Problem 1:
a) Plot the data over time.
b) Calculate the seven-point moving averages for the series.
c) Which type of decomposition model (additive or multiplicative) should you use here?
Problem 2:
a) Plot a clustered bar chart of the data, with stream as the x-axis label.
b) Plot a stacked bar chart of the data, with employment status as the x-axis label.
c) Plot a pie chart from the above data to show the relative size of each employment status in all.
d) Explain what these charts tell you about graduate employment, by employment status and by stream.
Problem 3:
a) Aggregate this data into a suitable frequency table using an interval of 10.
b) Plot a histogram of the data, using the same scale and type of chart for each exam.
c) For each exam calculate:
i. The mean mark
ii. The median
iii. The mode
iv. The standard deviation
d) Commenting briefly on the two sets of exam scores. This can be based on the results of your analyses in parts (a) to (c) and any additional statistical measures you consider appropriate. What is the conclusion you derive from the above?
Problem 4:
(a) Plot a scatter diagram of the data. What does your chart suggest?
(b) Calculate the correlation coefficient for the data.
(c) Determine the regression equation that can be used to estimate the average sale price of a house from a given house size.
(d) Explain what the following represent in the context of this particular data set:
(i) The correlation coefficient
(ii) The gradient (slope) of the regression equation
(iii) The intercept of the regression equation
(e) Use your regression equation to estimate the sales price if the house size is:
(i) 200 square meters
(ii) 300 square meters
Comment on both these predictions.