Required: i. Write the estimated regression equation for the full model with all three (3) variables. Conduct the global test, state the test statistic, the p-value, and evaluate whether or not to reject the null hypothesis using a = 0.05 ii. Give the numerical values (from the output) for two different test statistics for testing Ho: B3 = 0 (given that X1 and X2 are in the model). Evaluate your answer in words [1-2 sentences], in the context of the real estate situation, what this hypothesis is testing. ii. Assess the result if you use bedrooms as the only predictor to fit the simple linear regression model. Conclude your answer based on the information provided in the output.
Required: i. Write the estimated regression equation for the full model with all three (3) variables. Conduct the global test, state the test statistic, the p-value, and evaluate whether or not to reject the null hypothesis using a = 0.05 ii. Give the numerical values (from the output) for two different test statistics for testing Ho: B3 = 0 (given that X1 and X2 are in the model). Evaluate your answer in words [1-2 sentences], in the context of the real estate situation, what this hypothesis is testing. ii. Assess the result if you use bedrooms as the only predictor to fit the simple linear regression model. Conclude your answer based on the information provided in the output.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Concept explainers
Contingency Table
A contingency table can be defined as the visual representation of the relationship between two or more categorical variables that can be evaluated and registered. It is a categorical version of the scatterplot, which is used to investigate the linear relationship between two variables. A contingency table is indeed a type of frequency distribution table that displays two variables at the same time.
Binomial Distribution
Binomial is an algebraic expression of the sum or the difference of two terms. Before knowing about binomial distribution, we must know about the binomial theorem.
Topic Video
Question
business statistics
![Table 3: Stata Output for Sales Price of the House
reg_ss salesprice sqft bedroons lotsize
df
Number of obs-
67
6.69
0.0005
0.2417
0.2056
1.let05
Source|
MS
E3,
63) -
Model 2.4910e+11
Residual | 7.8158e+11
3 8.3035e+10
63
1.2406e+10
Prob > P
R-squared
Adj R-squared -
Root MSE
Total 1.0307e+12
66 1.561 6e+10
aalesprice |
Coef.
Std. Err.
(953 Conf. Interval]
14906.48
aqfti00 |
bedrocma |
lotsize I
9893.524
2508.561
3,94
-2.46
D.000
4880.564
-36018.68
14665.17
0.017
-65324.68
-6712.675
2.512511
1.124531
2.23
0.029
-2653148
דסד59ד.4
cons |
290558.1
75398.79
3.85
0.000
139885.6
441230.5
Seguent ial Sum of Squares for Regression
salesprice |
Coef.
Seq Ss
afl
Prob > F
aqft100 |
bedrocma -34701.03
6187.993
1.18e+11
1
63
9.474134
0.0031
6.96a+10
63
5.605086
0.0210
lotsize
2.512511
6.19a+10
63
4.989326
0.0291
reg_ss salesprice bedrooms sqtt lotsize
df
Number of obs -
67
6.69
0.0005
0.2417
0.2056
1.let05
Source |
MS
E( 3,
Prob > P
63)
Model | 2.4910e+11
3 8.3035e+10
63
1.2406e+10
Residual I 7.8158e+11
R-squared
Adj R-squared -
Root MSE
Total 1.0307e+12
66 1.5616e+10
aalesprice |
Coef.
Std. Err.
(953 Conf. Interval]
bedrooms | -36018.68
sqtti00 |
lotsize I
14665.17
-2.46
0.017
-65324.68
-6712.675
9893.524
2508.561
3.94
D.000
4880.564
14906.48
2.512511
1.124531
2.23
0.029
.2653148
4.759707
cons
290558.1
75398.79
3.85
0.000
139885.6
441230.5
Seguential Sum of Squares for Regression
salesprice |
Coef.
Seg
df1
df2
Prob > E
bedrocma
-668. 8945
4.02a+07
1
63
-0032419
0.9548
aqfti00
lotsize |
9739.083
1.87a+11
63
15.07598
0.0003
2.512511
6.19a+10
1
63
4.989326
0.0291
Required:
i.
Write the estimated regression equation for the full model with all three (3)
variables. Conduct the global test, state the test statistic, the p-value, and
evaluate whether or not to reject the null hypothesis using a = 0.05
i.
Give the numerical values (from the output) for two different test statistics
for testing Ho: B3 = 0 (given that X1 and X2 are in the model). Evaluate
your answer in words [1-2 sentences], in the context of the real estate
situation, what this hypothesis is testing.
İi.
Assess the result if you use bedrooms as the only predictor to fit the
simple linear regression model. Conclude your answer based on the
information provided in the output.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F54b8ee04-0603-4d53-b680-d292f5b90322%2F62dad784-aff0-4f49-9da2-6cd79cd2ab2d%2Fd87ksab_processed.png&w=3840&q=75)
Transcribed Image Text:Table 3: Stata Output for Sales Price of the House
reg_ss salesprice sqft bedroons lotsize
df
Number of obs-
67
6.69
0.0005
0.2417
0.2056
1.let05
Source|
MS
E3,
63) -
Model 2.4910e+11
Residual | 7.8158e+11
3 8.3035e+10
63
1.2406e+10
Prob > P
R-squared
Adj R-squared -
Root MSE
Total 1.0307e+12
66 1.561 6e+10
aalesprice |
Coef.
Std. Err.
(953 Conf. Interval]
14906.48
aqfti00 |
bedrocma |
lotsize I
9893.524
2508.561
3,94
-2.46
D.000
4880.564
-36018.68
14665.17
0.017
-65324.68
-6712.675
2.512511
1.124531
2.23
0.029
-2653148
דסד59ד.4
cons |
290558.1
75398.79
3.85
0.000
139885.6
441230.5
Seguent ial Sum of Squares for Regression
salesprice |
Coef.
Seq Ss
afl
Prob > F
aqft100 |
bedrocma -34701.03
6187.993
1.18e+11
1
63
9.474134
0.0031
6.96a+10
63
5.605086
0.0210
lotsize
2.512511
6.19a+10
63
4.989326
0.0291
reg_ss salesprice bedrooms sqtt lotsize
df
Number of obs -
67
6.69
0.0005
0.2417
0.2056
1.let05
Source |
MS
E( 3,
Prob > P
63)
Model | 2.4910e+11
3 8.3035e+10
63
1.2406e+10
Residual I 7.8158e+11
R-squared
Adj R-squared -
Root MSE
Total 1.0307e+12
66 1.5616e+10
aalesprice |
Coef.
Std. Err.
(953 Conf. Interval]
bedrooms | -36018.68
sqtti00 |
lotsize I
14665.17
-2.46
0.017
-65324.68
-6712.675
9893.524
2508.561
3.94
D.000
4880.564
14906.48
2.512511
1.124531
2.23
0.029
.2653148
4.759707
cons
290558.1
75398.79
3.85
0.000
139885.6
441230.5
Seguential Sum of Squares for Regression
salesprice |
Coef.
Seg
df1
df2
Prob > E
bedrocma
-668. 8945
4.02a+07
1
63
-0032419
0.9548
aqfti00
lotsize |
9739.083
1.87a+11
63
15.07598
0.0003
2.512511
6.19a+10
1
63
4.989326
0.0291
Required:
i.
Write the estimated regression equation for the full model with all three (3)
variables. Conduct the global test, state the test statistic, the p-value, and
evaluate whether or not to reject the null hypothesis using a = 0.05
i.
Give the numerical values (from the output) for two different test statistics
for testing Ho: B3 = 0 (given that X1 and X2 are in the model). Evaluate
your answer in words [1-2 sentences], in the context of the real estate
situation, what this hypothesis is testing.
İi.
Assess the result if you use bedrooms as the only predictor to fit the
simple linear regression model. Conclude your answer based on the
information provided in the output.
Transcribed Image Text:Table 3 gives Stata output for the following situation. The data consist of the 68
houses that have Quality = 1. Y = Sales Price of the house ("salesprice" on the
output), and the three predictor variables are:
X1 = Square feet divided by 100 = "sqft100" on the output
X2 = Number of bedrooms = "bedrooms" on the output
X3 = Lot size in square feet = "lotsize" on the output
Notice in the output that the model was fit twice, with the variables in two different
orders, but the designation of X1, X2 and X3 are as defined above. In other
words, we define X1 = Square feet divided by 100, and so on, no matter what
order they appear in the Stata command.
Expert Solution
This question has been solved!
Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.
Step by step
Solved in 3 steps
Knowledge Booster
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, statistics and related others by exploring similar questions and additional content below.Recommended textbooks for you
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
Elementary Statistics: Picturing the World (7th E…
Statistics
ISBN:
9780134683416
Author:
Ron Larson, Betsy Farber
Publisher:
PEARSON
The Basic Practice of Statistics
Statistics
ISBN:
9781319042578
Author:
David S. Moore, William I. Notz, Michael A. Fligner
Publisher:
W. H. Freeman
Introduction to the Practice of Statistics
Statistics
ISBN:
9781319013387
Author:
David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:
W. H. Freeman