Statics For Financial Decisions Report 2022

Running Head: STATISTICS FOR FINANCIAL DECISIONS
Statistics for Financial Decisions
Name of the Student:
Name of the University:
Author Note:

Running Head: STATISTICS FOR FINANCIAL DECISIONS1
Table of Contents
Introduction................................................................................................................................2
Discussion..................................................................................................................................3
Scatter plot on market price versus annual % change............................................................4
Scatter plot on total number of square meters versus Sydney price......................................5
Scatter plot on age of house versus Sydney price..................................................................6
Assignment model..................................................................................................................7
Least square regression..........................................................................................................7
Estimated and significant value of the model........................................................................7
Coefficient of determination..................................................................................................8
Confidence interval................................................................................................................9
Hypothesis test.....................................................................................................................10
Goodness of fit.....................................................................................................................10
Prediction of Dependent variable.........................................................................................11
Conclusion................................................................................................................................12
References................................................................................................................................13

Running Head: STATISTICS FOR FINANCIAL DECISIONS2
Introduction
Regression analysis is a statistical process where we estimate the relationship between
the dependent and independent variable (Chatterjee and Hadi 2015). In this study we use
ordinary least square method to determining the relationship between one dependent and four
independent variable. Here we show the general equation of ordinary least square model.
Y = β0+ β1 X1+ β2 X2 + β3 X 3+ β 4 X4+ ε. (For five variables)
Here isY is dependent variable and Xi, i= 1, 2, 3, 4 are independent variable.” ε.” is called
error or residual.
The variable of this study are House Price Index (a) (b): Brisbane, Sydney and
Melbourne, 2002–03 to 2016–17. In this Market Price ($000) is dependent variable, Sydney
price Index, Annual % change ,Total number of square meters, Age of house (years) are all
independent variable. The number of observation in each variable is 15.
In this report we show scatterplot, confidence interval and mainly ordinary least
square model and hypothesis testing between the market price and the land size in total
number of square meters.

Running Head: STATISTICS FOR FINANCIAL DECISIONS3
Discussion
Scatter plot on market price versus Sydney price
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Scatter Plot
Sydney price
Market price ($000)
Figure 1.1 Scatter Plot on Market price versus Sydney price
This figure 1.1 shows the trend line of the market prices on Sydney. In this figure we
show the relationship between two variable, in x- axis we take Sydney price, it is independent
and in Y-axis Market Price in dollars, it is dependent variable. The scatter plot shows that it is
linear. The highest market price shows the positive relationship. It shows that when Sydney
price increases similarly market price also increases. From the beginning part the two
variable is very close and concentrate but at the middle part it is slowly decrease and finally it
is again close. This shows that the relationship between two variable is good. We shows all