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The Art in the Science of Statistics at Macquarie University.

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Presentation on theme: "The Art in the Science of Statistics at Macquarie University."— Presentation transcript:

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2 The Art in the Science of Statistics at Macquarie University

3 Protein Structure Prediction - from amino acid string to three dimensional structure

4 SAINT Proteins Developing a sequential protein structure prediction algorithm

5 Mathematical Finance and Statistical Modelling 1634: Tulipomania in Netherlands 1636: The Amsterdam stock exchange issued option contracts allowing tulip bulbs to be purchased for a fraction of the price of a real tulip bulb. If a future market price of a tulip at option maturity time T, is going to be greater than some value K specified in the contract, then the buyer of the contract gets a payoff ‘Market Price of a bulb at T’ –K, otherwise the premium paid for the contract is lost. 1 tulip bulb = A Farm, Live stock, or Life??? These options are called Vanilla type options ( or, European Options).

6 Listen Determining Aircraft movement using sound Aircraft Sonogram

7 Compare MI-EL (developed at MU) with other popular Reconstruction Algorithms in Emission Tomography MI-EL MEM SPS ICA iter 8iter 16iter 32iter 64 smoothing parameter h = 10 -5

8 The effect of driver’s age on occurrence and size of motor vehicle insurance claims

9 Demand for electricity varies by Month, Type and Time of Day

10 Local Correlation plots This graph displays Seasonality Stationarity Autocorrelation Interpretation. Stationarity: Splots have similar shapes. Seasonality: Repeating patterns at regular lag intervals. Autocorrelation: High autocorrelation is indicated by brown/red colour. No autocorrelation is indicated by dark blue colour. High autocorrelation is present at regular intervals along both axes. High autocorrelation at regular lags indicates seasonality. High autocorrelation at regular intervals along CO2 axis fluctuations within seasons.

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14 An objective is to investigate the relationship between phytoplankton and their environmental habitat using multivariate statistical methods. Environmental Statistics The first three principal components explain the overall abundance of phytoplankton studied and the seasonal patterns of a particular genus or an assemblage of various genera that prefer to live in a similar environmental habitat, either in warm or cool water. The study of seven-year temporal patterns of phytoplankton reveals general patterns of phytoplankton in the reservoir. Such patterns respond to a certain range of environmental factors. This range may be used as indication of a change in phytoplankton composition and abundance. Thus, the environmental factors associated with a situation where one organism invades original inhabitants may be identified. Sangdao Wongsai, PhD student, Blue-green algae

15 Grapharti Screen shot of Grapharti from ninemsn daily news polls.Graphartininemsn

16 Variation in temperature

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18 Seeing Things?

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20 The artists

21 Guess who!


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