Online Statistics Course

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.

There are 10 lessons in this course:

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

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.

Aims:

• Become familiar with different statistical terms and the elementary representation of statistical data.
• Become familia with distributions, and the application of distributions in processing data.
• Apply measures of central tendency in solving research questions
• Demonstrate and explain the normal curve, percentiles and standard scores.
• Explain methods of correlation that describes the relationship between two variables.
• Make predictions with regression equations.
• Determine how much error to expect when making the predictions.
• Explain the basic concepts of underlying the use of statistics to make inferences.
• Analyze the difference between the means of two groups with the t Test.
• Describe the use of ANOVA (Analysis of Variance) in analysing the difference between two or more groups.
• Apply the concept of Non Parametric Statistics

For more information on this course, please request your free course information pack.