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causes a reduction of median housing price by 0.028 in the first variant, a reduction of 0.027 in the second variant, and a reduction of 0.027 in the third variant.
In the third variable, nitrogen oxide concentrations in parts per hundred million (NOX), the coefficients are 1.479 and 1.529 in the second and third variants respectively. These figures suggest that 1 percent change in nitrogen oxide concentration increases the medium housing price by 1.479 in the second variant and by 1.529 in the third variant.
Beta coefficient is the measure of the sensitivity of the estimates in influencing the median housing price. In the estimates, the beta coefficient is the slope of the model summarized into β0, β1, β4, β3, and β2.
Normally, the coefficients would imply 1 percentage change in the estimate 1 and 2 would cause an increase of 0.566 and 0.0261. However, using the beta approach, the two coefficients are below, suggesting that they are below the median housing price.
6. Suppose in model (3) I added in the variable NOX DCHAS, resulting in ln(MVi) = β0 + β1RMi + β2 ln(DISi) +β3NOXi + β4DCHAS;i + β5 NOX DCHAS +Ɛi . How would the interpretation of Ɛ3 change in model (3) after the inclusion of this variable? What is the interpretation of Ɛ5 in this model?
9. Given that the BP and White tests yield the same conclusion regarding the presence of heteroskedasticity, does this imply that the BP test is as good as the White test? Explain your reasoning in detail.
Heteroskedasticity implies to the circumstance when the variability of a variable is unequal across the range of values of a second variable that predicts it. In this circumstance, it means that the Bp test is as good as the white test since in the presence of heteroskedasticity, it is expected to be different for variability, which is not the case.
Heteroskedasticity does not necessarily imply an error, but only imply variableness, i.e. variability of a variable is unequal across the range of
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The availability and increase of other sources of income for the family was also considered to find out weather the same affected their level of commitment at work. This was done using the variable nwifeinc. The estimated coefficients had the signs as expected and were of statistical significance, this was demonstrated clearly buy the patterns displayed by the respective graphs of the variable coefficients.
The sample partial autocorrelation pt at lag t is the correlation between the two sets of residuals obtained from regressing yi and yi-t on the set of values in between them. Box and Jenkin’s method has three steps,
is the coefficient for the In(size) and the value is 0.058 implying that there exists a positive relationship between the dependent variable (ln(GDP percapita)) and the independent variable (In(size)). As such, a unit
Model 2 is therefore valid and this means that ln(population) and ln(GDP) are inversely proportional to government’s share of expenditure relative to GDP.
The R2 value in model 2 would remain the same despite change in unit measure of GDP.
ACF is significant across the years. However, at year 1 it is quite high but it has a decreasing uniform trend across the years; implying that the data is not stationary.
The above plot shows the partial autocorrelation of Lrpsi. PACF is
It is because of the changes in level B taking place before those in level A. Allowing B to precede A would result in modifications in A before B and therefore, giving a positive feedback as opposed to the current
census tract; DIS is a weighted distance to five major employment centers in the Boston region, NOX measures nitrogen oxide concentrations in parts per hundred million. DCHAS is a dummy variable which signifies if the Charles River passes through the census tract, i.e. DCHAS = 1