Introductory statistics Part1: Descriptive Statistics

Learn the concepts, calculations and applications of statistics at your own pace and comfort.
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Instructed by Luc Zio Business / Data & Analytics
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  • Lectures 29
  • Length 4.5 hours
  • Skill Level Beginner Level
  • Languages English
  • Includes Lifetime access
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About This Course

Published 10/2013 English

Course Description

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.

What are the requirements?

  • 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

What am I going to get from this course?

  • 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

What is the target audience?

  • College students
  • Professionals interested in understanding descriptive statistics
  • Students preparing to take college courses for credits

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: About the course: What are going to learn ?
04:21

This video explains the course goal and objectives.

Section 2: Data files for the test and comprehensive test to check for mastery.
1 page

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.

Section 3: Concepts of populations, samples, variables and type of sampling
05:47

This video starts with the definition of Statistics and talks at length about descriptive and inferential statistics.

05:59

This lecture talks about the concept of a population, sample, subject or units of the population and variables of interests. All these concepts are clearly explained for the student to grasp their meaning in a practical way.

11:03

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.

11:13

This lecture talks about qualitative and quantitative variables, the measurement level of data (nominal, ordinal, ratio, etc..) and the different type of statistical studies and the method of collecting data.

4 questions

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.

4 questions

In this quiz, you will be asked about concepts of populations, sample and variables of interest

Section 4: Describing qualitative and quantitative data in statistics
13:17

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.

02:36

This video illustrates the concepts of lower and upper class limits and shows how to compute the midpoints of the classes.

13:04

This video describes all the steps necessary for constructing a histogram. Those steps cover determining the optimal number of classes or bins using sound statistical techniques or rules of thumbs, grouping the data and constructing the histogram

09:25

This video describe how to construct an ogive chart,a frequency polygon chart and concludes by desribing
what is a Pareto chart and how to construct it.

03:45

This video talks about how to identify and avoid bad graphs in statistics and provides pointers for constructing good graphs.

Section 5: Statistical methods for describing the center, variation or spread of the data
11:47

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.

11:20

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.

03:05

In this lecture, we discuss how to classify statistical distributions as to skewed left, skewed right, bell-shaped and symmetrical.

2 questions

How to calculate the median and the mean

10:56

This video illustrates how to compute the sample variance using the shortcut as well as the regular formulas, it shows how to compute the sample standard deviation as well as the coefficient of variation of the data.

1 question

Demonstrating how to use the shortcut formula for computing the variance

2 questions

Test your knowledge computing the variance and sample standard deviation.

18:06

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.

3 questions

Applying the concepts about the empirical rule

3 questions

Calculting the percentage of data falling into an interval according to Chebushev's rule

Section 6: Descriptive statistics measures for grouped datasets
13:58

In this lecture we describe in full detail how to compute the mean, median, variance and standard deviation of a grouped data using real world practical examples.

Section 7: Numerical measures of relative standing
07:06

We describe how to compute and interpret the Z-scores using real world examples. We show how to use the Z-scores to identify outliers in datasets and also demonstrate the relationships between the Z scores and the empirical rule.

3 questions

Understand how to use use the Z score in making decision and interpreting data

15:58

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.

Section 8: Relationships between two variables:Correlations and simple linear regression.
10:24

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.

11:23

In this video, we illustrate through practical examples how to compute the sample correlation coefficient. In the process, we check for outliers
and influential points. We also verify that the assumpion of linearity is satisfied.

04:54

In this video, we discuss the basic concepts of linear regression and cover at length the concept of response and predictor variables. We also discuss the methods of least squares which is used to fit linear regression equation.

11:43

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.

Section 9: Real world applications of descriptive statistics using EXCEL
03:34

In this lesson, we will install the Analysis ToolPak Library and then use it to descriptive statistics computations

06:42

In this lecture, you will learn how to use the ANALYSIS TOOLPAK library to obtain descriptive statistics of the baseball players weights data.

03:22

In this lecture, you will learn how to compute the correlation coefficient of two quantitative variables in EXCEL and also how to interpret the results.

12:09

In this lecture, you will learn how to compute the sample Z scores of the data using EXCEL and to interpret the results.

08:11

In this lecture, you will learn how to construct a scatter plot of two quantitative variables and also fit a regression line to the data. You will also learn how to interpret the results.

Section 10: Conclusion and upcoming courses
08:59

Concluding remarks about the class

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Instructor Biography

Luc Zio, Adjunct faculty of Math and Statistics

I  have over 18 years of work experience in the field of statistics as an Applied Statistician. For the last  twelve years, I have also  been teaching undergraduate college level statistics courses at St Petersburg College.  As an Applied Statistician, I have developed over the years a strong interest in using EXCEL as a statistical tool with my classes in order to give to the students real world hands-on experience with Statistics.

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