Course is recommended for college credit by the American Council on Education's Credit Recommendation Service (ACE CREDIT)



Course is recommended for college credit by the American Council on Education's Credit Recommendation Service (ACE CREDIT)

Earn Statistics College Credits On Your Schedule. This course will introduce you to the study of Statistics. Statistics is a course involving numbers, but what’s relevant about the subject is the information an observer can obtain or learn from the numbers. Statistics is a science that involves not just numbers but also the gathering of information and an analytical process that helps achieve a goal. For example, a financial analyst may want to know the likelihood that the economy will fall into a recession next year. Used appropriately, an economist may apply the rules of probability and a level of confidence to answer the question.

Statistics is a skill set relevant to many fields: business, physical and social sciences, and even the arts. In this course, students will learn meaningful ways to describe data beginning with the qualitative and quantitative distinction. They’ll study basic statistical concepts and techniques for collecting data, interpreting relationships, and predicting outcomes. For example, a simple random sample is one way to collect information about a population. What’s the difference between a population and a sample and what can we learn from the data provided? You’ll just have to see! Students will also learn how to translate information into organized systems applicable to countless real-world scenarios.

Sample Syllabus Sections 1 & 2


1.1 Introduction to Statistics - General purpose of statistics and statistical thinking; define data, population, individual and sample

1.2 Math Review for Statistics - Sigma as a summation symbol; Define i and i.

1.3 Observations, Experiments, and Surveys - The difference between an observational study, an experiment, and a survey; define census, observation, primary data, and secondary data

1.4 Confounding and Lurking Variables - Define constant and variable; independent and dependent variables

1.5 Practical Considerations for Data Collection - Constraints of data collection

1.6 Simple Random Sampling - Examples of random samples with and without replacement

1.7 Qualitative and Quantitative Variables - Qualitative, or categorical data, and quantitative, or numerical data

1.8 Descriptive and Inferential Statistics - Define and differentiate statistics and parameter

1.9 Discrete and Continuous Variables - Discrete and continuous data, variables

1.10 Measuring Variables - Level of measurement of a variable; link between scales of measurement and qualitative-quantitative distinction; nominal, ordinal, interval, and ratio scales

1.11 A Look at Ethics and Statistics - Define ethics; identify the American Statistical Association ethical guidelines and the five key parts; misrepresentation of data


2.1 Data Organization and Display - Raw data, analysis, graph and prediction

2.2 Frequency - Organizing data into tables; calculating frequency

2.3 Relative Frequency - Relative frequency and frequency distribution

2.4 Cumulative Frequency - Distribution and frequency tables

2.5 Displaying Qualitative Data - Bar and pie charts

2.6 Displaying Quantitative Data: Stem-and-Leaf and Dot Plots - Define stem-and-leaf plot and dot plot and describe how they are used; find the range and median; identify histograms

2.7 Displaying Quantitative Data: Discrete Data I - Quantitative, discrete, and continuous data; define data class and histogram; identify histograms and tables using discrete data

2.8 Displaying Quantitative Data: Discrete Data II - Interpret a histogram

2.9 Displaying Quantitative Data: Continuous Data - Organize and display continuous data; define and calculate class width, lower and upper class limits

2.10 Shape of a Distribution - Define various types of distribution; begin to calculate probabilities

2.11 Skew - Skewed left, skewed right and non-skewed distributions; define deviations

2.12 Displaying Quantitative Data: Ogives - Define frequency ogives and relative frequency ogives

2.13 How Graphs Can Mislead - Describe inconsistencies, manipulations


3.1 Measures of Central Tendency,  More Units include:  Unit 4: Measures of Dispersion,  Unit 5: Measures of Position and Dispersion,  Unit 6: Probability,  Unit 7: Counting,  Unit 8: The Normal Distribution,  Unit 9: Correlation of Two Variables,  Unit 10: Regression of Two Variables,  Unit 11: Estimation and Confidence Intervals,  Unit 12: Hypothesis Tests on a Single Parameter,  Unit 13: Test a hypothesis regarding three or more means,  Unit 14: Special Topics in Statistics,  Unit 15: Discrete Probability Distributions -

Sample Questions
Test Your Current Knowledge of Statistics

  1. Scores that were collected for each item in a leadership-style inventory
  2. Age data in years that was recoded into child, adolescent, and adult categories
  3. Attitudinal data that was averaged to make a single attitude scale score
  4. Income data after extreme values, called outliers, were removed

  1. The curve is asymmetrical.
  2. The highest point is anywhere in the curve.
  3. The highest point in the curve is the range containing the highest probability.
  4. The tails on both sides increase in value, indicating a greater probability of occurring.

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Credit-by-Exam Prep Course

Buy the entire Statistics course and you get 16 course units. Each course is broken down into sections, which include educational videos, lecture notes, interactive quizzes and a quiz at the end of each section to get you ready to pass the DSST exam and earn college credits.


ACE Course

Course + online, proctored final exam. Each course unit is broken down into sections, which include educational videos, lecture notes, interactive quizzes and a practice test at the end of each section. Be prepared to pass the final exam and earn college credits all at your own pace and on your own time!
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Example Lecture Videos

Statistics - Nominal Measurements

Statistics - Ordinal Scale

Statistics - Interval Scale

Statistics - Qualitative or Quantitative

Statistics - Weighted Average

Statistics - Total Return Rate