Interactive Course

# STAT 300 - Modern Probability & Statistics

• 0 hours
• 0 Videos
• 72 Exercises
• 44 Participants
• XP

### Course Description

A Calculus-based introduction to probability and the application of mathematical principles to the collection, analysis, and presentation of data. Modern probability concepts, discrete/ continuous models, and applications; estimation and statistical inference through modern parametric, nonparametric, and simulation/randomization methods; maximum likelihood; Bayesian methods. This course prepares students for the preliminary P/1 exam of the Society of Actuaries and Casualty Actuarial Society.

A Calculus-based introduction to probability and the application of mathematical principles to the collection, analys...

### Course Outline

1. #### 2015 Trial

Description of this chapter

2. 2

#### Vectors and Data Frames

In this lab, you'll learn how to create and access vectors and data frames in R.

3. 4

#### Probability & Counting

After completing this chapter, you'll be able to simulate simple experiments to estimate probabilities.

4. 6

#### Binomial Distribution

After completing this chapter, you'll be able to calculate probabilities under the Binomial Distribution.

5. 1

#### Introduction to R

In this lab, you'll learn the basics of R. You'll use R as a calculator and then assign some variables.

6. 3

#### Intro to Statistical Inference

After completing this chapter, you'll be able to simulate simple experiments to estimate and interpret p-values.

7. 5

#### Counting & Permutation Tests

After completing this chapter, you'll be able to apply counting rules to estimate likelihoods.

8. 7

#### Discrete Distributions

After completing this chapter, you'll be able to calculate probabilities under the Binomial Distribution.

1. #### 2015 Trial

Description of this chapter

2. 1

#### Introduction to R

In this lab, you'll learn the basics of R. You'll use R as a calculator and then assign some variables.

3. 2

#### Vectors and Data Frames

In this lab, you'll learn how to create and access vectors and data frames in R.

4. 3

#### Intro to Statistical Inference

After completing this chapter, you'll be able to simulate simple experiments to estimate and interpret p-values.

5. 4

#### Probability & Counting

After completing this chapter, you'll be able to simulate simple experiments to estimate probabilities.

6. 5

#### Counting & Permutation Tests

After completing this chapter, you'll be able to apply counting rules to estimate likelihoods.

7. 6

#### Binomial Distribution

After completing this chapter, you'll be able to calculate probabilities under the Binomial Distribution.

8. 7

#### Discrete Distributions

After completing this chapter, you'll be able to calculate probabilities under the Binomial Distribution.

### Course Instructor

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