 # Specific at the end of the course students should be

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Specific:At the end of the course, students should be able to:1.Acquire understanding on the nature of statistics and how it playsan important role in our daily lives.2.Recognize and use the language of probability.
3.Comprehendthemethodforestimatingsomepopulationparameters.4.Study the sampling distribution of some useful statistics.5.Distinguish and apply the basic concepts of statistical inference tothe decision making process.6.Find the least squares regression line.7.Use the statistical software SPSS.COURSE CONTENTThis course contains Module I with four lessons. It is structured as follows:Module I.Random Variables and Probability DistributionsLesson 1.Random VariablesLesson 2.ProbabilityDistributionofDiscreteRandomVariable.Lesson 3.Mean of Discrete Probability DistributionLesson 4.Variance of Discrete Probability DistributionModule II.Normal and Sampling DistributionsLesson 1.Normal Distribution and Standard Normal VariablesLesson 2.Applying the Normal Curve Concepts in ProblemSolvingLesson 3.Random SamplingLesson 4.Sampling Distribution of the MeansModule III.Test of HypothesisLesson 1.Introduction to Hypothesis TestingLesson 2.Test of Hypothesis Using the z-testLesson 3.Test of Hypothesis Using the t-testLesson 4.Confidence Level and Sample SizeModule IV.Correlation and RegressionLesson 1.CorrelationLesson 2.Linear Regression
MODULE 1Random Variables and Probability DistributionsLesson 1.Random VariablesLesson 2.Probability Distribution of DiscreteRandom Variable.Lesson 3.Mean of Discrete ProbabilityDistribution.Lesson 4.Variance of Discrete ProbabilityDistribution.
MODULE 1Random Variables and Probability DistributionsIntroductionIf you are interested in determining the certainty or uncertainty of the occurrenceof an event such as the cases of experiment or the possibility of rain fall today, you needsome knowledge in probability theory. However, there are times when you perform anexperiment and your interest lies in some numbers associated with the outcome and notin the outcome itself. For example, in throwing two dice, you wish to get the sum of sixas expected outcome but you’re not interested in the particular numbers required toachieve it. In cases like these, there is a need for mathematical models that describe thebehavior of the outcomes related to these statistical experiments. These theoreticalmodels are calledprobability distributions.The expected outcomes in experiments, as well as the certainty and uncertainty ofthe occurrence of an event, can be determined through probability theory.OBJECTIVESAfter studying the module, you should be able to:°Distinguish between discrete and continuous random variables.

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Term
Spring
Professor
NoProfessor
Tags
Probability distribution, Probability theory, probability density function
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