Introductory statistics Part1: Descriptive Statistics
- 4 hours on-demand video
- 10 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
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- By the end of this course you will be knowledgeable in using descriptive statistical analysis techniques to summarize and analyze the data
- By the end of this course you will be able to compute measures of center of the data, measures of spread, measures of relative positions
- By the end of this course, you will know how to use the empirical rule for analyzing data
- By the end of this course you will know how to compute the correlation coefficient and make interpretations of the data
- By the end of this course, you will understand the concepts of sample, population and the different methods of sampling
The Body and brain weight data file allows the student to compute the correlation and regression line between Body weight and Brain weight.
The Square footage and House sale Price data is used for fitting a linear regression and forecasting future sales.
The EPA mileage ratings data file is used to demonstrate how to apply the empirical rule as well as creating histograms.
The Appraised value and House sale Price data is used for fitting a linear regression.
The FBI crime data is used for constructing a Pareto's chart.
This video starts with the definition of Statistics and talks at length about descriptive and inferential statistics.
This lecture talks about the different type of sampling in statistics. We cover the concept of random sampling, stratified, systematic and cluster sampling. We also discuss non-probability samples such as quota, purposive and haphazard self-selected sampling.
A study was conducted at a large university to estimate the percent of students using twitter as a social media way of communication. A sample of 1500 students was selected for the study and they were asked whether or not they have used twitter at least once in the past month.
This video describes how to analyze qualitative data sets by using frequency distribution tables, bar graphs and pie charts. It shows how quantitative data
can be analyzed using Tukey's stem and leaf plots and introduces histograms for analyzing large quantitative data sets.
In this video we show how to construct the mean, discuss the advantages and disadvantages of the mean, we illustrate how to calculate a weighted mean with real world examples.
In this lecture we show how to effectively compute the geometric and harmonic means using real data, how to compute the median of the data and make interpreations using the median.
We also show how to compute the mode and the midrange of the data.
In this lecture we discuss the empirical rule as well as Chebyshev's theorem. We show how to apply the empirical rule to data in order to verify if the data is mound-shaped and symmetric. We also illustrate through real world data how to apply Chebyshev's theorem to real world examples.
In this lecture, we show how to compute percentiles, percentiles ranks, quartiles. We illustrate how to use Tukey's methodology to identify outliers in the data and also talk about the 5-number summary of the data which are the minimum, maximum, the first quartile, the second quartile or median and the third quartile. We talk about how a boxplot can be constructed using the 5-number summary of the dataset.
In this video, we talk about the relationship about two quantitative variables, we cover the concept of positively and negatively correlated variables,
we discuss about the correlation coefficient and its properties.
In this video, we show how to calculate the equation for a simple linear regression equation through practical examples. We talk about how to identify outliers in the data and refitting the equation without the outliers. We discuss the measures of fit of the data such as the coefficient of determination. We also talk about predicting future observations in the data and making interpretations of the results.
In this lecture, you will learn how to use the ANALYSIS TOOLPAK library to obtain descriptive statistics of the baseball players weights data.
- At least 3 semester hours of algebra at the college or Advanced Placement level
- Scientific calculator such as TI 83/84 or any scientific calculator such as TI 39
The course was updated recently to include a Real Word Applications section using EXCEL to analyze descriptive statistics data.
I am using hands on REAL WORLD data sets in EXCEL to illustrate how to analyze data in the various concepts that are covered.
Even if you never used EXCEL before, you will be able to follow my steps to load the data and select the appropriate tabs to easily analyze your data.
This section is very handy for professionals and college students who need to analyze data and make interpretations.
This course presents sound college level material about descriptive statistics. It is intended for college students and professionals interested in learning and applying the concepts of descriptive statistics.
The course is presented in a way that helps students to understand and be able to also apply the concepts the concepts themselves and to succeed.
All the topics are treated extensively with a wealth of solved problems to help the students understand how to pratically solve similar problems.
Descriptive statistics is one area of statistical applications that uses numerical and graphical techniques to summarize the data, to look for patterns and to present the information in a useful and convenient way.
Detailed and fully solved exercises are provided in the videos with great comments to help students understand the material. In addition, a wealth of quizzes and a test is provided at the end of the course for students who want to test their mastery of the material.
The course will require around three hours to complete. A test is available to allow the student to demonstrate a mastery of the subject matter.
- College students
- Professionals interested in understanding descriptive statistics
- Students preparing to take college courses for credits