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# Do you believe that the population proportion of flat dwelling type could be 50%?

INSTRUCTIONS TO CANDIDATES

2. Section 2: Do you believe that the population proportion of flat dwelling type could be 50%?

Using Dataset 1, provide the frequency and the proportion (either as a decimal or a percentage) for each category for the variable Dwelling Type. You also need to provide a graphical display that easily shows the proportion of each category.

Then, construct a 95% confidence interval of the population proportion related to the question above.

3. Section 3: Is the Average Weekly Rent of flats in NSW more than \$590?

Using Dataset 1, first filter the dataset to include only flat dwelling type. Then describe the Weekly Rent distribution of all flats. You need to provide numerical summary (sample size, mean, standard deviation and median) as well as graphical display which shows the outliers, if any.

Then perform a suitable hypothesis test related the question above at 5% level of significance.

4. Section 4: Is there a difference in Weekly Rent among three different postcodes in Greater Sydney?

Using Dataset 1, first filter the dataset to include only the following postcodes: 2031, 2135, 2150 (of all types of dwellings). Then, describe the distribution of the Weekly Rent for all rental properties in those 3 postcodes. You need to provide both numerical summary as well as graphical display which shows any outliers.

Then, perform a suitable hypothesis test relevant to the question above, at 5% level of significance.

5. Section 5: Can we predict the weekly rent of all dwelling types in three postcodes in Greater Sydney by using the number of bedrooms?

First, filter Dataset 1 to include only the postcodes 2031, 2135, 2150 (of all types of dwellings). Note that this is the same as the filter in Section 4. Then describe the relationship between the number of bedrooms and the weekly rent. You need to provide both numerical summary as well as graphical display.

Next, perform a regression analysis and provide the regression output.

Finally, interpret the correlation coefficient, the coefficient of determination and the relevant p-values and use them to answer the question above.

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