Matrix of Curriculum Standards (Competencies),
with Corresponding Recommended Flexible Learning Delivery Mode and Materials per Grading Period
SENIOR HIGH SCHOOL CORE SUBJECT
STATISTICS AND PROBABILITY
Week of
the
Quarter/
Grading
Period
Learning competencies
(SHS Core – Statistics and Probability)
Week 1/ illustrates a random variable (discrete and continuous).
3rd Q
distinguishes between a discrete and a continuous random
variable.
finds the possible values of a random variable.
illustrates a probability distribution for a discrete random
variable and its properties.
computes probabilities corresponding to a given random
variable.
Week 2/ illustrates the mean and variance of a discrete random variable.
3rd Q
calculates the mean and the variance of a discrete random
variable.
interprets the mean and the variance of a discrete random
variable.
solves problems involving mean and variance of probability
distributions.
Week 3/ illustrates a normal random variable and its characteristics.
3rd Q
identifies regions under the normal curve corresponding to
different standard normal values.
Lesson
Exemplar/
Learning
resources
available
LR
develope
r
Link (if
available
online)
Assessment
(provide a
link if online)
Week of
the
Quarter/
Grading
Period
Week 4/
3rd Q
Week 5/
3rd Q
Week 5/
3rd Q
Week 6/
3rd Q
Week 7/
3rd Q
Learning competencies
(SHS Core – Statistics and Probability)
converts a normal random variable to a standard normal variable
and vice versa.
computes probabilities and percentiles using the standard
normal table.
illustrates random sampling.
distinguishes between parameter and statistic.
identifies sampling distributions of statistics (sample mean).
finds the mean and variance of the sampling distribution of the
sample mean.
defines the sampling distribution of the sample mean for normal
population when the variance is: (a) known; (b) unknown
illustrates the Central Limit Theorem.
defines the sampling distribution of the sample mean using the
Central Limit Theorem.
solves problems involving sampling distributions of the sample
mean.
illustrates point and interval estimations.
distinguishes between point and interval estimation.
computes for the point estimate of the population mean.
identifies the appropriate form of the confidence interval
estimator for the population mean when: (a) the population
variance is known, (b) the population variance is unknown, and
(c) the Central Limit Theorem is to be used.
illustrates the t-distribution.
identifies percentiles using the t-table.
Lesson
Exemplar/
Learning
resources
available
LR
develope
r
Link (if
available
online)
Assessment
(provide a
link if online)
Week of
the
Quarter/
Grading
Period
Learning competencies
(SHS Core – Statistics and Probability)
Lesson
Exemplar/
Learning
resources
available
LR
develope
r
Link (if
available
online)
Assessment
(provide a
link if online)
Lesson
Exemplar/
Learning
resources
available
LR
develope
r
Link (if
available
online)
Assessment
(provide a
link if online)
Week 8/ computes for the confidence interval estimate based on the
3rd Q
appropriate form of the estimator for the population mean.
solves problems involving confidence interval estimation of the
population mean.
draws conclusion about the population mean based on its
confidence interval estimate.
Week 9/ computes for the point estimate of the population proportion.
3rd Q
identifies the appropriate form of the confidence interval
estimator for the population proportion based on the Central
Limit Theorem.
computes for the confidence interval estimate of the population
proportion.
solves problems involving confidence interval estimation of the
population proportion.
draws conclusion about the population proportion based on its
confidence interval estimate
Week 10 identifies the length of a confidence interval.
/ 3rd Q computes for the length of the confidence interval.
Week of
the
Quarter/
Grading
Period
Learning competencies
(SHS Core – Statistics and Probability)
Week 10 computes for an appropriate sample size using the length of the
Week of
the
Quarter/
Grading
Period
Learning competencies
(SHS Core – Statistics and Probability)
/ 3rd Q
interval.
solves problems involving sample size determination.
illustrates: (a) null hypothesis; (b) alternative hypothesis; (c) level
of significance; (d) rejection region; and (e) types of errors in
hypothesis testing.
calculates the probabilities of committing a Type I and Type II
error.
identifies the parameter to be tested given a real-life problem.
formulates the appropriate null and alternative hypotheses on a
population mean.
identifies the appropriate form of the test-statistic when: (a) the
population variance is assumed to be known; (b) the population
variance is assumed to be unknown; and (c) the Central Limit
Theorem is to be used.
identifies the appropriate rejection region for a given level of
significance when: (a) the population variance is assumed to be
known; (b) the population variance is assumed to be unknown;
and (c) the Central Limit Theorem is to be used.
computes for the test-statistic value (population mean).
draws conclusion about the population mean based on the teststatistic value and the rejection region.
solves problems involving test of hypothesis on the population
mean.
formulates the appropriate null and alternative hypotheses on a
population proportion.
Week
1 / 4th Q
Week
2 / 4th Q
Week
3 / 4th Q
Week
4 / 4th Q
Week
5 / 4th Q
Lesson
Exemplar/
Learning
resources
available
LR
develope
r
Link (if
available
online)
Assessment
(provide a
link if online)
Week of
the
Quarter/
Grading
Period
Learning competencies
(SHS Core – Statistics and Probability)
identifies the appropriate form of the test-statistic when the
Central Limit Theorem is to be used.
identifies the appropriate rejection region for a given level of
significance when the Central Limit Theorem is to be used.
Week
computes for the test-statistic value (population proportion).
th
6 / 4 Q draws conclusion about the population proportion based on the
test-statistic value and the rejection region.
Week 6- solves problems involving test of hypothesis on the population
7 / 4th Q proportion.
Week
illustrates the nature of bivariate data.
7 / 4th Q constructs a scatter plot.
describes shape (form), trend (direction), and variation (strength)
based on a scatter plot.
Week
calculates the Pearson’s sample correlation coefficient.
8 / 4th Q solves problems involving correlation analysis.
Week
identifies the independent and dependent variables.
th
9 / 4 Q draws the best-fit line on a scatter plot.
calculates the slope and y-intercept of the regression line.
interprets the calculated slope and y-intercept of the regression
line.
Week 10 predicts the value of the dependent variable given the value of
/ 4th Q the independent variable.
solves problems involving regression analysis.
Lesson
Exemplar/
Learning
resources
available
LR
develope
r
Link (if
available
online)
Assessment
(provide a
link if online)