Foundation of Statistics with Minitab

Foundation of Statistics with Minitab, available at $59.99, has an average rating of 4.05, with 130 lectures, based on 295 reviews, and has 1960 subscribers.

You will learn about 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. This course is ideal for individuals who are The course is ideal for two groups of audiences. or – 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. or – 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. It is particularly useful for The course is ideal for two groups of audiences. or – 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. or – 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.

Enroll now: Foundation of Statistics with Minitab

Summary

Title: Foundation of Statistics with Minitab

Price: $59.99

Average Rating: 4.05

Number of Lectures: 130

Number of Published Lectures: 130

Number of Curriculum Items: 130

Number of Published Curriculum Objects: 130

Original Price: $79.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

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.

Target Audiences

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.

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.
Correlation, Regression.

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.
Randomized design.

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.

Course Curriculum

Chapter 1: Introduction to the Course

Lecture 1: Introduction and Data Files to Download

Chapter 2: Introduction to Statistics, Data and Statistical Thinking

Lecture 1: Statistics, Data and Statistical Thinking

Chapter 3: Managing Data in a Minitab Worksheet

Lecture 1: Getting Started with Minitab

Lecture 2: Summarizing Cases, Row Statistics

Lecture 3: Summarizing Columns, Using Session Commands

Lecture 4: Coding Data

Lecture 5: Ranking and Sorting of Data

Lecture 6: Standardizing Data

Lecture 7: Creating Subsets of Worksheet

Lecture 8: Combining Data using the Stack Option

Lecture 9: Separating Data using the Unstack Option

Chapter 4: Descriptive Statistics – Data Analysis For One Variable – Qualitative Data

Lecture 1: Describing Qualitative Data

Lecture 2: Numerically Summarizing Qualitative Variables

Lecture 3: Creating Bar Charts

Lecture 4: Creating Pie Charts

Lecture 5: Test Yourself – Questions

Lecture 6: Test Yourself – Answers

Chapter 5: Descriptive Statistics – Data Analysis For One Variable – Quantitative Data

Lecture 1: Numerical Measures of Central Tendency

Lecture 2: Numerical Measures of Variability

Lecture 3: Using the Mean and Standard Deviation to Describe Data

Lecture 4: Numerical Measures of Relative Standing

Lecture 5: Numerically Summarizing Quantitative Variables

Lecture 6: Graphical Methods for Describing Quantitative Data

Lecture 7: Creating Histograms

Lecture 8: Creating Stem-and-Leaf Displays

Lecture 9: Creating Dotplots and Individual Value Plots

Lecture 10: Methods for Detecting Outliers: Box Plots and z-scores

Lecture 11: Creating Boxplots

Lecture 12: Distorting the Truth with Descriptive Techniques

Lecture 13: Test Yourself – Questions

Lecture 14: Test Yourself – Answers

Chapter 6: Descriptive Statistics – Data Analysis for Comparing Groups

Lecture 1: Contingency Tables

Lecture 2: Cluster and Stack Bar Charts

Lecture 3: Comparing Subgroups by Dotplots and Individual Value Plots or Boxplots

Lecture 4: Comparing Subgroups Numerically

Lecture 5: Using Charts to Display Descriptive Statistics

Chapter 7: Descriptive Statisitcs – Relationships Between Two Quantitative Variables

Lecture 1: Bivariate Relationships

Lecture 2: Creating Scatterplots

Lecture 3: Adding a Grouping Variable to a Scatterplot

Lecture 4: Creating Marginal Plots

Lecture 5: Computing Covariance and Correlation

Lecture 6: Computing and Displaying the Regression Line

Lecture 7: Test Yourself – Questions

Lecture 8: Test Yourself – Answers

Chapter 8: Probability

Lecture 1: Events, Probability, and Sample Spaces

Lecture 2: Unions and Intersections

Lecture 3: Complementary Events

Lecture 4: Conditional Probability

Lecture 5: The Additive Rule and Mutually Exclusive Events

Lecture 6: The Multiplicative Rule and Independent Events

Lecture 7: Bayes's Rule

Lecture 8: Test Yourself – Questions

Lecture 9: Test Yourself – Answers

Chapter 9: Random Variables and Probability Distributions

Lecture 1: Two Types of Random Variables

Lecture 2: Probability Distributions for Discrete Random Variables

Lecture 3: The Binomial Distributions

Lecture 4: Other Discrete Distributions: Geometric, Poisson and Hypergeometric

Lecture 5: Calculating Individual and Cumulative Probability for the Binomial Distribution

Lecture 6: Calculating Individual Cumulative and Inverse Cumulative Poisson Probability

Lecture 7: Probability Distributions for Continuous Random Variables

Lecture 8: The Normal Distribution

Lecture 9: Descriptive Methods for Assessing Normality

Lecture 10: Other Continuous Distributions: Uniform and Exponential

Lecture 11: Calculating Porbablities Using Distribution Plots

Lecture 12: Calculating Critical Values of Random Variables using Distribution Plots

Lecture 13: Test Yourself – Questions

Lecture 14: Test Yourself – Answers

Chapter 10: Random Data – Sampling Distributions

Lecture 1: The Concept of Sampling Distributions

Lecture 2: Properties of Sampling Distributions: Unbiasedness and Minimum Variance

Lecture 3: Generating Random Data From Normal Distribution and Checking Normality

Lecture 4: Sampling from a Column

Lecture 5: Simulating Sampling with More Samples, Sampling Error

Lecture 6: Simulation Technique to Study Distributions of Sample Statistics

Lecture 7: The Sampling Distribution of a Sample Mean and the Central Limit Theorem

Lecture 8: Distribution of the Sample Mean. Known and Unknown Variance.

Lecture 9: The Sampling Distribution of the Sample Proportion and Sample Variance

Lecture 10: Distribution of the Sample Proportions. Large and Small Sample.

Lecture 11: Distribution of Sample Variances. Normal Population. Large and Small Sample.

Lecture 12: Test Yourself – Questions

Lecture 13: Test Yourself – Answers

Chapter 11: Inferences From One Sample – Estimation with Confidence Intervals

Lecture 1: Identifying and Estimating the Target Parameter

Lecture 2: Confidence Interval for a Population Mean: Normal (z) Statistic

Lecture 3: Confidence Interval for a Population Mean: Student’s t-Statistic

Lecture 4: Large-Sample Confidence Interval for a Population Proportion

Lecture 5: Confidence Interval for a Population Variance

Lecture 6: Finite Population Correction for Simple Random Sample

Lecture 7: Simulation of Confidence Intervals for the Mean of a Normal Population

Lecture 8: Simulation of Confidence Intervals for the Mean. Unknown Variance

Lecture 9: Determining the Sample Size

Instructors

László Bognár
Professor of Applied Statistics

Rating Distribution

1 stars: 10 votes
2 stars: 9 votes
3 stars: 34 votes
4 stars: 107 votes
5 stars: 135 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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