MODULE 1
Statistics - the science of collecting, organizing,
presenting, analyzing, and interpreting data to assist in
making more effective decisions.

Phases of Statistics:
1. Descriptive statistics - methods concerned with
organizing, presenting, summarizing, and
analyzing a set of data without drawing
conclusions or inferences about a population.
2. Inferential - methods concerned with the
analysis of sample data leading to predictions or
inferences about the population

Basic Vocab of Statistics:
 Population - collection, or set, of individuals or
objects or events whose properties are to be
analyzed
 Sample - A portion or part or subset of the
population of interest
 Parameter - a numerical characteristic of the
population
 Statistic  Variable - a characteristic of an item or individual
element in a population or sample
 Data - different values associated with a variable
population, the first unit being selected at
random.
Stratified random sampling - the population is
divided or stratified into more or less
homogenous subpopulations (stratum) before
sampling is done.
Cluster sampling - Population is divided into
several “clusters,” each representative of the
population
MODULE 2
Pie chart - a circle which is divided into sectors in such a
way that the area of each scope is proportional to the
size of the quantity represented by that sector.
Column chart - consists of a series of rectangular bars
where the length of the bar represents the magnitude to
be demonstrated
Bar chart - data labels are long or you have too many data
sets to display.
Scatter plot - gives a visual picture of the relationship
between the two variables, and aids the interpretation of
the correlation coefficient or regression model.
Types of data:
1. Qualitative (categorical) - data have values that
can only be placed into categories,
2. Quantitative Data (Numerical) - data that can be
expressed in numbers.
 Discrete – counted, whole number
 Continuous – decimal
Levels of measurements
1. Nominal scale – no implied ranking of categories
2. Ordinal scale - the categories of a variable can be
ranked
3. Interval scale - contains the property of identity,
order, and equality of scale but does not possess
the absolute zero property and multiples of
measures are not meaningful.
4. Ratio scale - contains the property of identity,
order, equality of scale and the absolute zero
property and multiples of measures are
meaningful
Sources of Data
1. Primary source - The data collector is the one
using the data for analysis
2. Secondary source - The person performing data
analysis is not the data collector
Sampling Techniques
1. Probability sampling - every element in the
population is given a chance of being included in
the population
 Simple random sampling
 Systematic sampling - a method of selecting a
sample by taking every kth unit from an ordered
Tabular presentation - process of condensing classified
data and arranging them systematically in rows and
columns
Common Types of Tables
1. Summary Table for Categorical data - a form of
frequency distribution table where observations
are classified based on categorical names
2. Single-value Grouping for Numerical Data - a
form of frequency distribution table where
distinct values are used as classes
Steps in Constructing an FDT
1. Determine an adequate number of intervals (K).
(usually between 5 to 20 class intervals)
Suggested Formulas: 𝐾 = √𝑛; or K = 1+ 3.322log
n, where n is the sample size
2. Determine the range (R). R = highest-lowest
3. Compute the class width (c). c = R/K
Round off c to a value that is easy to work with.
(Suggested Rule: c must have the same number
of decimal places as the original data)
4. List the class intervals
MODULE 5
Level of significance
- the maximum probability with which we are
willing to commit a Type I error.
- 0.05 (significant), 0.01 (highly significant)
Test statistic
- a statistic computed from the sample on which
the decision to reject or not to reject H0 is based
- if the computed test statistic falls in the
rejection region, the H0 is rejected
Critical or Rejection Region
- a part of the set of all possible values of a test
statistic for which H0,
- the size of the critical region is determined by the
alpha level. is rejected
- Sample data that fall in the critical region will
warrant the rejection of the null hypothesis.
Critical Value
- boundary between the rejection region and the
nonrejection region
- a value from a z- or t-table
Example:
Suppose the null hypothesis, Ho, is: Frank’s rock climbing
equipment is safe.
Type 1 error: Frank concludes that his rock climbing
equipment may not be safe when, in fact, it really is safe.
Type 2 error: Frank concludes that his rock climbing
equipment is safe when, in fact, it is not safe.