review

1. Common Statistical
Tools

2. DESCRIPTIVE STATISTICAL
involve summarizing and presenting data in a meaningful
way. This includes measures such as mean, median,
mode, range, and standard deviation, providing insights
into the central tendency and variability of a dataset.
Descriptive statistics help researchers organize and
simplify complex information for better understanding
and interpretation.

3. Mean- Represents the average value in a
dataset.
Median-Identifies the middle value in a sorted
dataset.
Mode-Identifies the most frequently occurring
value in a dataset.
Standard Deviation –Quantifies the amount of
variation or dispersion in a dataset.

4. INFERENTIAL STATISTICS
Inferential statistics is a statistics that involves making inferences or
predictions about a population based on a sample. It allows us to
draw conclusions and make generalizations about a larger group or
population based on the information collected from a smaller subset
or sample. Inferential statistics uses probability theory to estimate
population parameters and test hypotheses.

5. T-test - A t-test is a statistical method used
to compare the means of two groups and determine
if there is a significant difference between
them. It assesses whether the observed
differences are likely due to chance or if they
represent a true effect.
ANOVA, or Analysis of Variance, statistical technique
used to compare means among multiple groups. It
assesses whether there are any statistically
significant differences between the means of three or
more independent groups. ANOVA breaks down the total
variability in the data into different sources, such
as within-group variability and between-group
variability.

6. Chi-square test- Is a statistical method used to determine if
there is a significant association between two categorical
variables. It compares the observed frequencies of data in a
contingency table with the frequencies that would be expected
if the variables were independent. The purpose is to assess
whether the observed distribution of data differs from what
would be expected by chance, helping researchers identify
relationships or patterns in categorical data.

7. REGRESSION ANALYSIS
Regression analysis -Is used to examine the relationship
between one dependent variable and one or more
independent variables. Its purpose is to model and
understand the nature of this relationship, allowing
predictions or explanations of the dependent variable based
on the values of the independent variables. In essence, it
helps analyze and quantify the impact of various factors on
a particular outcome, making it a valuable tool in fields such
as statistics, economics, and various scientific disciplines.

8. CORRELATION ANALYSIS
Correlation analysis is a statistical method used to evaluate
the strength and direction of the relationship between two
variables. It measures the extent to which changes in one
variable are associated with changes in another. The
correlation coefficient, ranging from -1 to 1, indicates the
strength and direction of the correlation: -1 for a perfect
negative correlation, 1 for a perfect positive correlation, and 0
for no correlation.

9. Correlation analysis- Is a statistical method used to evaluate the strength
and direction of the relationship between two variables. It measures the
extent to which changes in one variable are associated with changes in
another. The correlation coefficient, ranging from -1 to 1, indicates the
strength and direction of the correlation: -1 for a perfect negative
correlation, 1 for a perfect positive correlation, and 0 for no correlation.
Correlation analysis - Is a statistical method used to evaluate the strength
and direction of the relationship between two variables. It measures the
extent to which changes in one variable are associated with changes in
another. The correlation coefficient, ranging from -1 to 1, indicates the
strength and direction of the correlation: -1 for a perfect negative
correlation, 1 for a perfect positive correlation, and 0 for no correlation.

10. PROBABILITY DISTRIBUTION
A probability distribution describes the likelihood of different
outcomes in a set of possible events. It assigns probabilities to
each possible outcome, indicating the likelihood of that outcome
occurring. The purpose of probability distributions is to model
and analyze uncertainty in various fields, such as statistics,
mathematics, and science. They help in making predictions,
understanding random phenomena, and making informed
decisions based on uncertainty.

11. SAMPLING TECHNIQUES
Sampling techniques - Are methods used to select a subset of
individuals or items from a larger population for the purpose of
research or statistical analysis. The goal is to gather
information from the sample and make inferences about the
entire population. Common sampling techniques include
random sampling, stratified sampling, systematic sampling, and
cluster sampling. Each method has its own advantages and
limitations, and the choice of technique depends on the research
objectives and characteristics of the population being studied.