# K to 12 Senior High School Core Curriculum – Statistics and Probability Curriculum Guide

Curriculum Guide  |  PDF

Published on 2018 September 25th

Description
This material is a curriculum guide of K to 12 Senior High School Core Curriculum – Statistics and Probability for Grade 11/12
Objective

#### Curriculum Information

 Education Type K to 12 Grade Level Grade 11, Grade 12 Learning Area Content/Topic Random Variables and Probability Distributions Normal Distribution Sampling and Sampling Distributions Estimation of Parameters Tests of Hypothesis Correlation and Regression Analyses Intended Users Educators Competencies 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.