This course covers the basics of descriptive and inferential statistics, including sampling methods, types of variables, frequency distributions, central tendency, probability, random variables, correlation, hypothesis testing, regression, and ANOVA. Students will learn how to analyze data, calculate probabilities, interpret results, and make informed decisions based on statistical analysis. The course uses a combination of lectures, examples, and practice problems to teach statistical concepts and techniques. This course is intended for individuals who want to develop their statistical analysis skills, whether for academic, professional, or personal purposes.
Overview
Syllabus
The Basics: Descriptive and Inferential Statistics.
Sampling Methods.
Types of Variables.
Independent and Dependent Variables.
Variable Measurement Scales.
Frequency Distributions and Cumulative Frequency Distributions.
Bar Graphs and Pie Charts.
Histograms and Stem & Leaf Plots.
Arithmetic Mean for Samples and Populations.
Central Tendency: Mean, Median, and Mode.
Variance and Standard Deviation of a Population.
Variance and Standard Deviation of a Sample.
Percentiles and Quartiles.
The Five Number Summary, Interquartile Range(IQR), and Boxplots.
The Effects of Outliers.
Skewness.
The Normal Curve and Empirical Rule.
Z-Scores (part one).
Z-Scores (part two).
Extra Z-Score Problems.
The Basics of Probability.
Addition Rule (Probability "or").
Multiplication Rule (Probability "and").
Permutations.
Combinations.
Discrete and Continuous Random Variables.
Discrete Probability Distributions.
Probability Histograms.
Mean and Expected Value of Discrete Random Variables.
Variance and Standard Deviation of Discrete Random Variables.
The Law of Large Numbers.
Binomial Distribution.
Mean and Standard Deviation of Binomial Random Variables.
Poisson Distribution/Process.
Mean and Standard Deviation of Poisson Random Variables.
Coordinate (Cartesian) Planes.
Quadrants.
Scatter Plots.
Pearson's r Correlation.
Hypothesis Testing with Pearson's r.
Spearman Correlation.
Linear Regression.
Correlation vs. Causation.
Parameters, Statistics, and Sampling Error.
Distribution of the Sample Mean.
The Central Limit Theorem.
Sample Proportions.
Confidence Intervals about the Mean, Population Standard Deviation Known.
Calculating Required Sample Size to Estimate Population Mean.
Student's t-Distribution.
Confidence Intervals about the Mean, Population Standard Deviation Unknown.
Confidence Intervals for Population Proportions.
Null and Alternative Hypotheses.
Type I and Type II Errors.
One-Tailed and Two-Tailed Tests.
Effect Size.
Power.
Statistical vs. Practical Significance.
Independent and Dependent Samples.
One Sample z-Test.
One Sample z-Test for Proportions.
One Sample t-Test.
Independent Samples t-Test.
Confidence Intervals for Independent Samples t-Test.
Effect Size for Independent Samples t-Test.
Dependent Samples t-Test.
Confidence Intervals for Dependent Samples t-Test.
Effect Size for Dependent Samples t-Test.
z-Test for Proportions, Two Samples.
Confidence Intervals for the Difference of Two Proportions.
Introduction to ANOVA.
One-Way ANOVA.
Effect Size for One-Way ANOVA.
Post-Hoc Tests for One-Way ANOVA.
Repeated-Measures ANOVA.
Factorial ANOVA, Two Independent Factors.
Factorial ANOVA, Two Dependent Factors.
Factorial ANOVA, Two Mixed Factors.
Chi-Square Test for Goodness of Fit.
Chi-Square Test for Independence.
Mann-Whitney U-Test.
Wilcoxon Signed-Ranks Test.
The Kruskal-Wallis Test.
The Friedman Test.
Calculating Required Sample Size to Estimate Population Proportions.
User Submitted Question: Alpha Levels.
Taught by
statslectures
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Reviews
5.0 rating, based on 1 Class Central review
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Statistics made practical and presented in a manner that is very understandable. I strongly recommend this course to every researcher and statistician willing to understand the practical aspect of statistics.
