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Diploma in Statistics

  • Description
  • Outcome
  • Certification
  • Statistics and statistical methods play a major role in the work environment in areas such as business, science, finance, economics, engineering to mention just a few. It is very important that people are comfortable with reading statistics and using statistical methods. This free online Diploma in Statistics will give you the knowledge and understanding of basic statistical methods such as sampling and collecting data, probability, distributions, regression analysis. By completing this course you will gain the knowledge and understanding to confidently read statistics and apply statistical methods within your daily working environment.

  • Upon completion of this course you will learn how to collect and analyse data. You will gain a good knowledge of the graphs that can be used and the reason for using these graphs.You will have a good understanding of probability, univariate data, and bivariate data. You will know the difference between normal, binomial and hypergeometric distribution. You will be able to do a regression analysis. You will understand the benefits of analytical methods such as trend analysis, residual analysis and the calculation of a seasonal index.

  • All Alison courses are free to study. To successfully complete a course you must score 80% or higher in each course assessments. Upon successful completion of a course, you can choose to make your achievement formal by purchasing an official Alison Diploma, Certificate or PDF.

    Having an official Alison document is a great way to share your success. Plus it’s:

    • Ideal for including in CVs, job applications and portfolios
    • An indication of your ability to learn and achieve high results
    • An incentive to continue to empower yourself through learning
    • A tangible way of supporting the Alison mission to empower people everywhere through education.

Diploma in Statistics
  • Free

  • 6-10 Hours

  • XSIQ

  • Assessment

  • Certification

  • 250 Pts

Modules List( 25 )
  • Module 1 - Introduction to Statistics
  • Module
    1
    Collecting and analysing data
    • Summarising data - overview
    • Mode
    • Mean
    • Median
    • Mode, mean, median
    • Comparing mode, mean, median
    • Range of data
    • Inter-quartile range
    • Review of summarising data
  • Module
    2
    Types of Graphs
    • Types of Graphs
    • Ice cream pictograph
    • Column and bar graphs
    • Examples of column graphs
    • Pie charts
    • Examples of pie charts
    • Line graphs
    • Temperature line graphs
    • Types of graphs
    • Interpreting column graphs
    • Manchester flights bar graph
    • Movie line graph
    • Interpretation of a sports pie graph
    • Review of graphs
  • Module
    3
    Frequency and Graphs
    • Overview of Frequency and graphs
    • Nominal data
    • Discrete data
    • Continuous data
    • Frequency tables with nominal data
    • Frequency tables with discrete data
    • Frequency tables - discrete data and summary statistics
    • Mean from frequency tables - discrete data
    • Interpreting column graphs
    • Family size cumulative frequency
  • Module 2 - Probability
  • Module
    4
    Probability
    • Introduction to probability
    • Probability words
    • Words describing chance
    • Finding probabilities theoretically
    • Probability with equally likely outcomes
  • Module
    5
    Probability and Relative Frequency
    • Probability and relative frequency
    • Short-run coin tossing
    • Short-run dice rolling
    • Predicting from past experience
    • Towards probability with coins
    • Towards probability with dice
    • Probability as long-run relative frequency
  • Module
    6
    Probability and Odds
    • Probability and Odds
    • Odds
    • Odds on
    • Odds and probability
    • Fair or unfair?
    • Deciding fairness using probability
  • Statistics Assessment 1
  • Statistics Assessment 1
    • Assessment 1
    • Statistics 1 Assessment
  • Module 3 - Summary Statistics
  • Module
    7
    Summary Statistics
    • Summary statistics
    • The mean
    • The median – definition
    • Cumulative frequency
    • Cumulative frequency graph
    • The mode
    • Advantages and disadvantages of the mean
    • The median for even data sets
    • Advantages and disadvantages of the median
    • The mean - example
    • The median - example
  • Module
    8
    Range
    • The soccer activity
    • The range
    • The interquartile range
    • The interquartile range - example 1
    • The interquartile range - example 2
    • The standard deviation
    • Boxplots
    • Boxplots - example
    • Using your calculator
  • Module
    9
    Discrete Random Variables
    • Random variables
    • Discrete probability distribution
    • The mean and variance of a discrete random variable
    • Standard deviation as a measure of spread
  • Module 4 - Types of Data
  • Module
    10
    Univariate Data Part 1
    • Introduction
    • Types of data
    • Types of univariate data
    • Numerical data
  • Module
    11
    Univariate Data Part 2
    • Displaying data
    • Bar graphs
    • Stem and leaf diagrams 1
    • Stem and leaf diagrams 2
  • Module
    12
    Bivariate Data
    • Introduction to Bivariate Data
    • Dependent and independent variables
    • Percentage tables
    • Parallel boxplots
    • Back-to-back stemplots
    • Graphical display of bivariate data
  • Module 5 - Distributions
  • Module
    13
    Normal Distribution
    • The normal curve
    • Continuous random variables and the normal distribution
    • Calculation of probabilities for a normal distribution
    • Approximating the binomial distribution
  • Module
    14
    Binomial Distribution
    • Binomial probability function
    • Number of successes in a given number of trials
    • Effect of changing the parameter p
    • Effect of changing the parameter n
    • Mean and variance of a binomial random variable
  • Module
    15
    Hypergeometric Distribution
    • Sampling without replacement
    • Mean of a hypergeometric random variable
    • Variance of a hypergeometric random variable
    • Mean and variance of a hypergeometric random variable example 1
    • Mean and variance of a hypergeometric random variable example 2
    • Formula for calculating probabilities
    • Calculating probabilities
  • Statistics Assessment 2
  • Statistics Assessment 2
    • Assessment 2
    • Statistics 2 Assessment-1
  • Module 6 - Regression Analysis
  • Module
    16
    Regression
    • Introduction to the regression line
    • Finding the equation of a regression line
    • Interpretation of slope and intercept
    • Practice question
    • The three-median regression line
    • Using your calculator
    • The three-median regression example
    • The three-median regression practice questions
    • The least squares regression line
    • Making predictions from a regression line
  • Module
    17
    Coefficient of Determination
    • Scatterplots
    • Scatterplots: using your calculator
    • Pearson's product moment correlation coefficient, r
    • Calculating r
    • The coefficient of determination
    • Practice question
    • Strength of association
  • Module
    18
    Non-linear Data
    • Non-linear data
    • Square transformation
    • Log transformations
    • Reciprocal transformation
    • Example 1
    • Example 2
  • Module 7 - Analytical Methods
  • Module
    19
    Trend Analysis
    • Trends
    • Cyclic patterns
    • Random patterns
    • Describing patterns in time series data
    • Seasonal patterns
    • Smoothing a time series
    • Median smoothing
    • Smoothing using moving averages
    • Smoothing - example 1
    • Smoothing - example 2
  • Module
    20
    Residual Analysis
    • Introduction to Residual Analysis
    • Residual analysis - part 1
    • Plotting the residuals
    • Residual analysis - part 2
    • Residual analysis - part 3
  • Module
    21
    Calculating a Seasonal Index
    • Calculating a seasonal index
    • Interpreting seasonal indices
    • Seasonal movements
    • Deseasonalising the data
    • Deseasonalising the data – example
  • Statistics Assessment 3
  • Statistics Assessment 3
    • Assessment 3
    • Statistics 3 Assessment-1
  • Module 8 - End of Course Assessment
  • End of course Assessment
    • End of course Assessment
    • End of Course Assessment
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