DAILY LESSON LOG OF M11/12SP-IVh-2 (Week Eight-Day 2)
School Grade Level Grade 11
Teacher Learning Area Statistics & Probability
Teaching Date and Time Quarter Second
Objectives must be met over the week and connected to the curriculum standards. To meet the
objectives, necessary procedures must be followed and if needed, additional lessons, exercises and
remedial activities may be done for developing content knowledge and competencies. These are
I. OBJECTIVES assessed using Formative Assessment Strategies. Valuing objectives support the learning of
content and competencies and enable children to find significance and joy in learning the lessons.
Weekly objectives shall be derived from the curriculum guides.
A. Content Standards The learner demonstrates understanding of key concepts of correlation and
regression analysis.
B. Performance Standards The learner is able to perform correlation and regression analysis on real life
problems in different disciplines.
Learning Competency: The learner will be able to calculate the Pearson’s
sample correlation coefficient. (M11/12SP-IVh-2 )
C. Learning Competencies/ Learning Objectives:
Objectives 1. Identify the formula in calculating pearson’s sample correlation coefficient.
2. Calculate pearson’s sample correlation coefficient.
3. Integrate patience in calculating pearson’s sample correlation coefficient.
II.CONTENT Correlation and Regression Analysis
III.
LEARNING RESOURCES teacher’s guide, learner’s module, internet
A. References
1. Teacher’s Guide Pp. 404-406
2. Learner’s Materials
3. Textbook pages
4. Additional Materials
from Learning Resource
(LR) portal
B. Other Learning Resources Laptop, projector
These steps should be done across the week. Spread out the activities appropriately so that
pupils/students will learn well. Always be guided by demonstration of learning by the pupils/
students which you can infer from formative assessment activities. Sustain learning systematically
IV. PROCEDURES by providing pupils/students with multiple ways to learn new things, practice the learning,
question their learning processes, and draw conclusions about what they learned in relation to
their life experiences and previous knowledge. Indicate the time allotment for each step.
The teacher will ask the students, “How do you estimate strength association
between the variables based on a scatter plot?”
Possible response: A correlation of r = 0.9 suggests a strong, positive
association between two variables, whereas a correlation of r = -0.2 suggest a
weak, negative association. A correlation close to zero suggests no linear
association between two continuous variables.
A. Review previous lesson
or presenting the new
lesson
The teacher will ask the following questions to students:
1. Why do we need to calculate the Pearson’s sample correlation coefficient?
Possible answer: We estimate a sample correlation coefficient, more
specifically the Pearson Product Moment correlation coefficient. The sample
correlation coefficient, denoted r, ranges between -1 and +1 and quantifies the
B. Establishing a purpose direction and strength of the linear association between the two variables. The
for the lesson correlation between two variables can be positive (i.e., higher levels of one
variable are associated with higher levels of the other) or negative (i.e., higher
levels of one variable are associated with lower levels of the other).
The sign of the correlation coefficient indicates the direction of the
association. The magnitude of the correlation coefficient indicates the strength
of the association.
C. Presenting examples/ The teacher will present example from yesterday’s lesson and its solve the
instances of the new pearson’s correlation coefficient with the students.
lesson Example:
(Refer to the example yesterday: Correlation of Gestational Age and Birth
Weight)
To compute the sample correlation coefficient, we need to compute the
variance of gestational age, the variance of birth weight, and also the
covariance of gestational age and birth weight.
To compute the variance of gestational age, we need to sum the squared
deviations (or differences) between each observed gestational age and the
mean gestational age. The computations are summarized below.
The variance of birth weight is computed just as we did for gestational age as
shown in the table below.
To compute the covariance of gestational age and birth weight, we need to
multiply the deviation from the mean gestational age by the deviation from
the mean birth weight for each participant, that is:
D. Discussing new concepts The teacher will present the process in solving the Pearson’s Correlation
and practicing new skills
Coefficient.
Steps in solving the Pearson’s Correlation Coefficient:
1. Get the mean of each variable.
2. Find the variance of each.
3. Compute the covariance.
4. Solve for the correlation coefficient.
Or use this formula,
#1
In correlation analysis, we estimate a sample correlation coefficient, more
specifically the Pearson Product Moment correlation coefficient. The sample
correlation coefficient, denoted r, ranges between -1 and +1 and quantifies the
direction and strength of the linear association between the two variables. The
correlation between two variables can be positive (i.e., higher levels of one
variable are associated with higher levels of the other) or negative (i.e., higher
levels of one variable are associated with lower levels of the other).
The sign of the correlation coefficient indicates the direction of the
association. The magnitude of the correlation coefficient indicates the strength
of the association.
For example, a correlation of r = 0.9 suggests a strong, positive association
between two variables, whereas a correlation of r = -0.2 suggest a weak,
negative association. A correlation close to zero suggests no linear association
between two continuous variables.
E. Discussing new concepts
and practicing new skills
#2
The teacher will ask the students to work in pairs and find the correlation coefficient of
their formative exam yesterday.
F. Developing mastery
(leads to formative
assessment 3)
Possible answer:
r=0.827920353
The sample correlation coefficient indicates a strong positive correlation.
The teacher allows the students to give examples and do the activity such as
G. Finding practical arm span and height of a person, daily allowance and weight of a person, etc.
applications of concepts Ask them to tabulate their data and calculate the correlation coefficient using
and skills in daily living Pearson’s sample.
The teacher will have a closure about the topic by giving the students things to
remember.
Bear in mind:
H. Making generalizations The sample correlation coefficient, denoted r, ranges between -1 and +1 and
and abstractions about quantifies the direction and strength of the linear association between the two
the lesson variables. The correlation between two variables can be positive (i.e., higher
levels of one variable are associated with higher levels of the other) or
negative (i.e., higher levels of one variable are associated with lower levels of
the other).
I. Evaluating Learning The teacher lets the students work individually.
Solve.
Possible answer:
J. Additional activities or
remediation
V. REMARKS
Reflect on your teaching and assess yourself as a teacher. Think about your students’ progress.
What works? What else needs to be done to help the pupils/students learn? Identify what help
VI. REFLECTION your instructional supervisors can provide for you so when you meet them, you can ask them
relevant questions.
A. No. of learners who earned 80%
of the evaluation
B. No. of learners who require
additional activities for
remediation who scored below
80%
C. Did the remedial lesson work?
No. of learners who have caught
up with the lesson.
D. No. of learners who continue to
require remediation
E. Which of my teaching strategies
worked well? Why did these
work?
F. What difficulties did I encounter
which my principal or supervisor
can help me solve?
G. What innovation or localized
materials did I use/ discover
which I wish to share with other
teachers
