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Competencies
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Illustrates a random variable (discrete and continuous).
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.
Constructs the probability mass function of a discrete
random variable and its corresponding histogram.
Computes probabilities corresponding to a given
random variable.
Illustrates the mean and variance of a discrete random
variable.
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.
Illustrates a normal random variable and its
characteristics.
Constructs a normal curve.
Identifies regions under the normal curve
corresponding to different standard normal values.
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.
Identifies point estimator for the population mean.
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.
Constructs a t-distribution.
Identifies regions under the t-distribution corresponding
to different t-values.
Identifies percentiles using the t-table.
Computes for the confidence interval estimate based on
the 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.
Identifies point estimator for the population proportion.
Computes for the point estimate of the population
proportion.
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
Identifies the length of a confidence interval.
Computes for the length of the confidence interval.
Computes for an appropriate sample size using the
length of the 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 test-statistic 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.
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.
Computes for the test-statistic value (population
proportion).
Draws conclusion about the population proportion
based on the test-statistic value and the rejection
region.
Solves problems involving test of hypothesis on the
population proportion.
Illustrates the nature of bivariate data.
Constructs a scatter plot.
Describes shape (form), trend (direction), and variation
(strength) based on a scatter plot.
Estimates strength of association between the variables
based on a scatter plot.
Calculates the pearson’s sample correlation coefficient.
Solves problems involving correlation analysis.
Identifies the independent and dependent variables.
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.
Predicts the value of the dependent variable given the
value of the independent variable.
Solves problems involving regression analysis.
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