Question

# here is the questions about econ291, i'm confusing about question 7,8,9,10. These four questions. 1 1 v Based on what we reviewed in lecture, a hedonic price model is a regression model that identiﬁes the
value of a differentiated product (a residential house in this case) to its internal factors. For this assignment, you will construct both a simple and a multiple hedonic model for residential housing
and estimate its parameters using real Canadian data. Do bigger houses sell for higher prices? To answer this question empirically, we will construct a simple
hedonic value model for an unspeciﬁed Canadian city using real data on 832 residential houses that were
sold in this city during a particular calendar year. These data are reported in the Excel data ﬁle
h0usemodelhedonic.xls. For this hedonic data set, the following variables are measured:
1- Each residential house has been identiﬁed with (ID).
2- Price of the residential housing (VALUE), measured in thousands of Canadian dollars. 3- Area of the house as lot size (LOT), measured in acres. 4- Time of the year that the house was sold, (QUARTER). [QUARTER =1 denotes the ﬁrst quarter
(Winter); QUARYER = 2 denotes the second quarter (Spring); QUARTER = 3 denotes the third
quarter (Summer); and QUARTER = 4 denotes the fourth quarter (Fall)]. Moreover, to augment LOT with the purpose of debunking more of the omitted variables, we need to
construct a multiple linear regression (question 10), with four (4) additional regressors, namely: 5- Number of bedrooms (BDRM), measured in units.
6- Number of bathrooms (BTHRM), measured in units.
7- Number of parking spaces in the driveway (DRIVEWAY), measured in units. 8- A binary (dummy) variable representing the house’s basements (BSMT; BSMT=1 if the house has
basement and BSMT=0 if the house does not have basement). Activity numbers 1 to 9 in here focus on the construction, components and applications of simple regression
models. On activity #10, you will run a multiple regression model of VALUE on all important variables using data 3- Test, at 5% level of significance, the null hypothesis of Ho : B1 = 0 , against a one-side alternative of
Ha : B1 &gt; 0. Deploy (i) the pre-specified level of significance approach and (ii) the p-value approach.
4- For the estimated regression in activity #1, construct a 95% confidence interval for the intercept
parameter Po , and use the estimated confidence interval to test the null hypothesis of Ho : Bo = 0
against the alternative of Ha : Bo # 0 as discussed in class.
5- Use the estimated regression in activity #1 to predict the mean vale of a residential house in this city
with a lot size of 0.5 acres.
6- With the assist of GRETL, construct the ANOVA table for the estimated regression in activity #1.
What are the values of the Total Sum of Squares (TSS), Explained Sum of Squares (ESS), and Sum
of Squared Residuals (SSR) from the ANOVA table?
Explain the meanings of these sums of squares.
What can you conclude about the relationship between the three sums of squares?
7- Provide an appropriate (and detailed) interpretation of the coefficient of determination for the
regression in activity #1. Note that only indicating &quot;good fit&quot; or &quot;poor fit&quot; does not constitute a
detailed explanation.
8- Test, at 5% level of significance, the null hypothesis of Ho : 1 = 0 , against a two-sided alternative
of Ha : B1 + 0 . Deploy (i) the pre-specified level of significance approach and (ii) the p-value
approach.
9- Is it possible that the model you estimated in activity #1 suffers from omitted variable bias? Explain.
10- Construct the regression model #10-2 in here. Then, compare the fits of the following two regressions
using SER, R2 and R2 :
10-1) VALUE; = Bo + BiLOT; + ui (from activity #1 above)
10-2) VALUE; = Bo+ BLOT; + B2BDRM; + B3BTHRM; + BADRIVEWAY; + BBSMT; + ui

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