# Our Online Statistics Courses

As a member of STATSprofessor.com when you log into the site, you will land at your member's home page. On the left hand side of the page will be a list of chapters for your course. Simply click the chapters to browse the topics contained within. Once you find the topic that you need help with, just click it to begin streaming the videos for that topic. Watch the videos as many times as you need until you understand the topic fully. When you feel you are ready for your test, take one of our sample exams to test your understanding.

You can peruse the course content below or go directly to one of our courses.

## Stats 1 Course Chapters & Topics

The Stats 1 course features more than 450 videos and four sample exams.

 1. Introduction 1.1 Welcome 1.2 Sample Statistics and Population Parameters 1.3 Types of Data - Discrete vs. Continuous 2. Methods for Describing Sets of Data 2.1 Frequency Tables and Histograms 2.2 Summation Notation 2.3 Measures of Central Tendency: Mean, Median, Mode 2.4 Skewed Distributions 2.5 Measures of Variability: Range, Standard Deviation, and Variance 2.6 Chebyshev’s Theorem 2.7 Empirical Rule 2.8 Measures of Relative Standing – Z scores 3. Probability 3.1 Sample Spaces and Tree Diagrams 3.2 Basic Probability 3.3 Fundamental Counting Rule and Combinations 3.4 Additive Rule of Probability 3.5 Conditional Probability 3.6 Multiplicative Rule of Probability 3.7 Probability of At Least One 4. Discrete Random Variables 4.1 Probability Distributions for Discrete Random Variables 4.2 Expected Value: The Mean of a Discrete Random Variable 4.3 Standard Deviation of a Discrete Random Variable 4.4 Binomial Distribution and Binomial Probability 4.5 Using the Binomial Table 4.6 Mean, Variance, and Standard Deviation of a Binomial Random Variable 5. Normal Random Variables 5.1 Using the Z-table 5.2 Probability Using the Normal Distribution 5.3 Finding Percentiles of the Normal Curve (Using the Table in Reverse) 5.4 Normal as Approximation to Binomial 6. Sampling Distributions 6.1 Minimum Variance Unbiased Point Estimators 6.2 Using the Central Limit Theorem 7. Confidence Intervals 7.1 Finding Critical Z Values 7.2 Large-Sample Confidence Intervals for a Population Mean 7.3 Determining the Sample Size 7.4 Finding Critical T Values 7.5 Small-Sample Confidence Intervals for a Population Mean 7.6 Confidence Intervals for a Population Proportion 8. Tests of Hypothesis-One Sample 8.1 Determining the Claim, Null and Alternative Hypotheses 8.2 Critical Values for the Rejection Region 8.3 Large-Sample Test of Hypothesis about a Population Mean 8.4 Observed Significance Levels: p-Values 8.5 Small-Sample Test of Hypothesis about a Population Mean 8.6 Hypothesis about a Population Proportion 8.7 Type I and Type II Error Probabilities 9. Confidence Intervals and Tests of Hypothesis-Two Samples 9.1 Z-Interval to Compare Two Population Means: Independent Samples 9.2 Test to Compare Two Population Means: Independent Samples 9.3 t-Interval to Compare Two Population Means: Independent Samples (Equal Variances) 9.4 t-Test to Compare Two Population Means: Independent Samples (Equal Variances) 9.5 t-Interval to Compare Two Population Means: Independent Samples (Unequal Variances) 9.6 t-Test to Compare Two Population Means: Independent Samples (Unequal Variances) 9.7 Hypothesis Test to Compare Two Population Means: Matched-Pair Experiments 9.8 Confidence Interval to Compare Two Population Means: Matched-Pair Experiments 9.9 Hypothesis Test to Compare Two Population Proportions: Independent Sampling 9.10 Using the f-table to Find Critical Values 9.11 Hypothesis Test to Compare Two Population Variances: Independent Sampling

## Stats 2 Course Chapters & Topics

The Stats 2 course features more than 390 videos and four sample exams.

 7. Confidence Intervals 7.1 Finding Critical Z Values 7.2 Large-Sample Confidence Intervals for a Population Mean 7.3 Determining the Sample Size 7.4 Finding Critical T Values 7.5 Small-Sample Confidence Intervals for a Population Mean 7.6 Confidence Intervals for a Population Proportion 8. Tests of Hypothesis-One Sample 8.1 Determining the Claim, Null and Alternative Hypotheses 8.2 Critical Values for the Rejection Region 8.3 Large-Sample Test of Hypothesis about a Population Mean 8.4 Observed Significance Levels: p-Values 8.5 Small-Sample Test of Hypothesis about a Population Mean 8.6 Hypothesis about a Population Proportion 8.7 Type I and Type II Error Probabilities 9. Confidence Intervals and Tests of Hypothesis-Two Samples 9.1 Z-Interval to Compare Two Population Means: Independent Samples 9.2 Test to Compare Two Population Means: Independent Samples 9.3 t-Interval to Compare Two Population Means: Independent Samples (Equal Variances) 9.4 t-Test to Compare Two Population Means: Independent Samples (Equal Variances) 9.5 t-Interval to Compare Two Population Means: Independent Samples (Unequal Variances) 9.6 t-Test to Compare Two Population Means: Independent Samples (Unequal Variances) 9.7 Hypothesis Test to Compare Two Population Means: Matched-Pair Experiments 9.8 Confidence Interval to Compare Two Population Means: Matched-Pair Experiments 9.9 Hypothesis Test to Compare Two Population Proportions: Independent Sampling 9.10 Using the f-table to Find Critical Values 9.11 Hypothesis Test to Compare Two Population Variances: Independent Sampling 10. Analysis of Variance: Comparing More Than Two Means 10.1 ANOVA: The Completely Randomized Design 10.2 Multiple Comparisons of Means 10.3 ANOVA: The Randomized Block Design 11. Simple Linear Regression 11.1 Creating the Least Squares Equation 11.2 Finding S for the Random Error Terms 11.3 Hypothesis Tests about the Slope β1 11.4 Finding the Standard Error of the Slope Estimator 11.5 Confidence Interval for the Slope β1 11.6 Finding r the Coefficient of Correlation 11.7 Finding r-squared the Coefficient of Determination 11.8 Using the Model to Create an Estimation Interval 11.9 Using the Model to Create a Prediction Interval 13. Categorical Data Analysis: Chi-Square Tests 13.1 Finding Chi-Square Critical Values 13.2 Checking the Assumptions for a Chi-Square Goodness-of-Fit Test 13.3 The Chi-Square Test Statistic 13.4 Testing Categorical Probabilities: One-Way Table 13.5 Finding Expected Cell Counts 13.6 Testing Categorical Probabilities: Two-Way (Contingency) Table 14. Nonparametric Statistics 14.1 Using the Binomial Table 14.2 Normal as Approximation to Binomial 14.3 Ranking Data 14.4 The Sign Test 14.5 Wilcoxon Ranked Sum Test for Independent Samples 14.6 Wilcoxon Signed-Ranks Test for Paired Difference Experiments 14.7 Kruskal-Wallis H-Test: Completely Randomized Design 14.8 Friedman Fr-Test: Randomized Block Design