For a layman, ‘Statistics’ means numerical information expressed in quantitative terms. This information may relate to objects, subjects, activities, phenomena, or regions of space.

• In this chapter, you will learn about Statistical Problems, Descriptive Statistics, Graphical Methods, Frequency Distributions (Histograms), Other Methods, Numerical methods, Measures of Central Tendency, Measures of Variability, Empirical Rule and Percentiles.

• In this module you will study Sample Space and Events, Probability of an Event, Equally Likely Outcomes, Conditional Probability and Independence, Laws of Probability, Counting Sample Points, and Random Sampling.

• This chapter covers Random Variables, Expected Values and Variance, Binomial, Poisson, and Hypergeometric.

This module covers Standard Normal, Normal, Uniform, and Exponential. The continuous recall arises in situations when the population (or possible outcomes) are continuous (or quantitative).

In this chapter, you will learn about the Central Limit Theorem, and the Sampling Distribution of the Sample Mean, the Sample Proportion, the Difference Between Two Sample Means, and the Difference Between Two Sample Proportions.

In this chapter, you will learn to construct and interpret confidence intervals. You will also learn a new distribution, the Student’s-t, and how it is used with these intervals.  Throughout the chapter, it is important to keep in mind that the confidence interval is a random variable. It is the population parameter that is fixed.

Now we are down to the bread-and-butter work of the statistician: developing and testing hypotheses. It is important to put this material in a broader context so that the method by which a hypothesis is formed is understood completely.

The comparison of two independent population means is very common and provides a way to test the hypothesis that the two groups differ from each other.

The chi-square distribution can be used to find relationships between two things, like grocery prices at different stores.

This chapter introduces a new probability density function, the F distribution. This distribution is used for many applications including ANOVA and testing equality across multiple means.

Correlation analysis measures the strength of the arithmetic relationship between two variables. Correlation may be visually represented with a scatter diagram.

In this chapter, you will learn that simple regression analysis defines the mathematical relationship between two variables.