7BSP1117 Financial Data Analysis Assignment Sample 2023
ANSWER 1
(A)
The regression equation for predicting the variability of operating expenses is as follows.
ln Y t = b1 + b2 ln X2t + b3 ln X3t + b4 ln X4t + et
In consideration with the given data set, the predictive model thus estimated is as follows.
Predictive Model
Operating Expenses: b2 ln Pricet + b3 ln Sales3t + b4 ln Time Trend4t + et
The estimated model has an R-Squared of 99.2% (Figure 1). It means that the model explains 99.2% of the variability in the dependent variable (Operating Expenses). b2, b3, and b4 is the slope co-efficient for the predictive model. b2 is equivalent to 0.282 and this means that 1-unit change in Price results in a 0.282 change in Operating Expenses. Similarly, b3 is equivalent to 1.420. This means that 1-unit change in Sales results in 1.420 change in Operating Expenses. b4 is 0.170. This means that 1-unit change in time-trend results in a 0.170 change in Operating Expenses.
(B)
(I)
H0: b3 = 0, H1: b3 ¹ 0
Using the p-value, it can be seen that b3 is 0.182. The p-value is greater than Alpha (0.05). Thus, the null hypothesis has been accepted (failed to be rejected) at the 5% level of significance.
(II)
H0: b3 = b4 = 0, H1: b3 or b4 ¹ 0
- F-Statistic: 331.074
- F-Critical: 5874
Given that the F-Statistic exceeds F-Critical, H0 has been rejected. Thus, the alternative hypothesis has been accepted at the 5% level of significance; b3 or b4 ¹ 0.
95% Confidence Interval for b2
- Lower Bound: 1.420 – (5.341 * 0.266): 0.835
- Upper Bound: 1.420 + (5.341 * 0.266): 2.005
The 95% interval identified above shows the range of values that one can be 95% certain does contain the true mean of the population.
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