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 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.

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.

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.

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.

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

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

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.

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

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 how to classify statistical distributions as to skewed left, skewed right, bell-shaped and symmetrical.

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.

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

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.

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 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.

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.

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

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

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.

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

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.

In this lecture, you'll learn how to create a Boxplot in EXCEL, a powerful tool for visualizing data distributions. Boxplots, also known as box-and-whisker plots, are great for identifying the spread and central tendencies of your data, such as the median, quartiles, and potential outliers.

Step-by-step, I'll guide you through the process of preparing your data, selecting the appropriate chart type in EXCEL, and customizing the boxplot to highlight key insights. By the end of this lecture, you’ll have hands-on experience in creating boxplots and interpreting the results, a skill that's essential for analyzing data distributions in various professional and academic fields.

Whether you're new to EXCEL or looking to enhance your data visualization skills, this lecture will provide you with the tools to better understand and communicate your data's story.

In this lecture, you'll learn how to create a Boxplot in EXCEL, a powerful tool for visualizing data distributions. You will learn to handle the case when the X variable is a numeric and to convert it to character first before you will be able to complete the chart.

Boxplots, also known as box-and-whisker plots, are great for identifying the spread and central tendencies of your data, such as the median, quartiles, and potential outliers.

Concluding remarks about the class