**Improve your Ability to Collect, Analyze and Communicate Complex Statistical Data **

Statistics is an essential tool in many areas of research including psychology and the sciences. This course provides the most essential knowledge and skills required by consultants and researchers in a wide variety of disciplines. Topics covered include measures of central tendency, correlation, regression t-test, inferential statistics such as chi square and ANOVA.

Course Structure and Content

There are ten lessons in this course as outline below:

1. Introduction

- Key terms and concepts: data, variables
- Measurements of scale: nominal, ordinal, interval,ratio
- Data presentation
- Probability
- Rounding of data
- Scientific notation
- Significant figures
- Functions
- Equations
- Inequalities
- Experimental design
- The normal curve
- Data collection
- Simple, systemic, stratified and cluster random sampling
- Remaining motivated to learn statistics

2. Distributions

- Scope and nature of distributions
- Class intervals and limits
- Class boundaries
- Frequency Distribution
- Histograms
- Frequency polygons
- Normal distributions
- Other distributions
- Frequency curves

3. Measures of central tendency

- Range, percentiles, quartiles, mode, median, mean
- Variance
- Standard deviation
- Degrees of freedom
- Interquartile and semi interquartile deviations

4. The Normal curve and Percentiles and Standard Scores

- Normal distribution characteristics
- Percentiles
- Standard scores
- Z scores
- T score
- Converting standard scores to percentiles
- Area under a curve
- Tables of normal distribution

5. Correlation

- Scope and nature of Correlation
- Correlation coefficient
- Coefficient of determination
- Scatter plots
- Product movement for linear correlation coefficient
- Rank correlation
- Multiple correlation

6. Regression

- Calculating regression equation with correlation coefficient
- Least squares method
- Standard error of the estimate

7. Inferential Statistics

- Hypothesis testing
- Test for a mean
- Errors in accepting or rejecting null hypothesis
- Levels of significance
- One and two tailed tests
- Sampling theory
- Confidence intervals

8. The t Test

- Assessing statistical difference with the t test
- t Test for independent samples
- t Test for dependent (paired) samples

9. Analysis of variance

- Scope and application of ANOVA
- Factors and levels
- Hypothesis
- Calculate degrees of freedom
- Calculate sum of squares within and between groups
- Calculate mean square
- Calculate F

10. Chi square test

- Chi square goodness of fit test
- Calculate degrees of freedom
- Chi square test of independence
- Calculate expected frequencies
- Degrees of freedom
- Contingency tables
- Find expected frequencies
- Calculate degrees of freedom

**Using Statistics**

Collecting statistical information is the starting point for any task involving statistics; but alone, there is little point to collecting the information. It is only when statistics are organized, analyzed and communicated to others, that they really find value.

A good statistician needs to be a communicator; and there are many different tools which can be used to communicate statistical data to others. One of the most common tools is to use a graph.

Graphs are used to show proportional relationships and trends. The most commonly used types of graph are:

- Pie chart - a circle divided into sections, with each section representing a percentage of the whole
- Bar graph - vertical or horizontal bars that show a comparison between categories.
- Line graph - a line that links points plotted in relation to two axes drawn at right angles. A line graph shows trends or the change of one or more variables over time.
- Flow chart - a chart that displays the major steps and links in a process.

Some tips for preparing graphs:

- Place a simple and descriptive title directly above or below the graph.
- Use an appropriate scale, e.g. if an age group is 0 to 10 years, don’t show a scale of 0 to 20 years.
- Keep the design simple.
- Prepare a separate graph for each point.
- Tie the graph to the text, refer to it in the text, and place it as close to the text as possible.
- Limit the graphs to two or three sizes within a document, using the table outline (in two or three sizes) to create a sense of uniformity.