Rise above the mean with statistics!
Strengthen your career with this course in statistics. If you want to develop scientifically-based research studies, this is your starting point. Learn how to interpret data sets, and how to prove your point based on scientifically proven methods
This course provides the most essential knowledge and skills required by consultants and researchers in a wide variety of disciplines. This course assumes a basic knowledge of Statistics, at least to the level covered by our Research Project I course.
Lesson Structure
There are 10 lessons in this course:
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Introduction
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Key terms and concepts: data, variables
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Measurements of scale: nominal, ordinal, interval,ratio
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Data presentation
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Probability
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Rounding of data
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Scientific notation
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Significant figures
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Functions
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Equations
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Inequalities
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Experimental design
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The normal curve
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Data collection
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Simple, systemic, stratified and cluster random sampling
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Remaining motivated to learn statistics
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Distributions
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Scope and nature of distributions
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Class intervals and limits
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Class boundaries
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Frequency Distribution
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Histograms
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Frequency polygons
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Normal distributions
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Other distributions
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Frequency curves
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Measures of central tendency
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Range, percentiles, quartiles, mode, median, mean
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Variance
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Standard deviation
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Degrees of freedom
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Interquartile and semi interquartile deviations
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The Normal curve and Percentiles and Standard Scores
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Normal distribution characteristics
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Percentiles
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Standard scores
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Z scores
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T score
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Converting standard scores to percentiles
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Area under a curve
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Tables of normal distribution
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Correlation
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Scope and nature of Correlation
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Correlation coefficient
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Coefficient of determination
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Scatter plots
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Product movement for linear correlation coefficient
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Rank correlation
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Multiple correlation
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Regression
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Calculating regression equation with correlation coefficient
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Least squares method
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Standard error of the estimate
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Inferential Statistics
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Hypothesis testing
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Test for a mean
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Errors in accepting or rejecting null hypothesis
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Levels of significance
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One and two tailed tests
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Sampling theory
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Confidence intervals
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The t Test
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Assessing statistical difference with the t test
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t Test for independent samples
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t Test for dependant (paired) samples
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Analysis of variance
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Scope and application of ANOVA
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Factors and levels
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Hypothesis
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Calculate degrees of freedom
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Calculate sum of squares within and between groups
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Calculate mean square
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Calculate F
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Chi square test
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Chi square goodness of fit test
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Calculate degrees of freedom
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Chi square test of independence
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Calculate expected frequencies
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Degrees of freedom
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Contingency tables
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Find expected frequencies
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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
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Become familiar with different statistical terms and the elementary representation of statistical data.
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Become familiar with distributions, and the application of distributions in processing data.
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Apply measures of central tendency in solving research questions
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Demonstrate and explain the normal curve, percentiles and standard scores.
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Explain methods of correlation that describes the relationship between two variables.
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Make predictions with regression equations.
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Determine how much error to expect when making the predictions.
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Explain the basic concepts of underlying the use of statistics to make inferences.
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Analyze the difference between the means of two groups with the t Test.
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Describe the use of ANOVA (Analysis of Variance) in analysing the difference between two or more groups.
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Apply the concept of Non Parametric Statistics
There are different types of statistics.
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.
Understanding how to gather and analyze statistics is the starting point for using statistical data in real world situations, to make better decisions for planning and management, at work, play or in any other aspect of your life.
When you comprehend these things, you will understand how statistics is something that can make your life better, wherever you live, and whatever you do.
HOW CAN THIS COURSE HELP YOU?
This course will introduce you to the science behind statistics - there is a growing need for data science experts today. Almost all businesses and industries use statistical data to see how their businesses are trending and how business can be made to grow by analysing trends and directing business policy and decisions.
The role of the data scientist is to analyse trends, predict future directions and use this statistical analysis to suggest better ways to improve and grow businesses.
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