Introduction to Statistics
- 2.5 hours on-demand video
- 7 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
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- Understand and learn how to calculate a number of different descriptive statistics
- Increase your quantitative and numerical reasoning skills!
- Increase marketable job skills in data analytics
- No special software or other materials are required.
In the course, you will learn how to easily and effectively analyze and interpret data involving introductory statistics. The following topics are covered in this course:
Scales of measurement - nominal, ordinal, interval, ratio.
Goal/Learning Objective: Easily understand the often-confused scales of measurement covered in most statistics texts.
Central Tendency - mean, median, and mode are illustrated along with practice problems; measures of central tendency and skewed distributions are explained, as well as how to calculate the weighted mean.
Goals/Learning Objectives: Summarize a set of data, find the center location in a distribution of scores, understand and identify the location of measures of central tendency in skewed distributions, understand and interpret how to find the overall or combined mean for two different sets of data.
Variability - How to calculate the standard deviation and variance as well as how to interpret percentiles are provided in simple and clear language.
Goals/Learning Objectives: Understand and explain variability (spread) in a set of numbers, including how to rank data and interpret data such as standardized test scores (for example, the 95th percentile).
Charts and Graphs - How to calculate a cumulative frequency distribution table as well as how to calculate a stem and leaf plot is illustrated.
Goals/Learning Objectives: Learn how to easily organize, summarize, understand, and explain a set of numbers.
Probability, the Normal Curve and z-Scores - An introduction to probability is provided, along with properties of the normal distribution and how to calculate and interpret z-scores
Goals/Learning Objectives: Understand beginning probability including important characteristics of the normal (Gaussian) distribution, as well as how to calculate and interpret z-scores.
Bonus Features: Cement understanding with practice opportunities including several quizzes with complete video coverage of the solutions.
Update: New Videos Added on Hypothesis Testing and on Correlation! (See Sections 6 and 7 of the Course.)
- Those interested in learning more about descriptive statistics should take this course (those interested only in inferential statistics should not take the course)
An introduction to both the course and to the instructor is provided in this video.
In this video, the scales of measurement are covered, of which there are four in total: nominal, ordinal, interval, and ratio.
This video lecture covers the mean, median, and mode. First the mode is covered, including examples of two modes (bimodal) and three or more modes (multimodal). Next, finding the median is covered for both an even and odd number of values. After the median, how to calculate the mean (arithmetic average) is covered.
In this video, we take a look at the relationship between the mean, median, and mode and asymmetrical (skewed) distributions. As the video illustrates, the order of the three measures of central tendency (where they fall on a number line in relation to each other) depends on whether a distribution is positively or negatively skewed.
In this video, we take a look at how to create a frequency distribution table. Frequency distribution tables are helpful for organizing a set of values. They allow for quick identification of several statistical values, such as the minimum and maximum values, and the mode for a set of data.
In this video, we examine how to construct a cumulative frequency distribution table, which includes the columns X, f, and cf. X corresponds to the values (or scores) of a variable X, f is the frequency value for each X (how many of each X there are), and cf is the cumulative frequency.
- Goal: The purpose of creating this table is to organize (or provide order) to a set of numbers, which is one of the goals of statistics. After the table is created, notice how the largest and smallest values can be quickly and easily noted, as well as the modal (mode) value in the data set. Therefore, this table provides 'order' to the values.
In this video we examine how to construct a stem and leaf plot on a set of numbers ranging from the tens to fifties.
- Goal: Like the frequency distribution table, the purpose of creating this table is to organize (or provide order) to a set of numbers, which is one of the goals of statistics. After the table is created, notice how the largest and smallest values can be noted fairly easily, as well as the modal (mode) value in the data set. Therefore, this table also provides 'order' to the values. As noted in the video, stem and leaf plots offer the added benefit of providing a plot of the distribution of values (if viewed from the side).
In this video, the normal distribution and z scores are covered. First, properties of the normal distribution are described, including how the mean, median, mode are equal to zero and how the normal distribution is symmetrical. Next the areas under the curve are illustrated, closing with a demonstration of the 68, 95, 99.7 rule for values that are 1, 2, and 3 standard deviations away from the mean.
In this video lecture, we take a look at the properties of the z score normal distribution, including (1) that it is symmetrical, (2) that the mean, median, and mode are all equal to zero, and (3) that the standard deviation is equal to 1.
In this video lecture, z scores are covered, including how to solve for z scores for a number of different examples. Also illustrated is how the z score indicates the number of standard deviations a value is from the mean. For example, a z score of 1.5 indicates that a value is 1.5 standard deviations above the mean.
In this video the null and alternative hypotheses are covered for one-tailed tests. One tailed tests are used when the researcher / analyst is looking for a specific outcome (or direction of effect) and usually isn't interested if the opposite direction of effect is found.