Introductory statistics Part1: Descriptive Statistics
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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.
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Section 1: About the course: What are going to learn ? | |||
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Lecture 1 | 04:21 | ||
This video explains the course goal and objectives. |
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Section 2: Data files for the test and comprehensive test to check for mastery. | |||
Lecture 2 | 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. |
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Section 3: Concepts of populations, samples, variables and type of sampling | |||
Lecture 3 | 05:47 | ||
This video starts with the definition of Statistics and talks at length about descriptive and inferential statistics. |
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Lecture 4 | 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. |
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Lecture 5 | 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. |
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Lecture 6 | 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. |
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Quiz 1 | 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. |
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Quiz 2 | 4 questions | ||
In this quiz, you will be asked about concepts of populations, sample and variables of interest |
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Section 4: Describing qualitative and quantitative data in statistics | |||
Lecture 7 | 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 |
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Lecture 8 | 02:36 | ||
This video illustrates the concepts of lower and upper class limits and shows how to compute the midpoints of the classes. |
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Lecture 9 | 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 |
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Lecture 10 | 09:25 | ||
This video describe how to construct an ogive chart,a frequency polygon chart and concludes by desribing |
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Lecture 11 | 03:45 | ||
This video talks about how to identify and avoid bad graphs in statistics and provides pointers for constructing good graphs. |
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Section 5: Statistical methods for describing the center, variation or spread of the data | |||
Lecture 12 | 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. |
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Lecture 13 | 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. |
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Lecture 14 | 03:05 | ||
In this lecture, we discuss how to classify statistical distributions as to skewed left, skewed right, bell-shaped and symmetrical. |
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Quiz 3 | 2 questions | ||
How to calculate the median and the mean |
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Lecture 15 | 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. |
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Quiz 4 | 1 question | ||
Demonstrating how to use the shortcut formula for computing the variance |
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Quiz 5 | 2 questions | ||
Test your knowledge computing the variance and sample standard deviation. |
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Lecture 16 | 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. |
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Quiz 6 | 3 questions | ||
Applying the concepts about the empirical rule |
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Quiz 7 | 3 questions | ||
Calculting the percentage of data falling into an interval according to Chebushev's rule |
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Section 6: Descriptive statistics measures for grouped datasets | |||
Lecture 17 | 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. |
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Section 7: Numerical measures of relative standing | |||
Lecture 18 | 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. |
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Quiz 8 | 3 questions | ||
Understand how to use use the Z score in making decision and interpreting data |
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Lecture 19 | 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. |
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Section 8: Relationships between two variables:Correlations and simple linear regression. | |||
Lecture 20 | 10:24 | ||
In this video, we talk about the relationship about two quantitative variables, we cover the concept of positively and negatively correlated variables, |
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Lecture 21 | 11:23 | ||
In this video, we illustrate through practical examples how to compute the sample correlation coefficient. In the process, we check for outliers |
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Lecture 22 | 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. |
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Lecture 23 | 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. |
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Section 9: Real world applications of descriptive statistics using EXCEL | |||
Lecture 24 | 03:34 | ||
In this lesson, we will install the Analysis ToolPak Library and then use it to descriptive statistics computations |
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Lecture 25 | 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. |
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Lecture 26 | 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. |
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Lecture 27 | 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. |
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Lecture 28 | 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. |
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Section 10: Conclusion and upcoming courses | |||
Lecture 29 | 08:59 | ||
Concluding remarks about the class |
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.