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- 1.1 Illustrates a random variable (discrete and continuous)
1.2 Distinguishes between a discrete and a continuous random variable - 1.3 Find the possible values of a random variable
1.4 Illustrates a probability distribution for a discrete random variable and its properties - 1.5 Construct the probability mass function of a discrete random variable and its corresponding histogram
- 1.6 Computes probabilities corresponding to a given variable
- 1.7 Illustrates the mean and variance of a discrete random variable
1.8 Calculates the mean and variance of a discrete random variable
1.9 Interprets the mean and the variance of a discrete random variable - 1.10 Solves problems involving mean and variance of probability distribution
- 2.1 Illustrates a normal random variable and its characteristics
2.2 constructs a normal curve. - 2.3 Identifies regions under the normal curve corresponding to different standard normal values
2.4 converts a normal random variable to a standard normal variable and vice versa. - 2.5 Computes probabilities and percentiles using the standard normal table.
- 3.1 Illustrates random sampling
3.2 Distinguish between parameter and statistic - 3.3 Identifies sampling distributions of statistics ( sample mean )
3.4 Finds the mean and variance of the sampling distribution of the sample mean - 3.5 Defines the sampling distribution of the sample mean for normal population when the variance is a.) known b.) unknown
- 3.6 Illustrates the Central Limit Theorem
3.7 defines the sampling distribution of the sample mean using the Central Limit Theorem - 3.8 Solves problems involving sampling distributions of the sample mean
- 4.1 Illustrates point and interval estimation
4.2 Distinguish between point and interval estimation - 4.3 Identifies estimator for the population mean
4.4 Computes for the point estimate of the population mean. - 4.5 The learner 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 is to be used
- 4.6 Illustrates t-distribution
4.7 Constructs a t-distribution
4.8 Identifies region under the t-distribution corresponding to different t-values. - 4.9 Identifies percentiles using the t-table
4.10 Computes for the confidence interval estimate based on the appropriate form of the estimator for the population mean . - 4.11 Solves problems involving confidence interval estimation for the population mean.
4.12 Draws conclusion about the population mean based on its confidence interval estimate. - 4.13 Identifies point estimator for population proportion
4.14 Computes for the point estimate of the population proportion - 4.15 Identifies the appropriate form of the confidence interval estimator for the population proportion based on the Central Limit Theorem
4.16 Computes for the confidence interval estimate of the population proportion
4.17 Solves problems involving confidence interval estimation of the population proportion
4.18 Draws conclusion about the population proportion based on its confidence interval estimate - 4.19 Identifies the length of a confidence interval
4.20 Computes for the length of the confidence interval
4.21 Computes for an appropriate sample size using the length of the interval
4.22 solves problems involving sample size determination. - 5.1 Illustrates:(a) null hypothesis, (b) alternative hypothesis, (c) level of significance, (d) rejection region; and (e) types of errors in hypothesis testing
5.2 Calculates the probabilities of committing a Type I and Type II error. - 5.3 Identifies the parameter to be tested given a real-life problem
5.4 Formulates the appropriate null and alternative hypothesis on a population mean. - 5.5 The learner 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.
5.6 The learner 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. - 5.7 The learner computes for the test-statistic value (population mean)
5.8 The learner draws conclusion about the population mean based on the test-statistic value and the rejection region.
5.9 The learner solves problems involving test of hypothesis on the population mean. - 5.10 The learner formulates the appropriate null and alternative hypotheses on a population proportion
5.11 Identifies the appropriate form of test-statistic when the central limit theorem is to be used - 5.12 Identifies the appropriate refection region for a given level of significance when the central limit theorem is to be used
5.13 Computes for the Test – Statistic Value (Population Proportion) - 5.14 Draws conclusion about the population proportion based on the test-statistic value and the rejection region
5.15 Solves problems involving test of hypothesis on the population proportion - 6.1 Illustrates the nature of bivariate data
6.2 Construct a scatter plot - 6.3 Describe shape, trend, and variation based on a scatter plot
6.4 Estimates strength of association between the variables based on a scatter plot
6.5 Calculates the Pearson’s sample correlation coefficient - 6.6 Solve Problems involving correlation analysis Correlation coefficient
- 6.7 Identifies the independent and dependent variables
6.11 Predicts the value of the dependent variable given the value of the independent variable. - 6.8 Draws the best-fit line on a scatter plot/
6.9 Calculates the slope and y-intercept of the regression line
6.10 Interprets the calculated slope and y-intercept of the regression line - 6.12 Solves problems involving regression analysis