The data set shows Sales Price, Area, Number of Rooms, Number of Bedroom, Age and River View

of 63 single family homes.Sales Price is in thousands and River View indicates whether a home has the

view of river or not. A home with the river view is code as 1 and with no view is given by 0.

Using a sample of 30 homes conduct three multiple regression analyses: (1) multiple

linear regression with the quantitative variables, (2) multiple linear regression both quantitative

qualitative variables, and (3) multiple regression with interaction.

Answer the following:

1.Specify the regression models.

2.Explain your regression models. There are three of them: multiple linear regression with all quantitative variables,multiple linear regression with all the quantitative variables and dummy variable, and multiple linear regression with the quantitative variables and interaction effect.

3.Explain the basic assumptions in regression model.

4.Identify the independent and dependent variables.

5.Explain the nature of the variables.

6.Find the descriptive statistic of all the variables.

7.Generate appropriate charts each variable (pie, bar charts, histogram, box plots, stem-and-leaf, and QQ plot).

8.Interpret the major findings (in tasks# 6 and #7).

9.Conduct the correlation analysis with the software.

10.Explain the findings of correlation analysis.

11.Generate scatter plots with the quantitative variables.

12.Explain the outputs of scatter plots.

13.Conduct the following multiple regression analyses with the software (SPSS):

a.Linear regression analysis with the quantitative variables

i.Explain the findings of your regression analysis

ii.Conduct one forecast for a set of independent variables and find the residual

iii.Conduct hypothesis testing for the slope of an independent variable

iv.Conduct interval estimate for the slope of an independent variable

b.Linear regression analysis with all the six variables which includes the dummy variable

i.Explain the findings of your regression analysis with the dummy variable

c.Linear regression analysis all the quantitative variables and an interaction of two independent variables.

i.Explain the findings of your regression analysis with the interaction effect.

DATA

Residence 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Sales Price (in $1000) 53.5 49.0 50.5 49.9 52.0 55.0 80.5 86.0 69.0 149.0 46.0 38.0 49.5 105.0 152.5 85.0 60.0 58.5 101.0 79.4 125.0 87.9 80.0 94.0 74.0 69.0 63.0 67.5 35.0 142.5

Square feet 1008 1290 860 912 1204 1204 1764 1600 1255 3600 864 720 1008 1950 2086 2011 1465 1232 1736 1296 1996 1874 1580 1920 1430 1486 1008 1282 1134 2400

Rooms 5 6 8 5 6 5 8 7 5 10 5 4 6 8 7 9 6 5 7 6 7 5 5 5 9 6 5 5 5 9

Bedrooms 2 3 2 3 3 3 4 3 3 5 3 2 3 3 3 4 3 2 3 3 3 2 3 3 3 3 2 3 2 4

Age 35 36 36 41 40 10 64 19 16 17 37 41 35 52 12 76 102 69 67 11 9 14 11 14 16 27 35 20 74 15 15 16

View 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 0