Statistics is the science of learning from data. Gaining statistical knowledge will help you collect and analyse data and present accurate, compelling results. Whether you work in marketing, medicine or public policy – and almost anything in between – this online statistics course is ideal professional development for you.
Introduction to Statistics is an online professional development course that will provide you with vital statistical knowledge and skills required across disciplines.
In this online statistics course, you will learn statistical terms, applications of distributions in processing data, central tendency in solving research questions and methods of correlation between variables. You will also learn to make predictions with regression equations and error expectations and apply ANOVA and non-parametric statistics.
On completion of this course, you will have a comprehensive understanding of statistics to make better decisions in business and real-world situations.
Unit - Statistics
Unit 1 – Introduction
Key terms & concepts
Data, constant and variables
Measurements of scale
Nominal, Ordinal, Interval, ratio
Table and graphs
Rounding of data
Functions and equations
Experimental design – creating useful statistics
The normal curve
Simple random sampling
Systematic random sampling
Stratified random sample
Cluster random sampling
A word of encouragement
Unit 2 – Distributions
Class intervals and limits
General rules for creating a frequency distribution
Cumulative frequency polygon
Unit 3 – Measures of central tendency
Measures of central tendency
Deviation or variance
Degrees of freedom
Interquartile and semi-interquartile deviations
Example set of data
Unit 4 – The Normal Curve, Percentiles and Standard Scores
Normal distribution characteristics
Standard scores (z scores and t scores)
Converting standard scores to percentiles
Area under a curve
Table of normal distribution
Unit 5 – Correlation
Some points about correlation
Product moment for linear correlation coefficient (product moment formula)
Rank correlation (spearman’s formula for rank correlation)
Unit 6 – Simple linear regression
Calculating regression equation with the correlation coefficient
Least squares method
Unit 7 – inferential statistics
Test for a mean
Errors in accepting or rejecting null hypothesis
Levels of significance
One and two tailed tests
Sampling distribution of the mean
Central limit theorem
Confidence intervals for the mean
Unit 8 – The t test
Assessing statistical difference with the t-test
Prime purpose for using a t-test
T test for independent samples
T test for dependent (paired) samples
Unit 9 – Analysis of variance (ANOVA)
When would you use ANOVA?
Factors and levels
Calculate the degrees of freedom
Calculate the sum of squares for within and between groups
Calculate mean square
Unit 10 – Chi-Square
Chi square goodness-of-fit test
Calculate chi squared
Calculate degrees of freedom
Chi-square test of independence (Pearson’s Chi Square)
2 x 2 contingency tables
Calculate the expected frequencies
Degrees of freedom
Finding the expected frequencies
Degrees of freedom
Estimated duration 50 hours
Course Delivery and Start
Start anytime, self-paced and 100% online
Assessment will be comprised of written exercises, including short-answer questions, reflective tasks, short reports and/or projects. There are no examinations or due dates for assessment. As a result, you can complete training in your own time and at your own pace with the assistance of unlimited tutor support.
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