do not forget to uploaded your dataset together with the report).
iii. To get full credits, please make sure you read the questions carefully and to answer each
of the parts completely. Remember to submit two items-your dataset in Excel and the
report in Word.
iv. Make sure to show the calculation steps or the Excel commands used to solve the
problems in the word file. If only results are showed in the word, you cannot get the full
credits.
v. Make sure you label answers for each part/question clearly, if I can’t find it, I can’t grade
it. Fail to follow the above instructions will get points deducted accordingly.
vi. Make sure to uploaded both the Word and Excel files to Canvas to get full credits.
vii. Since this is a team project, each team member must make contributions in order to get a
grade for this project, and a peer evaluation sheet will be given and your team project
score will be calculated by using the total team project score times the peer evaluation
score you get from all your team members.
1
Suppose you work in a real estate firm and your manager ask you to utilize the historical data
assigned to your team to evaluate the relationship between house price, beds, baths, sqft_home,
sqft_lot, and build year, and then predict the house price based on given information. Please
following the order of the questions below for the analysis.
1.
Calculate and interpret the correlation coefficient between house price and beds, house
price and baths, house price and sqft_home, house price and sqft_lot, and house price and
build year (15 pts in total; 3 pts for each correlation).
2.
Among the five correlations your team calculated from Question 1, choose the one that has
the highest positive correlation with house price to conduct a hypothesis test. At the 5%
significance level, determine whether the correlation coefficient is significant. Please
clearly state the null and alternative hypothesis, show how your team test the hypothesis,
and provide a clear conclusion based on your hypothesis test results (20 pts).
3.
Using Sqft_home as explanatory variable to conduct a simple linear regression, and
interpret the results regarding the relationship between house sale price and Sqft_home (15
pts).
4.
Using Sqft_home and Baths as explanatory variables to conduct a multiple linear
regression. Interpreting the results regarding the relationship between house sale price,
Sqft_home, and baths and identifying which factor has more impacts on house sale price
(15 pts).
5.
Based on the regression results from Question 3 & 4, find the model that best predicts the
house sale price of a house. Use goodness-of-fit measures to find the appropriate
explanatory variables (15 pts).
6.
Based on the model you choose from Question 5, predict the house sale price if a house
has 3000 Sqft within the house with 3 baths (10 pts).
7.
Please make sure that the finished project is professional, including a cover page with your
team and all team members’ name, course name and number, semester, submission date,
Excel dataset, etc. (10 pts)
2
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