Probability, hypothesis testing and regression analysis continued the topic of quantitative analysis in week 8.  Our discussion on the statistic techniques that we are using with the SPSS package focuses on the interpretation of outputs rather than the mathematics behind them. This seems reasonable given the limited time we have assigned to such a large area.

The first points covered were definitions of probability:

  • Marginal (simple) probability – rolling 3 six in a row with a standard dice => (1/6) x (1/6) x (1/6)
  • Joint probability P(AB) => P(A) x P(B)
  • Conditional Probability – I would stick with Bayes theorem => see below
Conditional Probability
Conditional Probability
  • Binomial Distribution – probability of a number times and event occurs given a true or false outcome and n trials. ie: how many times will head appear in 20 tosses of a coin.
  • Normal (Gaussian) distribution – Requires continuous random variables (ie age), see below
Normal distribution demands the percentages show for each standard deviation interval

Hypothesis testing and Regression analysis followed. The recurring theme is the significance value of less then 0.05 required for hypothesis support.

SPSS seems like a great tool for statistical analysis with all of the statistic methods widely used and relatively simple use.