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AGR302Agricultural Sciences2 Unitsintermediate

AGRICULTURAL STATISTICS AND DATA PROCESSING

This course introduces students to the fundamental concepts of agricultural statistics and data processing. It covers the meaning of statistics and biostatistics, frequency distribution, probability, hypothesis testing, correlation and regression, covariance, and Analysis of Variance (ANOVA). The course aims to equip students with the knowledge and skills to collect, manage, analyze, and interpret agricultural data effectively for informed decision-making in agricultural sciences.

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156h
Study Time
13
Weeks
12h
Per Week
intermediate
Math Level
Course Keywords
Agricultural StatisticsData ProcessingANOVACorrelationHypothesis Testing

Course Overview

Everything you need to know about this course

Course Difficulty

Intermediate Level
Builds on foundational knowledge
65%
intermediate
📊
Math Level
Moderate Math
🔬
Learning Type
Hands-on Practice

Course Topics

Key areas covered in this course

1

Descriptive Statistics

2

Probability Distributions

3

Hypothesis Testing

4

Analysis of Variance (ANOVA)

5

Correlation and Regression

6

Chi-Square Tests

Total Topics6 topics

Requirements

Knowledge and skills recommended for success

Basic Mathematics

Introductory Statistics

💡 Don't have all requirements? Don't worry! Many students successfully complete this course with basic preparation and dedication.

Assessment Methods

How your progress will be evaluated (3 methods)

Assignments

Comprehensive evaluation of course material understanding

Written Assessment

Tutor-Marked Assignments

Comprehensive evaluation of course material understanding

Written Assessment

Final Examination

Comprehensive evaluation of course material understanding

Written Assessment

Career Opportunities

Explore the career paths this course opens up for you

Agricultural Statistician

Apply your skills in this growing field

Data Analyst

Apply your skills in this growing field

Research Scientist

Apply your skills in this growing field

Agricultural Consultant

Apply your skills in this growing field

Farm Manager

Apply your skills in this growing field

Industry Applications

Real-world sectors where you can apply your knowledge

Agricultural Research InstitutesGovernment Agricultural DepartmentsPrivate Agricultural CompaniesFarming and AgribusinessConsulting Firms

Study Schedule Beta

A structured 13-week journey through the course content

Week
1

Module 1: Introduction

2h

Unit 1: Population and Sample

2 study hours
  • •Define population and sample.
  • •Differentiate between discrete and continuous variables.
  • •Discuss the importance of sampling in statistical analysis.
Week
2

Module 1: Introduction

2h

Unit 2: Frequency Distribution, Measures of Location and Measures of Variation

2 study hours
  • •Define frequency and frequency distribution.
  • •Organize data using frequency distributions.
  • •Represent data in methods other than frequency distribution.
Week
3

Module 1: Introduction

2h

Unit 3: Probability

2 study hours
  • •Define probability and its basic properties.
  • •Classify probability into classical, empirical, and subjective probabilities.
  • •Solve probability problems using different approaches.
Week
4

Module 1: Introduction

2h

Unit 4: Probability Distributions

2 study hours
  • •Define distribution and probability distribution.
  • •Compute probabilities in binomial probability distributions.
  • •Understand and apply the normal distribution.
Week
5

Module 1: Introduction

2h

Unit 5: Descriptive Statistics

2 study hours
  • •Define descriptive statistics.
  • •Discuss the concept of Univariate Analysis.
  • •Differentiate between descriptive statistics and inferential statistics.
Week
6

Module 2: Data Analysis Techniques

2h

Unit 1: Sampling, Data Collection and Data Processing Techniques

2 study hours
  • •Describe different methods of data collection.
  • •Learn how to design questionnaires.
  • •Understand different types of sampling techniques.
Week
7

Module 2: Data Analysis Techniques

2h

Unit 2: Inference and Hypothesis Testing; Type I and Type II Errors

2 study hours
  • •Define hypothesis testing and statistical inference.
  • •Formulate null and alternative hypotheses.
  • •Calculate test statistics for hypothesis testing.
Week
8

Module 2: Data Analysis Techniques

2h

Unit 3: Analysis of Variance

2 study hours
  • •Explain the underlying models to ANOVA.
  • •State the steps in performing a one-way ANOVA.
  • •Justify the result of one-way ANOVA.
Week
9

Module 2: Data Analysis Techniques

2h

Unit 4: Correlation and Regression Analysis

2 study hours
  • •Calculate the strength and direction of a relationship between two variables.
  • •Evaluate and interpret the product moment correlation coefficient.
  • •Find the equations of regression lines and use them where appropriate.
Week
10

Module 2: Data Analysis Techniques

2h

Unit 5: Analysis of Covariance

2 study hours
  • •Discuss the basic ideas behind ANCOVA.
  • •State when to use ANCOVA.
  • •State Null hypotheses for ANCOVA and explain how the test works.
Week
11

Module 3: Advanced Statistical Tests

2h

Unit 1: Hypothesis Testing of Attributes Data

2 study hours
  • •State the requirements for chi-square analysis.
  • •Explain how to calculate expected cell counts under the null distribution.
  • •Perform the Pearson and Likelihood Ratio Chi-Square tests.
Week
12

Module 3: Advanced Statistical Tests

2h

Unit 2: Goodness of Fit

2 study hours
  • •Formulate null and alternative hypotheses for goodness of fit analysis.
  • •Calculate expected frequencies for a variety of probability models.
  • •Use χ 2 distribution to test if a set of observations fits an appropriate probability model.
Week
13

Module 3: Advanced Statistical Tests

4h

Unit 3: Chi-Square Test for Independence

2 study hours
  • •Identify the type of data and arrange the data in matrix form.
  • •Formulate Null Hypothesis and its alternative for Test of Independence.
  • •Compute Expected Frequencies and Chi-Square Statistic for Test of Independence.

Unit 4: Field Experimentation, Collection and Processing of Data

2 study hours
  • •Discuss the essentials of experimentation.
  • •Explain the principles of field experimentation.
  • •Understand the importance of data collection and processing.

This study schedule is in beta and may not be accurate. Please use it as a guide and consult the course outline for the most accurate information.

Course PDF Material

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Study Tips & Exam Preparation

Expert tips to help you succeed in this course

1

Review all study units and focus on key concepts and formulas.

2

Practice solving problems related to hypothesis testing, ANOVA, and regression.

3

Create summary sheets of important statistical tests and their applications.

4

Work through all Tutor-Marked Assignments (TMAs) and self-assessment questions.

5

Allocate sufficient time for revision and practice before the examination.

6

Focus on understanding the assumptions and limitations of each statistical test.

7

Practice interpreting statistical results and drawing meaningful conclusions.

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