Language: English | Size: 2.26 GB | Duration: 18h 52m
Introducion to Descriptive and Inferential Statistics using the Minitab software
What you’ll learn
Use wide palette of Descriptive Statistics tools to visualize the structure of your dataset. Get the skills of choosing the appropriate graphical technic or the numerical descriptive measures to explore the tendencies or phenomena hidden in your data.
Understand the role and the objectives of Inferential Statistics when you have only a smaller or larger sample of data and your aim is to infer about the whole population of data related to different business tendencies, production quality questions or even scientific phenomena to be explored.
Get the skill of analyzing data with the Minitab software and get the master of one, two or multiple samples estimation problems and hypothesis tests. Use the Analysis of Variance (ANOVA) method to wide range of real life situations.
Learn how to interpret the outputs of a software driven data analysis.
Learn the way of using a statistical software not only for analyzing data but for making rather complex statistical concepts clear.
Get ready to go further and take the course of “Statistical Methods for Quality Improvement” about Statistical Process Control, Analysis of Experiments and Capability Analysis, which are the core chapters of Six Sigma Statistics applied worldwide in manufacturing and service sectors.
Download and install Minitab. Version 17.1 is used in the video lectures but earlier or later versions can also be used since little changes have been made in the way of manipulating data.
Download the dataset used throughout the course. The dataset downloadable from the “Lecture 1. Introduction and Data Files”.
It is good to have a text book of Statistics just in case if you want to get deeper insight into a specific method, however throughout the course comprehensive Lecture Notes serve as a good summary to each topic.
In this course the Lecture Notes related to the excellent textbook “Statistics for Business and Economics by McClave, Benson and Sincich, Ed.12 Pearson 2014” are used and you as an enrolled student can download these slides of Lecture Notes.
Optionally you may use the free online stat book: Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University.
Start to learn Statistics in a way where the use of a statistical software is in the center. Data analysis sessions are used to initiate you not only into solving problems with a software but also making the concepts of Statistics clear with using the capabilities of a high performance statistical software package in visualizing the hidden structures and tendencies in your datasets.
Get the skills of visualizing your data structure with the most appropriate tools of Descriptive Statistics.
Learn from animated video lessons about the process of manipulating data, visualizing the central tendencies, the spread of your data or the relationships between variables.
Graphical methods for summarizing qualitative and quantitative data.
Dot plots, Individual value plot, Box-plots, Stem-and-leaf plots, Histograms.
Numerical descriptive statistics for quantitative variables.
Mean, Median, Mode.
Graphical and numerical methods for investigating relationships between variables.
Simulate random data, calculate probabilities, and construct graphs of different distributions.
Discrete distributions: Binomial, Hypergeometric, Poisson etc.
Continuous distributions: Normal, Exponential, Student-t, Chi square etc.
Learn how to generate random data to simulate repeated sampling to study different sample statistics.
Large and small sample cases with known or unknown variances.
Simulation of confidence intervals for population mean or population proportions.
Get the skills of conducting hypothesis tests and constructing confidence intervals.
One-, two- and multiple sample situations.
Tests for population means, population proportions, or population variances.
Checking the validity of the assumptions.
Z-tests, t-tests, ANOVA.
This course is comprehensive and covers the introductory chapters of both the Descriptive and Inferential Statistics.
48 video lectures.
5 hours video.
Lecture Notes with 745 slides (not downloadable)
Test Yourself Questions and Answers with 79 slides.
Enjoy the benefit of the well-structured, short and yet comprehensive video lectures.
In these lectures all things happen inside a software driven analysis.
All in one place, within the same video lesson, gaining computer skills, getting theoretical background, and mainly getting the ability to interpret the outputs properly.
These lessons are specially prepared with intensive screen animations, concise and yet comprehensive, well-structured explanations. If you like you can turn on subtitles to support the comprehension.
The verification of the assumptions for a test, the basic theoretical background or even the formulas applied in a procedure appear in these video tutorials at the right instances of the analysis. The outputs are explained in a detailed manner in such an order that enables you to make the appropriate conclusions.
Learn in a way when you watch the video and do the same simultaneously in your own Minitab.
Watching a video, pausing it and doing the same steps simultaneously in your own Minitab is the best way of getting experience and practice in data manipulation. Repeating the sessions with different sample data develops your skill to solve statistical problems with a software.
Who this course is for:
The course is ideal for two groups of audiences.
– For undergraduate or graduate students who have been studying Statistics at their universities and need help in understanding the concepts of statistics and in applying the different methods solving problems either by hands or by a software.
– For those who use statistical methods in their jobs and need a short but yet comprehensive guide for a specific chapter of Statistics and use some of these video lectures as a quick reference guide how to do the analysis or how to interpret the printouts of a software driven statistical analysis.