Online Statistics Course
 Learn to gather, process and apply statistical data, for business, research or any other purpose.
 Self paced course that caters equally to the slow or fast learner
 Support from experts trained in statistics at university, and experienced in the use of statistics in the real world
Statistics involves gathering, organising and analysis of data (normally numerical). In enables us to draw conclusions and make inferences on the basis of such analyses.
Descriptive statistics describe a set of data, while inferential statistics make inferences about large groups based on data from a smaller subset of the group. To infer means to draw a conclusion based on facts or premises. Thus an inference is the end result; a proposition based on the act of inferring.
Statistical data is critical to management of every commercial or government enterprise. Without statistics, our understanding of society, and the physical world; not to mention economics; would be greatly diminished.
COURSE STRUCTURE
There are 10 lessons as follows:
 Introduction
 Distributions
 Measures of central tendency
 The Normal curve and Percentiles and Standard Scores
 Correlation
 Regression
 Inferential Statistics
 The t Test
 Analysis of variance
 Chi square test
Each lesson culminates in an assignment which is submitted to the school, marked by the school's tutors and returned to you with any relevant suggestions, comments, and if necessary, extra reading.
What is in each lesson?
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
 Cooeficient of determination
 Scatter plots
 Product movement forlinear 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 independant samples
 t Test for dependant (paired) samples
9. Analysis of variance
 Scope and application of ANOVA
 Factors and levels
 Hypothesis
 Calculate degrees of freedon
 Calculate sum of squares within and between groups
 Calculate mean square
 Calculate F
10. Chi square test
 Chi quare goodness of fit test
 Calculate degrees of freedom
 Chi square test of independance
 Calculate expected frquencies
 Degrees of freedon
 Contingency tables
 Find expected frequencies
 Calculate degrees of freedom
WHAT THE COURSE COVERS
Here are just some of the things you will be doing:
 To familiarise the student with different statistical terms and the elementary representation of statistical data.
 To familiarize the student with distributions, and the application of distributions in processing data.
 To apply measures of central tendency in solving research questions
 Demonstrate and explain the normal curve, percentiles and standard scores.
 To understand the methods of correlation that describes the relationship between two variables.
 To make predictions, with regression equations and determine how much error to expect, when making the predictions.
 To understand the basic concepts of underlying the use of statistics to make inferences.
 To examine the difference between the means of two groups with the t Test.
 Understand the use of ANOVA (Analysis of Variance) in analysing the difference between two or more groups.
 To introduce and apply the concept of Non Parametric Statistics.
The Language of Statistics
Data – simply means information, usually in statistics this means numerical data (numbers).
Data may be raw (numerically unorganised) or in arrays (organised into ascending or descending order of magnitude). The difference between the largest and the smallest value in the array gives us the range.
Variables A variable is a quantity or value denoted by a symbol. However in statistics we take this a step further, often in statistics we study living things, and not surprisingly they differ, often greatly between themselves. So variables are also measures of characteristics which may differ. If it only ever has one value then it is a constant (ie: it is not a variable!). If it can only assume definite values, then it is known as a discrete variable (e.g. the number of children in a family, N, can only be a whole number). If it can assume a value between two given variables then it is known as a continuous variable (e.g. the height of an individual, H, could be 170cm, 170.7cm, 170.79461cm depending on the accuracy of the measurement)
Nominal – the use of names to help measure variables. Variables measured on such a scale are known as categorical or qualitative variables. In this type of scale, variable may be assigned to descriptive categories, for example gender may be either male or female. Each category may then be assigned a number that does not denote importance, rank or size. The number is arbitrary. For example; blonde = 1, black = 2, grey = 3 and brown = 4.
Ordinal – the order in which things occur. Eg. 1,2,3,4…. Unlike the numbers assigned arbitrarily in nominal scales, in ordinal scales the numbers do imply rank on a continuous scale.
The scale used depends on the variable it is describing. Hence a race would rank accordingly from first place to last. The winner would be 1, second place 2 and so on. Note that the information here is only about rank and it does not describe the variability within placements. For example in a horse race we might know that racehorse 1 came first, and racehorse 2 came second, but we are not told about how close together these racehorses were.
Interval – the ‘distance’ between two or more values. eg. Differences in temperature. The distinguishing feature of interval scales is the lack of an absolute zero point. The characteristic cannot be not there, hence zero on the scale does not imply absence. The units used in such a scale are measured equally, but because zero does not imply absence, we cannot measure the ratio between values.
Ratio – the relationship between two or more values. eg. Differences in the height or weight of objects. The same as an interval scale with equal distances along the scale meaning the same thing no matter where on the scale you are except that zero on the scale does represent the absence of the variable being measured. Thus we can measure ratios, for example 4 apples is twice as much as 2 apples.
ENROL & LEARN MORE Study one step at a time, and gradually build a solid grasp of this subject in your own time.


