Introduction To Statistics (Full Course)

Entry Level Statistics College Course

What you'll learn

Express statistical information using proper terminology and graphs

Calculate measures of central tendency and dispersion

Interpret measures of central tendency and dispersion

Calculate probabilities and statistical measures

Perform sampling techniques

Determine appropriate probability models for given situations

Estimate parameters based on samples

Perform hypothesis testing

Analyze contingency tables using chi square technique

Determine linear relationship using regression and correlation

Use one-way ANOVA

Requirements

Basic Algebra Skills are needed for learning Statistics

Description

An overview of the ideas and concepts that are basic to modern statistics. Topics include descriptive statistics, probability, estimation, hypothesis testing, and linear regression. Students will be exposed to applications from a variety of fields.This course focuses on statistical reasoning and the solving of problems using real-world data rather than on computational skills. Emphasis is on interpretation and evaluation of statistical results that arise from simulation and technology-based computations using technology more advanced than a basic scientific calculator, such as graphing calculators with a statistical package, spreadsheets, or statistical computing software. Topics must include data collection processes (observational studies, experimental design, sampling techniques, bias), descriptive methods using quantitative and qualitative data, bivariate data, correlation, and least squares regression, basic probability theory, probability distributions (normal distributions and normal curve, binomial distribution), confidence intervals and hypothesis tests using p-values.This is a college entry statistics course, where any student can take this as long as you have basic algebra skills. AP Statistics students could also take this course to review all the basic concepts.There is one chapter on Probability, and it covers all the basics principle of probabilities needed for 1st semester statistics.We use TI Graphing Calculator to calculate all the statistics formulas in this course.Upon successful completion of this course, the student should be able to do the following:Produce and interpret descriptive statistics, graphically, numerically, and in tabular format.Calculate and interpret probability using union and intersection rules.Explain the concepts of random variable and distribution.Use technology to calculate probabilities with the normal and binomial distributions.Produce a confidence interval estimate from a given sample.Explain the rationale of hypothesis testing.Carry out, with the aid of technology, a variety of hypothesis tests, including z-tests and t-tests and interpret the meaning of the results.Use correlation analysis to determine the strength of a linear relationship between bivariate data and apply linear regression to describe this relationship.

Overview

Section 1: Chapter 1: Sampling and Data

Lecture 1 Sampling and Data

Section 2: Chapter 2: Displaying Data

Lecture 2 Displaying Data

Section 3: Chapter 3: Numerical Descriptors

Lecture 3 Numerical Descriptors

Section 4: Chapter 4: Probability Topics

Lecture 4 Probability Topics

Section 5: Chapter 5: Discrete Random Variables

Lecture 5 Discrete Random Variables

Section 6: Chapter 6: Continuous Random Variables

Lecture 6 Continuous Random Variables

Section 7: Chapter 7: Normal Distribution

Lecture 7 Normal Distribution

Section 8: Chapter 8: The Central Limit Theorem

Lecture 8 The Central Limit Theorem

Section 9: Chapter 9: Confidence Intervals

Lecture 9 Confidence Intervals

Section 10: Chapter 10: Hypothesis Testing with One Sample

Lecture 10 Chapter 10: Hypothesis Testing with One Sample

Section 11: Chapter 11: Hypothesis Testing with Two Sample

Lecture 11 Chapter 11: Hypothesis Testing with Two Samples

Section 12: Chapter 12: The Chi-Square Distribution Continued

Lecture 12 Chapter 12: The Chi-Square Distribution Continued

Section 13: Chapter 13: Linear Regression and Correlation

Lecture 13 Chapter 13: Linear Regression and Correlation

Section 14: Chapter 14: F-Distribution and One-Way Anova

Lecture 14 Chapter 14: F-Distribution and One-Way Anova

This course is perfect for anyone that needs to take Statistics or Probability in College/High School.