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AGRICULTURAL
STATISTICS
REVIEW ON THE BASIC
CONCEPTS OF
STATISTICS
STATISTICS
Statistics is an art and science that deals with
the collection, organization, creative presentation,
analysis and the interpretation of data. Statistics is
used in different fields; hereby, in the field of science
and technology it is used in the analyses of the
causes and effects of the different variables affecting
experiments. It is widely used in agriculture
especially in the conduct of researches. It allows
researchers to assess the error associated with
conducting an experiment and to separate real
treatment differences from differences caused by
uncontrollable environmental factors.
STATISTICS
Data is not generally associated with
mathematics. However, data is the base of all
operations in statistics. Statistics are the results of
data analysis - its interpretation and presentation. In
this chapter, the basic concepts of statistics,
collection of data, presentation of data and
summation notation will be further discussed to
provide understanding for data and statistics.
Definitions
Statistics is a collection of methods for planning studies and experiments, obtaining
data, and then organizing, summarizing, presenting, analyzing, interpreting, and
drawing conclusions based on the data.
Data are observations (such as measurements, genders, survey responses) that have
been collected.
Constant refers to fundamental quantities that do not change in value
Variables are quantities that may take anyone of a specified set of values
Definitions
A population is the complete collection of all elements (scores, people,
measurements, and so on) to be studied. The collection is complete in
the sense that it includes all subjects to be studied.
A census is the collection of data from every member of the population.
A sample is a subcollection of members selected from a population.
Descriptive VersusInferential Statistics
The field of statistics is divided into two major divisions: Descriptive and
Inferential Statistics.
1. Descriptive statistics – concerned with the methods of collecting,
organizing, presenting, computing and interpreting data appropriately and
creatively to describe or assess group characteristics.
- Statistics that summarize data to make sense or meaning of a list of numeric
values
Some statistical tools:
- measures of central tendency
- measures of position or location
- measures of variability
Descriptive VersusInferential Statistics
2. Inferential statistics – are procedures that allow researchers to infer
or generalize observations made with samples to the larger population
from which they were selected.
Some statistical tools:
- confidence interval
- hypothesis testing (t-test, F-test or ANOVA)
- correlation and regression
PARAMETERVS STATISTIC
A parameter is a numerical measurement describing some characteristic
of a population.
Example:
- 50% of 350 students at Tagbina Elementary School got below a 3
score on a standardized test. You know this because you have each
and every student’s test score.
PARAMETERVS STATISTIC
A statistic is a numerical measurement describing some characteristic of
a sample.
Example:
- 70% of Tagbina residents agree with the latest health care proposal.
It’s not possible to actually ask thousands of people whether they
agree. Researchers have to just take samples and calculate the rest.
Classificationof Data
Data analysis is broad, exploratory, and downright complex. But when
we take a step back and attempt to simplify data analysis, we can quickly
see it boils down to two things: qualitative and quantitative data.
These two types of data are quite different, yet they make up all of the
data that will ever be analyzed.
Before diving into data analytics, it’s important to understand the key
differences between qualitative and quantitative data.
Classificationof Data
1. Quantitative data consist of numbers representing counts or
measurements.
Example: The weights of vegetables
2. Qualitative (or categorical or attribute) data can be separated into
different categories that are distinguished by some nonnumeric
characteristic.
Example: The color of the different varieties of banana
Classificationof Variables
According to functional relationships:
Variables are given a special name that only applies to experimental
investigations. One is called the dependent variable and the other the
independent variable.
1. Dependent variable is a variable that is observed and measured to
determine the effect of another variable
- A variable that is believed to change or is affected by the other
variable
- Is the “presumed effect
Classificationof Variables
2. Independent variable is a variable that is manipulated in an
experiment
- A variable that affects the other variable
- Variable remains unchanged between conditions being observed in an
experiment
- Is the “presumed cause”
Classificationof Variables
According to continuity of values:
1. Discrete Variable- Variable that cannot assume any value between
two given values
Values- Countable
Range of specified number- Complete or whole
Example: Number of students in a class
Classificationof Variables
2. Continuous Variable- Variable that can theoretically
assume any value between two given values
Values- Measurable
Range of specified number- Incomplete
Example: Height of a person
Level of Measurements
The way a set of data is measured is called its level of
measurement. Correct statistical procedures depend on a researcher
being familiar with levels of measurement. Not every statistical
operation can be used with every set of data. Don’t do computations
and don’t use statistical methods that are not appropriate for the
data.
Level of Measurements
Nominal- is characterized by data that consist of names,
labels, or categories only. The data cannot be arranged in an
ordering scheme (such as low to high).
Example: Yes/no/undecided: Survey responses of yes, no,
and undecided
Level of Measurements
Ordinal- Data are at the ordinal level of measurement if they
can be arranged in some order, but differences between data
values either cannot be determined or are meaningless.
Level of Measurements
Interval- is like the ordinal level, with the additional property
that the difference between any two data values is meaningful.
However, data at this level do not have a natural zero starting
point (where none of the quantity is present).
Level of Measurements
Ratio- is the interval level with the additional property that
there is also a natural zero starting point (where zero indicates
that none of the quantity is present). For values at this level,
differences and ratios are both meaningful.
END

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AGRICULTURAL-STATISTICS.pptx

  • 1. AGRICULTURAL STATISTICS REVIEW ON THE BASIC CONCEPTS OF STATISTICS
  • 2. STATISTICS Statistics is an art and science that deals with the collection, organization, creative presentation, analysis and the interpretation of data. Statistics is used in different fields; hereby, in the field of science and technology it is used in the analyses of the causes and effects of the different variables affecting experiments. It is widely used in agriculture especially in the conduct of researches. It allows researchers to assess the error associated with conducting an experiment and to separate real treatment differences from differences caused by uncontrollable environmental factors.
  • 3. STATISTICS Data is not generally associated with mathematics. However, data is the base of all operations in statistics. Statistics are the results of data analysis - its interpretation and presentation. In this chapter, the basic concepts of statistics, collection of data, presentation of data and summation notation will be further discussed to provide understanding for data and statistics.
  • 4. Definitions Statistics is a collection of methods for planning studies and experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data. Data are observations (such as measurements, genders, survey responses) that have been collected. Constant refers to fundamental quantities that do not change in value Variables are quantities that may take anyone of a specified set of values
  • 5. Definitions A population is the complete collection of all elements (scores, people, measurements, and so on) to be studied. The collection is complete in the sense that it includes all subjects to be studied. A census is the collection of data from every member of the population. A sample is a subcollection of members selected from a population.
  • 6. Descriptive VersusInferential Statistics The field of statistics is divided into two major divisions: Descriptive and Inferential Statistics. 1. Descriptive statistics – concerned with the methods of collecting, organizing, presenting, computing and interpreting data appropriately and creatively to describe or assess group characteristics. - Statistics that summarize data to make sense or meaning of a list of numeric values Some statistical tools: - measures of central tendency - measures of position or location - measures of variability
  • 7. Descriptive VersusInferential Statistics 2. Inferential statistics – are procedures that allow researchers to infer or generalize observations made with samples to the larger population from which they were selected. Some statistical tools: - confidence interval - hypothesis testing (t-test, F-test or ANOVA) - correlation and regression
  • 8. PARAMETERVS STATISTIC A parameter is a numerical measurement describing some characteristic of a population. Example: - 50% of 350 students at Tagbina Elementary School got below a 3 score on a standardized test. You know this because you have each and every student’s test score.
  • 9. PARAMETERVS STATISTIC A statistic is a numerical measurement describing some characteristic of a sample. Example: - 70% of Tagbina residents agree with the latest health care proposal. It’s not possible to actually ask thousands of people whether they agree. Researchers have to just take samples and calculate the rest.
  • 10. Classificationof Data Data analysis is broad, exploratory, and downright complex. But when we take a step back and attempt to simplify data analysis, we can quickly see it boils down to two things: qualitative and quantitative data. These two types of data are quite different, yet they make up all of the data that will ever be analyzed. Before diving into data analytics, it’s important to understand the key differences between qualitative and quantitative data.
  • 11. Classificationof Data 1. Quantitative data consist of numbers representing counts or measurements. Example: The weights of vegetables 2. Qualitative (or categorical or attribute) data can be separated into different categories that are distinguished by some nonnumeric characteristic. Example: The color of the different varieties of banana
  • 12. Classificationof Variables According to functional relationships: Variables are given a special name that only applies to experimental investigations. One is called the dependent variable and the other the independent variable. 1. Dependent variable is a variable that is observed and measured to determine the effect of another variable - A variable that is believed to change or is affected by the other variable - Is the “presumed effect
  • 13. Classificationof Variables 2. Independent variable is a variable that is manipulated in an experiment - A variable that affects the other variable - Variable remains unchanged between conditions being observed in an experiment - Is the “presumed cause”
  • 14. Classificationof Variables According to continuity of values: 1. Discrete Variable- Variable that cannot assume any value between two given values Values- Countable Range of specified number- Complete or whole Example: Number of students in a class
  • 15. Classificationof Variables 2. Continuous Variable- Variable that can theoretically assume any value between two given values Values- Measurable Range of specified number- Incomplete Example: Height of a person
  • 16. Level of Measurements The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. Not every statistical operation can be used with every set of data. Don’t do computations and don’t use statistical methods that are not appropriate for the data.
  • 17. Level of Measurements Nominal- is characterized by data that consist of names, labels, or categories only. The data cannot be arranged in an ordering scheme (such as low to high). Example: Yes/no/undecided: Survey responses of yes, no, and undecided
  • 18. Level of Measurements Ordinal- Data are at the ordinal level of measurement if they can be arranged in some order, but differences between data values either cannot be determined or are meaningless.
  • 19. Level of Measurements Interval- is like the ordinal level, with the additional property that the difference between any two data values is meaningful. However, data at this level do not have a natural zero starting point (where none of the quantity is present).
  • 20. Level of Measurements Ratio- is the interval level with the additional property that there is also a natural zero starting point (where zero indicates that none of the quantity is present). For values at this level, differences and ratios are both meaningful.
  • 21. END