MATLAB Functions and Results
arbit.m – This function takes in the unemployment data and takes every third value after
every three months to match with the corresponding GDP data.
oilagri.m - This function gets data about oil prices and agricultural products' prices and finds
the correlation coefficient. It plots a regression plot and determines an equation to predict
changes in agricultural products' prices at future date, and also the probability of the predicted
trend being correct. The data is monthly from January, 1980 to August, 2011.
Correlation coefficient was found to be 0.27644
Regression equation is [0.10069 X (oil prices) + 0.23307]
Probability of predicting correct trend (increase or decrease) is 0.59103
oilind.m - This function gets data about oil prices and industrial materials' prices and finds
the correlation coefficient. It plots a regression plot and determines an equation to predict
changes in industrial materials' prices at future date, and also the probability of the predicted
trend being correct. The data is monthly from January, 1980 to August, 2011.
Correlation coefficient was found to be 0.36490
Regression equation is [0.13752 X (oil prices) + 0.30521]
Probability of predicting correct trend (increase or decrease) is 0.57784
agrind.m - This function gets data about agricultural products' prices and industrial materials'
prices and finds the correlation coefficient. It plots a regression plot and determines an
equation to predict changes in industrial materials' prices at future date, and also the
probability of the predicted trend being correct. The data is monthly from January, 1980 to
August, 2011.
Correlation coefficient was found to be 0.65582
Regression equation is [0.67859 X (agri. prices) + 0.13888]
Probability of predicting correct trend (increase or decrease) is 0.71768
unempag.m - This function gets data about unemployment and agricultural products' prices
and finds the correlation coefficient. It plots a regression plot and determines an equation to
predict changes in unemployment at future date, and also the probability of the predicted
trend being correct. The data is monthly from January, 1980 to August, 2011.
Correlation coefficient was found to be -0.16392
Regression equation is [-0.00892 X (agri. prices) + 0.00936]
Probability of predicting correct trend (increase or decrease) is 0.39578
unempind.m - This function gets data about unemployment and industrial materials' prices
and finds the correlation coefficient. It plots a regression plot and determines an equation to
predict changes unemployment at future date, and also the probability of the predicted trend
being correct. The data is monthly from January, 1980 to August, 2011.
Correlation coefficient was found to be -0.16580
Regression equation is [-3.15420 X (ind. prices) + 0.31226]
Probability of predicting correct trend (increase or decrease) is 0.53826
unempgdp.m - This function gets data about GDP and unemployment and finds the
correlation coefficient. It plots a regression plot and determines an equation to predict
changes in unemployment at a future date, and also the probability of the predicted trend
being correct. The data is quarterly from January, 1948 to April, 2011.
Correlation coefficient was found to be -0.53879
Regression equation is [-0.12100 X (GDP figures) + 0.10584]
Probability of predicting correct trend (increase or decrease) is 0.36675