I need to create a statistics project paper using regression model.

User Generated

xngunebfrr

Mathematics

International business

Vilnius University

Description

Select business or economic problem and try to solve it using a regression model or a time series forecasting model (for example: exponential smoothing). The problem can be related with forecasting, asset pricing or relationship assessment between different variables.


Project Structures:

Introduction, Methods, Results, Conclusion and Excel file + Word file.

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Project guidelines Select business or economic problem and try to solve it using a regression model or a time series forecasting model (for example: exponential smoothing). The problem can be related with forecasting, asset pricing or relationship assessment between different variables. The following project structure is recommended: An introduction. This part describes the problem, motivation of its selection and etc. It also describes the purpose of the research and formulates hypotheses that will be tested during the research. Methods. This section describes how the research will be conducted. For example, what research methods will be used, what data will be collected, how it be collected, and etc. Results. The results and interpretation are presented. Conclusions. It should be noted that conclusions are not the summary of the work. The conclusions need to answer the questions raised in the introduction. The scope of the project is not limited, i.e. there is neither a minimum nor a maximum number of pages. Data sources must be indicated in the study. The work can be done both independently and in groups. There can be no more than 3 students within the group. The work should be submitted through the Moodle system. It should consist of two files, i.e. MS Word file describing the entire research and MS Excel file containing all the data used in the research and the calculations performed.
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Explanation & Answer

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Area (sq m) Selling Price (in million)
35
13
37
17
39
15
40
16
43
18
48
19
50
20
55
25
60
35
65
37
70
38
75
40
80
43
85
44
90
50
95
54
100
60 predicted Price (in millions)=
135
73
137
77
139
75 Area (sq m)=
140
76
143
78
148
79
150
80
155
85
160
95
165
97
170
98
175
100
180
103
185
104
190
110
195
114
200
120
235
133
237
137
239
135
240
136
243
138

We will be using Linear Regression along with Gra
Using that relation we can predict others house pr
Let Theta0 and Theta1 be initial weights

Now, initially linear equation be
h(x) = theta0 + theta1 * x
Now, updating linear equation by using gradient d
Now, after training dataset
theta0 =
-6.17366
theta1 =
0.602182
so, final linear equation will be
h(x) = -6.17366081 + 0.60218233 * x
so, for prediction
114.2628

200 change value here to get predicted val

r Regression along with Gradient Descent Algorithm to find final linear Equation which is relation between area and price of house
can predict others house prices
be initial weights
Gradient Descent Algorithm
theta0 = theta0 - (alpha/m) * Σ(h(x(i) - y(i)))
theta1 = thet...

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