Essays on The Study of Houses' Price Assignment

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The paper "The Study of Houses' Price" is a wonderful example of an assignment on macro and microeconomics. Price Location Custom Square Metres 202500 1 0 126.945 193250 1 0 122.76 282250 0 0 158.1 309750 1 0 158.658 172500 1 1 125.364 174750 0 1 130.2 213750 1 0 176.7 217500 1 0 119.04 234750 0 0 132.804 205000 0 0 127.875 236250 0 0 146.94 243750 1 0 132.99 323750 0 1 348.75 423750 1 0 272.583 512500 1 1 246.45 135000 0 0 106.206 362250 1 1 159.03 181250 0 0 119.04 537500 1 1 271.653 218500 0 0 114.948 324750 1 1 255.099 167500 1 0 125.55 175000 0 0 139.965 262500 0 0 150.66 281250 1 1 159.03 233500 1 0 143.499 312500 1 0 199.95 154750 1 0 77.841 240000 1 0 146.289 317500 1 0 174.84 248750 1 0 139.5 218000 0 0 114.297 250000 0 0 142.755 199750 1 0 122.202 225000 1 0 130.2 284750 1 0 156.24 191500 0 0 111.6 145000 0 0 97.743 255000 0 0 149.358 219000 1 0 107.508 499750 1 1 239.94 182500 1 0 95.511 230500 1 0 123.318 243750 0 0 161.727 537500 1 1 247.752 312500 1 1 211.761 187500 1 0 124.434 399750 1 1 226.92 537500 1 1 264.864 300000 1 0 181.164 Task 1: Descriptive statistics 95% confidence interval for the prices of houses The confidence interval means that we are 95% confident that the price of houses lies between. Task 2: Hypothesis testing Hypothesis testing is done as follows: Null and alternate hypotheses The and, the critical value is T-test is computed using as follows: Reject null hypothesis since falls in the critical region. There is sufficient evidence to show that the price of houses is $280,000. Task 3 Regression equation R-square value shows whether a model explains most of the variations in the data.

A higher value of R-square indicates that a model explains most of the variation while a lower value indicates that a model explains less. The constant value of 21321 shows the price of a house with zero square meters. On the other hand, the slope indicates the rate at which price changes with a change in square meters. According to the regression output above, the standard error is 59192. It shows variability of points from the line of best fit.

In this case, there is greater variability. In testing whether or not the coefficients of the slope are significant, the intention is to determine if the slope carries a different value from zero. During the process, a comparison is made between p-value and the level of significance. The p-value for the slope of the stock price index is approximately 0, which is less than the level of significance of 0.05. This indicates that the slope coefficient is significant at 0.05. Scatter plot of price vs square meters is displayed below Coefficient of determination evidenced by summary regression output is 0.6931.

This means that the fitted model explains only 69.31% of variability in exam scores. The regression equation is able to predict selling price for a house with 50 square meters. This is done as follows The other factor that can be included is distance from the urban centre. Question 2 Original Time Series Plot Time series plot of bookings versus time Summary of the plot By observation, the plot above has a cyclical component with up and down movement around the trend. The fluctuations are equal across the period under investigation. Besides, it has a trend illustrated by an increasing long-term pattern. The first component that is visible is from the time series plot is trend.

There is a gradual upward movement of the data over time. Secondly, the plot displays seasonality component. In this case, there is a pattern of fluctuation around the trend line with regular interval. 4 point centred moving average Smoothing the data using a 4 period centred moving average Time series plot that shows both the original and the 4-period centred moving average on the same plot The four-period Exponential smoothing Alpha = 0.5 Alpha = 0.2 Original Exponential smoothing with an alpha of 0.2 is the preferred option because it gives forecasts that closely reflect the actual values.

This shows that values of low values of alpha have a greater smoothing effect.

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