Question

The North Valley Real Estate data 2015 reports information on homes on the market.

- Let selling price be the dependent variable and size of the home the independent variable. Determine the regression equation. Estimate the selling price for a home with an area of 2,200 square feet. Determine the 95% confidence interval for all 2,200 square foot homes and the 95% prediction interval for the selling price of a home with 2,200 square feet.
- Let days-on-the-market be the dependent variable and price be the independent variable. Determine the regression equation. Estimate the days-on-the-market of a home that is priced at $300,000. Determine the 95% confidence interval of days-on-the-market for homes with a mean price of $300,000, and the 95% prediction interval of days-on-the-market for a home priced at $300,000.
- Can you conclude that the independent variables “days on the
market” and “selling price” are positively correlated? Are the size
of the home and the selling price positively correlated? Use the
.05 significance level. Report the
*p*-value of the test. Summarize your results in a brief report.

Answer #1

2: A real estate agent is interested in what factors determine
the selling price of homes in Northwest Arkansas. She takes a
random sample of 20 homes, and conducts a multiple regression
analysis. The dependent variable is price of the home (in thousands
of dollars), the square footage of the home, and whether the home
is located in a new subdivision (0 = no; 1 = yes). The results of
the multiple regression analysis are shown below. Answer the
following...

Question 16
Imagine that you are a realtor and are interested in selling
homes by the beach. Some have ocean views and some do not. Here is
a regression equation that will help you to predict the cost of a
home (in $1,000s) based on the square footage (sq.ft.) and whether
or not it has an ocean view. Ocean view will be a dummy variable
set at 1 for an ocean view unit, and 0 for no ocean view.
Predicted...

1.A real estate analyst has developed a multiple regression
line, y = 60 + 0.068 x1 – 2.5
x2, to predict y = the market
price of a home (in $1,000s), using two independent variables,
x1 = the total number of square feet of living
space, and x2 = the age of the house in years.
With this regression model, the predicted price of a 10-year old
home with 2,500 square feet of living area is __________.
$205.00
$255,000.00
$200,000.00...

The data show the list and selling prices for several expensive
homes. Find the regression equation, letting the the list price be
the independent (x) variable. Find the best predicted selling
price of a home having a list price of $ 2 million. Is the result
close to the actual selling price of $ 2.2 million? Use a
significance level of 0.05. What is the regression equation? What
is the best predicted selling price of a home having a list...

A random sample of nine custom homes currently listed for sale
provided the following information on size and price. Here, x
denotes size, in hundreds of square feet, rounded to the nearest
hundred, and y denotes price, in thousands of dollars, rounded to
the nearest thousand.
The summaries of the data are given by
n=9, ∑xi=270, ∑x2i=8316, ∑yi=5552, ∑y2i=3504412,
∑xiyi=169993
Given the summaries of the data, find the least-squares
regression equation.
Graph the regression equation and the data points.
Interpret...

The data show the list and selling prices for several expensive
homes. Find the regression equation, letting the the list price be
the independent (x) variable. Find the best predicted selling
price of a home having a list price of $4 million. Is the result
close to the actual selling price of $4.2million?
List price (millions of $)
2.8
2.9
2.3
3.4
2.1
1.6
Selling price (millions of $)
2.6
3
2.2
3.7
1.9
1.8
What is the regression equation?...

3. United Park City Properties real estate
investment firm took a random sample of five condominium units that
recently sold in the city. The sales prices Y (in thousands of
dollars) and the areas X (in hundreds of square feet) for each unit
are as follows
Y= Sales Price
( * $1000)
36
80
44
55
35
X = Area (square feet) (*100)
9
15
10
11
10
a. The owner wants to forecast sales on the basis of...

Use Data Analysis in Excel to conduct the Regression Analysis to
reproduce the excel out put below (Note: First enter the data in
the next page in an Excel spreadsheet)
Home Sale Price: The table below provides the Excel output of a
regression analysis of the relationship between Home sale price(Y)
measured in thousand dollars and Square feet area
(x):
SUMMARY OUTPUT
Dependent:
Home Price
($1000)
SUMMARY OUTPUT
Dependent:
Home Price
($1000)
Regression Statistics
Multiple R
0.691
R Square
0.478...

Data were collected from a random sample of 340 home sales from
a community in 2003. Let Price denote the selling price (in
$1,000), BDR denote the number of bedrooms, Bath denote the number
of bathrooms, Hsize denote the size of the house (in square
feet), Lsize denote the lot size (in square feet), Age denote the
age of the house (in years), and Poor denote a binary variable
that is equal to 1 if the condition of the house...

Data Set Preparation
(Using A JMP Folder) Can email you if comment your email.
1. (10 pts.) Using the “Toyota Corolla” data set on Canvas (Home
à “JMP” à “(Under: JMP Data Sets folder)”, you will be modeling the
“Price” of a car as the dependent variable (Y). Please select one
independent variable (X) you think may help explain Price, from the
following three: “Age”, “Mileage”, or “Weight” of a car. In the
space below, state your choice and explain...

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