Advanced Statistics functions in EXCEL 20102013 (Part 2)
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Updated material to include latest advanced Statistical functions in EXCEL 2013
This course has been updated to include the latest advanced statistical functions in EXCEL 2013. It provides you with a vast array of analytical tools to handle most
The course is about using the advanced statistical functions in EXCEL for data analysis and analytics projects.
You will learn how to effectively use Advanced Excel Statistical Functions about the Normal distribution, Studentt distribution, chi square distribution, F distribution, Gamma, Lognormal, Negative Binomial, Exponential, etc.
You will learn how to calculate the margin of errors and use them to create confidence intervals. In addition, you will learn how to create Standard Normal tables as well as Student t tables.
This course is very well illustrated with lots of practical illustrations and quizzes to help you grasp the concepts of each function and understand them in a practical way. Excel files and screenshots are provided to make the understanding of the lectures very easy.
Depending on the prior preparation, this course can be completed in one month or less.
Taking this course will provide you with a rich array of advanced analytical tools that can be used for educational and professional work.
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Section 1: Welcome to Statistical functions in EXCEL 2010 (Part 2 Intermediate and Advanced)  

Lecture 1  03:51  
Introduction about the class and me  
Lecture 2  05:57  
Learning the main window interface and inserting functions  
Section 2: Functions related to the normal or bellshaped distribution  
Lecture 3  06:27  
In this video, we are computing standard normal distributions probabilities. This is equivalent to reading the Standard Normal Z table for finding probabilities, except that here, EXCEL computes it directly for us. 

Lecture 4  07:09  
In this video, we learn how to compute probabilities about a Normal distribution with a given mean and standard deviation. 

Lecture 5  04:55  
In this lesson, we learn how to solve problems related to the inverse of the stand normal distribution. For example, if the weights of pygmy goats are normal with a mean of 80 pounds and standard deviation of 3.5 pounds, what goat weight corresponds to the 95th percentile? 

Lecture 6  03:01  
In this lesson, we are calculating inverse or Zvalue of a standard normal distribution. For example, what is the Z value corresponding to the 90th percentile of the standard normal distribution? 

Lecture 7  07:35  
In this lesson, we use the standard normal density function to create a bellshaped curve. This curve comes in handy if you want to show how the bellshaped distribution looks like. 

Lecture 8  07:19  
In this video, we use builtin standard normal distribution functions to create a standard normal table. The table can be printed and used in lieu of the tables in statistical books. 

Lecture 9  01:39  
This video shows how to compute the Z values or standardize the data in EXCEL 2010 

Lecture 10  04:39  
In this lesson we show how to use a Ztest function to test an hypothesis about the population mean assuming a normal distribution. 

Lecture 11  03:24  
In this video, we show that the function CONFIDENCE.NORM computes the margin of error for a large sample size confidence interval about the population mean assuming a normal distribution. 

Section 3: Functions related to the Student t distribution  
Lecture 12  02:54  
Finding the left tailed Pvalue for testing an hypothesis about a population mean using Student T.DIST function 

Lecture 13  02:19  
In this lesson, we show how to find a critical Student t value corresponding to a left tailed test of hypothesis about the population mean 

Lecture 14  02:26  
In this video, we show how to find the critical value corresponding to a two tailed test using Student t test 

Lecture 15  03:17  
In this video, we show how to find a righttailed Pvalue for a Student t test. 

Lecture 16  02:11  
Finding a twotailed Pvalue when testing an hypothesis about the population mean using Student t distribution  
Lecture 17  05:36  
In this video, we show How to create a Student t table using EXCEL Student t function 

Lecture 18  03:33  
In this lesson, we show how to test an hypothesis about two populations means using Student t test. We provide detailed explanation for the test. 

Lecture 19  04:57  
In this video, we are calculating the Margin of Error corresponding to a small sample size confidence interval for the population mean using Student t distribution. 

Section 4: Computing Chisquare distribution probabilities and critical values  
Lecture 20  03:09  
In this lesson, we show how to find the left tailed Pvalue of a chi square distribution. This is important when we are testing an hypothesis with a left tailed test. 

Lecture 21  02:30  
In this lesson, we show how to find the right tailed Pvalue of a chi square distribution which is used to decide about statistical hypotheses. 

Lecture 22  03:16  
In this video, we are finding the critical tables values corresponding to a chi square left tailed test 

Lecture 23  02:27  
In this lesson, we show how to find the critical tables values corresponding to a chi square right tailed test 

Lecture 24  05:17  
In this lesson, we show how to conducte a test of chi square test of independence using the chi square test 

Section 5: Computing FDistribution probabilities  
Lecture 25  03:06  
In this lesson, we show how to find the left tailed Pvalue with F.DIST function 

Lecture 26  03:17  
In this video, we show how to find the right tailed Pvalue of the F distribution 

Lecture 27  04:08  
In this lesson, we show how to find the right tailed critical value of an F distribution 

Lecture 28  01:45  
In this lesson, we show how to test that two populations variances are equal with the FTEST function. This step is necessary for those who want to test the equality of two populations means. 

Section 6: Other advanced Statistical functions in EXCEL 2010  
Lecture 29  02:04  
In this lesson, we show how to use the exponential distribution in practice. The exponential distribution is used to model waiting time probabilities.. Example: How long will we have to wait until a customer arrives at a toll booth?How long will machinery work without needing repair? 

Lecture 30  05:25  
In this video, we show how to fit the exponential growth model to data that appears to have an expontential growth and then make future forecasts. 

Lecture 31  02:30  
We show in this video how to use the Gamma distribution, another waiting time model to compute probabilities. It is used in marketing, insurance risk, economics, meteorology, etc.. 

Lecture 32  03:49  
In this video we show how to use the GammaLN function to compute factorials of numbers 

Lecture 33  03:34  
In this video, we show how to compute probabilities about the negative binomial for solving a real world problem. It returns the probability that there will be a given number of failures before the rth success. The geometric distribution is a special case of of the Negative Binomial. It is defined as the num.ber of trials (failures) until the 1st success 

Lecture 34  02:26  
In this video, we show how to use the lognormal distribution, which is a distribution where the ln(x) is normally distributed. It is sometimes called the Cobb–Douglas function in business. 

Lecture 35  03:08  
In this video, we show how to compute the inverse of the log normal distribution which is a distribution where the ln(x) is normally distributed. It is sometimes called the Cobb–Douglas function in business. 

Lecture 36  02:19  
In this video, we show how to compute probabilities of the Weibull distribution. It is extensively used in the field of reliability engineering for measuring component lifetime. 

Section 7: Advanced Statistical functions in EXCEL 2013  
Lecture 37  02:20  
In the lecture we talk about the new advanced statistical functions introduced in EXCEL 2013 

Lecture 38  02:18  
This lecture explains the use of the newly introduced function Gamma function in EXCEL 2013 to evaluate the gamma of the numbers that are greater or equal to one. 

Lecture 39  04:13  
In this lecture, we illustrate how to use the Gauss function to evaluate standard normal distribution probabilities. 

Lecture 40  03:59  
This lecture explains how to use PHI function in EXCEL 2013 to evaluate the density of the standard normal distribution. 

Section 8: Practice project files and Quizzes  
Lecture 41  8 pages  
Exercises with solutions to reinforce the understanding of the concepts  
Quiz 1 
Solving problems using the normal distribution (Part1)

2 questions  
Quiz 2 
Find probabilities under the standard normal distribution

2 questions  
Quiz 3 
Finding the left tailed Pvalue for a Student t distribution

1 question  
Quiz 4 
Finding the righttailed Pvalue for a Student t distribution

1 question  
Quiz 5 
Finding the two tailed Pvalue for a Student t distribution

1 question  
Lecture 42  3 pages  
Finding the Z values of the standard normal distribution using (alpha over 2)  
Lecture 43  3 pages  
Finding the Student t values used in confidence interval for a given alpha and sample size n  
Section 9: Closing remarks and follow up  
Lecture 44  02:19  
Thank you and closing remarks 
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 handson experience with Statistics.