Specific at the end of the course students should be

Specific :
At the end of the course, students should be able to:
1. Acquire understanding on the nature of statistics and how it plays
an important role in our daily lives.
2. Recognize and use the language of probability.

3. Comprehend the method for estimating some population
parameters.
4. Study the sampling distribution of some useful statistics.
5. Distinguish and apply the basic concepts of statistical inference to
the decision making process.
6. Find the least squares regression line.
7. Use the statistical software SPSS.
COURSE CONTENT
This course contains Module I with four lessons. It is structured as follows:
Module I. Random Variables and Probability Distributions
Lesson 1. Random Variables
Lesson 2. Probability Distribution of Discrete Random
Variable.
Lesson 3. Mean of Discrete Probability Distribution
Lesson 4. Variance of Discrete Probability Distribution
Module II. Normal and Sampling Distributions
Lesson 1. Normal Distribution and Standard Normal Variables
Lesson 2. Applying the Normal Curve Concepts in Problem
Solving
Lesson 3. Random Sampling
Lesson 4. Sampling Distribution of the Means
Module III. Test of Hypothesis
Lesson 1. Introduction to Hypothesis Testing
Lesson 2. Test of Hypothesis Using the z-test
Lesson 3. Test of Hypothesis Using the t-test
Lesson 4. Confidence Level and Sample Size
Module IV. Correlation and Regression
Lesson 1. Correlation
Lesson 2. Linear Regression

MODULE 1
Random Variables and Probability Distributions
Lesson 1. Random Variables
Lesson 2. Probability Distribution of Discrete
Random Variable.
Lesson 3. Mean of Discrete Probability
Distribution.
Lesson 4. Variance of Discrete Probability
Distribution.

MODULE 1
Random Variables and Probability Distributions
Introduction
If you are interested in determining the certainty or uncertainty of the occurrence
of an event such as the cases of experiment or the possibility of rain fall today, you need
some knowledge in probability theory. However, there are times when you perform an
experiment and your interest lies in some numbers associated with the outcome and not
in the outcome itself. For example, in throwing two dice, you wish to get the sum of six
as expected outcome but you’re not interested in the particular numbers required to
achieve it. In cases like these, there is a need for mathematical models that describe the
behavior of the outcomes related to these statistical experiments. These theoretical
models are called probability distributions .
The expected outcomes in experiments, as well as the certainty and uncertainty of
the occurrence of an event, can be determined through probability theory.
OBJECTIVES
After studying the module, you should be able to:
° Distinguish between discrete and continuous random variables.