  # STATISTICS

Statistics and mathematics are an important part of our day to day lives. Study statistics and learn about an important part of doing any form of research.

Course Code: BSC304
Fee Code: S3
Duration (approx) Duration (approx) 100 hours
Qualification Statement of Attainment
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## Study statistics to be better at research, more effective as a planner or more profitable in business

Statistics is a very useful thing to learn. Statistical data is used in many professions; more than what you might think. A knowledge of statistics not only helps you collect and compile statistics; but it also enables you to better understand their relevance and application whether in business, research or day to day situations ranging from predicting the weather to determining what you should invest your money in.

## Lesson Structure

There are 10 lessons in this course:

1. Key Terms & 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 & Cluster Random Sampling
2. Distributions
• Scope & Nature of Distributions
• Class Intervals & 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 & Semi Interquartile Deviations
4. The Normal Curve & Percentiles & Standard Scores
• Normal Distribution Characteristics
• Percentiles
• Standard Scores
• Z Scores
• T Score
• Converting Standard Scores to Percentiles
• Area Under a Curve
• Table of Normal Distribution
5. Correlation
• Scope And Nature of Correlation
• Correlation Coefficient
• Coefficient of Determination
• Scatter Plots
• Product Movement For Linear 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
• Sampling Distribution of the Mean
• Central Limit Theorem
• Confidence Intervals
8. The t Test
• Assessing Statistical Difference with the t Test
• t Test for Independent Samples
• t Test for Dependant (Paired) Samples
• Standard t Test Probabilities
9. Analysis of Variance (ANOVA)
• Scope & Application of ANOVA
• Factors & Levels
• Hypothesis
• Calculate Degrees of Freedom
• Calculate Sum of Squares Within & Between Groups
• Calculate Mean Square
• Calculate F
10. Chi Square Test
• Chi Square Goodness of Fit Test
• Calculate Degrees of Freedom
• Chi Square Test of Independence
• Calculate Expected Frequencies
• Degrees Of Freedom
• Contingency Tables
• Find Expected Frequencies

## Aims

• Discuss different statistical terms and the elementary representation of statistical data.
• Discuss distributions, and the application of distributions in processing data.
• Use measures of central tendency for solving research questions
• Demonstrate and explain the normal curve, percentiles and standard scores.
• Explain methods of correlation that describes the relationship between two variables.
• Predict, with regression equations and determine how much error to expect
• Explain basic concepts of underlying the use of statistics to make inferences.
• Explain the difference between the means of two groups with the t Test.
• Explain the use of ANOVA (Analysis of Variance) in analysing the difference between two or more groups.
• Describe and 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.

This course will introduce you to the science behind statistics. 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.

This course will help you to analyse trends, predict future directions and use this statistical analysis to suggest better ways to improve and grow businesses.

## Course Contributors

The following academics were involved in the development and/or updating of this course. ###### Dr. Lynette Morgan (Crops)

Lyn has a broad expertise in horticulture and crop production. Her first job was on a mushroom farm, and at university she undertook a major project studying tomatoes. She has studied nursery production and written books on hydroponic production of herbs. ###### Christine Todd

University lecturer, businesswoman, photographer, consultant and sustainability expert; with over 40 years industry experience
B.A., M.Plan.Prac., M.A.(Social).
An expert in planning, with years of practical experience in permaculture. ###### Barbara Seguel

Teacher and Researcher, Biologist, Aquaculture expert.
Barbara has a B.Sc. and M.Sc in Aquaculture Engineering.
Over the past decade, Barbara has worked in Hawaii, Mexico, Chile, New Zealand, and is now settled in Australia. She has co authored severa

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