1. Chapter 13: Using Statistics
Introduction
13.1 Descriptive statistics
13.2 Formulating hypotheses
13.3 Selecting your test
13.4 Testing means
13.5 Testing correlation
13.6 Conclusion

2. Introduction Only quick overview
If you can’t do it yourself, ask an expert

3. 13.1 Descriptive statistics
Describe characteristics of population or sample
Size (n, N)
Mean (X, )
Variance (s, ) and Standard deviation (s², ŝ², ²)
Correlation (p, )
Line graph: Show relationship or trend
Bar graph: Visual comparison of values
Pie chart: Distribution of total

4. 13.2 Formulating hypotheses
Used if sample is representative of population
H0: =0  H1: >0, H0: 0,H0: =1

5. 13.3 Selecting your test Parametric and Non-parametric tests
Type I error: Reject hypothesis if it was true
Type II error: Accept hypothesis if was false
Level of significance (e.g. =0.05)
Probability of making a type 1 error, i.e. we are 95% certain that we may reject the hypothesis
Avoid type II error by never accepting a hypothesis

6. 13.4 Testing means H0: =0 and H0: X=Y z-tests and t-tests
Tables 13.1 and 13.2 to determine which test to be used when
Tables 13.3 and 13.5 to determine if H0 may be rejected

7. 13.5 Testing correlation H0: XY = 0
H1: XY < 0, H1: XY > 0, H1: XY  0
Calculate t and compare with table 13.5 with df=n-2
If the correlation is not linear, variables have to be transformed, e.g. logarithmically.

8. 13.5 Testing correlation: Contingency tables (²)
Used for correlation between nominal data
E.g. see table 13.7
H0: No relationship between two variables
Calculate ² and compare with Table 13.8
df = (n-1)(m-1)

9. Workshop
Question: Do you think that a messy desk is indicative of a persons’ productivity? Does gender or age have something to do with it?
(Note: The question is intentionally vague and ambiguous.)
Decide on the dependent and independent variables.
Formulate a hypothesis for this question.
Design an experiment
Use Excel and compile a set of hypothetical data
Analyse the data
Draw a conclusion