Foundation of Statistics with Minitab

Introducion to Descriptive and Inferential Statistics using the Minitab software
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  • Lectures 130
  • Contents Video: 5 hours
    Other: 13.5 hours
  • Skill Level All Levels
  • Languages English, captions
  • Includes Lifetime access
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About This Course

Published 5/2015 English Closed captions available

Course Description

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
  • Test Yourself Questions and Answeres 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.

What are the requirements?

  • 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.

What am I going to get from this course?

  • 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.

What is the target audience?

  • 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.

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Introduction to the Course
Introduction and Data Files to Download
Preview
03:55
Section 2: Introduction to Statistics, Data and Statistical Thinking
Statistics, Data and Statistical Thinking
40 pages
Section 3: Managing Data in a Minitab Worksheet
Getting Started with Minitab
04:44
Summarizing Cases, Row Statistics
06:48
Summarizing Columns, Using Session Commands
05:47
Coding Data
02:38
Ranking and Sorting of Data
04:06
Standardizing Data
02:55
Creating Subsets of Worksheet
02:03
Combining Data using the Stack Option
03:19
Separating Data using the Unstack Option
02:40
Section 4: Descriptive Statistics - Data Analysis For One Variable - Qualitative Data
Describing Qualitative Data
Preview
11 pages
03:51

From the lecture:

"In this tutorial we will begin the process of analyzing data by learning how Minitab can be used to explore and summarize data for a single variable, numerically.

First, we deal with qualitative variables. In this demonstration we use the Infants worksheet where the data are part of a research where we have been conducting a study of the factors that appeared to be associated with a new mother's decision to breastfeed her infant or not. 68 low income pregnant women who attended a clinic affiliated with a group are the subjects.

Our task is to summarize the data collected on these women and their new born children. "

03:39

From the lecture:

"If we want to graphically represent the percentage associated with the category, we have two ways to do this: the bar chart and the pie chart. Now, let's begin with the bar chart. "

Creating Pie Charts
Preview
02:00
Test Yourself - Questions
7 pages
Test Yourself - Answers
4 pages
Section 5: Descriptive Statistics - Data Analysis For One Variable - Quantitative Data
Numerical Measures of Central Tendency
Preview
17 pages
Numerical Measures of Variability
Preview
18 pages
Using the Mean and Standard Deviation to Describe Data
Preview
9 pages
Numerical Measures of Relative Standing
Preview
16 pages
02:14

From the lecture:

"Now we will use Minitab to find numerical summaries for quantitative variables.

We are going to start by looking at descriptive statistics for the time the pregnant women spent with a nutritionist before their childbirth, before their delivery."

Graphical Methods for Describing Quantitative Data
Preview
13 pages
05:27

From the lecture:

"Minitab offers a number of graphs designed to display quantitative data.

In this section we will examine histograms."


05:39

From the lecture:

"Stem-and-Leaf Display of quantitative data enables us to see the actual data while retaining much of the same features of a histogram.

It is an example of a character graph. The numbers in the centre column represents the stems or left most digits of the data values. The column on the right contains the leaves because Minitab records a leaf unit of one. Each leaf represents the one's digit of a data value, and each stem represents the ten's digit of the data value. "

03:55

From the lecture:

"Now we will look at a dot plot and an individual value representation of data. "

" The scattering of the points allows for each point on the display to represent exact values. "

Methods for Detecting Outliers: Box Plots and z-scores
Preview
12 pages
04:23

From the lecture:

"A boxplot provides us with rather skeletal view of our data set. "

"A boxplot uses five numbers to describe a set of data. The maximum value, the 3rd Quartile, the Median, the 1st Quartile and the minimum value. Collectively these five numbers are known of the 5-number summary of the data set.

Minitab constructs a rectangle, a box between the 1st and the 3rd Quartiles and displays a horizontal line at the location of the Median. This box encloses the middle half of the data. The Whiskers that extend either direction indicate the non-outlying data. If there no outlier values, the whiskers extend to the smallest and the largest values in the data set."

Distorting the Truth with Descriptive Techniques
Preview
9 pages
Test Yourself - Questions
Preview
10 pages
Test Yourself - Answers
Preview
3 pages
Section 6: Descriptive Statistics - Data Analysis for Comparing Groups
04:30

From the lecture:

"In this tutorial we will construct tables to compare groups based upon two qualitative variables.

We will examine the smoking status of 68 pregnant women who participated in a clinical study. Their data are recorded in the Infants data file. First, investigate the relationship between smoking status and ethnicity because both of these variables are qualitative. We explore the relationship between them by obtaining a two-way table of counts called a Contingency Table. Sometimes this table is called a Cross Tabulation Table. "

03:31

From the lecture:

"Here we will construct bar charts to represent relationship between two categorical variables. We will use one of the variables as a so-called cluster or grouping variable.

We will use the file Infants, and we will construct two bar charts between the variables Smoke and Ethnic, and we define Ethnic as a Cluster Variable."

03:29

From the lecture:

"Minitab allows us to compare subgroups of quantitative variables by showing different graphs separately for each subgroup.

As an illustration we will use the data in the BallPark data worksheet. Here, in this worksheet the data relate to the 30 Major League baseball teams."

04:32

From the lecture:

"We can get summary statistics for several quantitative variables simultaneously and in this way we can compare subgroups using numerical descriptive measures.

Now, we use the BallPark data again."

02:17

From the lecture:

"The Bar Chart command can be used to produce many kinds of displays of Summary Statistics.

To explore this capability, we use the BallPark data worksheet. Here, the data relate to the 30 Major League baseball teams."

Section 7: Descriptive Statisitcs - Relationships Between Two Quantitative Variables
Bivariate Relationships
16 pages
06:02

From the lecture:

"Generally, the best way to begin an exploration of the relationship between the quantitative variables is to construct a co-called scatterplot. This is a two-dimensional graph in which each value is represented by a single dot."

Adding a Grouping Variable to a Scatterplot
03:03
02:02

From the lecture:

"A marginal plot combines the features of a scatterplot with some of the one variable graphs. It means that we can examine the relationship between two variables while also viewing the distribution of each variable, all on the same graph."

04:30

From the lecture:

"The covariance and the correlation coefficient are numerical measures of the strength of the linear relationship between two quantitative variables. "

"A positive covariance suggests that high values for one variable tend to be associated with high values for the other. However, because the value for the covariance depends on the units associated with the two variables, it is difficult to determine the exact strength of the relationship from this value.

Pearson's Correlation Coefficient or simply the Correlation Coefficient measures the strength of the linear relationship between two quantitative variables in a way that it does not depend on the units of the two variables. It is usually designated by small or lower case "r", and always lies between -1 and +1. In fact, "r" is the covariance divided the product of the standard deviations of the variables. "

04:55

From the lecture:

"While Correlation Coefficient measures the strength of the linear relationship between two quantitative variables, the Regression Line or Least-Squares Line summarizes the form of this relationship."

Test Yourself - Questions
3 pages
Test Yourself - Answers
2 pages
Section 8: Probability
Events, Probability, and Sample Spaces
17 pages
Unions and Intersections
12 pages
Complementary Events
4 pages
Conditional Probability
6 pages
The Additive Rule and Mutually Exclusive Events
7 pages
The Multiplicative Rule and Independent Events
7 pages
Bayes's Rule
5 pages
Test Yourself - Questions
7 pages
Test Yourself - Answers
2 pages
Section 9: Random Variables and Probability Distributions
Two Types of Random Variables
13 pages
Probability Distributions for Discrete Random Variables
10 pages
The Binomial Distributions
9 pages
Other Discrete Distributions: Geometric, Poisson and Hypergeometric
23 pages
05:46

From trhe lecture:

"Binomial Distribution comes up when we repeat the so-called Trial more times in succession.

A Trial is the most basic type of a Random Experiment when the experiment has only two outcomes, usually called Success and Failure, and while repeating this trial more times, the probability of getting success or failure remains unchanged.

So, Binomial Distribution is specified by two parameters, "n", small "n", the number of trials and "p", small "p", the probability of success on each trial. The Number of Success can be 0, 1, 2, and so on, up to "n".

As an example, find out the probability of getting 3 successes when p is 1/6 and n is equal to 10. "

04:30

From the lecture:

"Poission Distribution arises when we count the number of Occurrences of an Event relatively infrequently. This distribution is completely specified by just one parameter by the Mean of the number of occurrences.

For example, if we know that in a city there are, on average, 6 accidents per weekend, then we can calculate the probability there will be, say, 5, 10 or 20 accidents next week, or no accidents at all. "

Probability Distributions for Continuous Random Variables
2 pages
The Normal Distribution
41 pages
Descriptive Methods for Assessing Normality
6 pages
Other Continuous Distributions: Uniform and Exponential
11 pages
04:47

From the lecture:

"In this section we will use some of the Minitab's capabilities related to calculating and graphing, plotting probabilities of random events. let's assume, for example, that the heights of people in a group has normal distribution with mean µ is equal 170 centimeters, and with standard deviation σ is equal 10 centimeters.

Now, calculate probabilities of events related to the random experiment when we select 1 person randomly from this group. "

05:58

From the lecture:

"In inferential statistics we often need to determine certain values of a random variable called as critical values which refer to a predefined probability. The chance to get a larger or alternatively smaller value as an outcome of the experiment than the critical value is equal to this predefined probability.

Let's assume, for example, that the heights of people in a group is normally distributed with Mean 170 centimeters and with Standard Deviation, σ, is equal 10 centimeters. Now, let's determine that distinct value of heights called Right Tail Critical Value, for which it's true that the probability of randomly selecting one such person from this group, who is taller than this value, is equal, let's say, 10%. "

Test Yourself - Questions
4 pages
Test Yourself - Answers
1 page
Section 10: Random Data - Sampling Distributions
The Concept of Sampling Distributions
15 pages
Properties of Sampling Distributions: Unbiasedness and Minimum Variance
6 pages
04:50

From the lecture:

"The family of normal distributions, sometimes we call them Bell-Shaped Curves, plays a central role in Statistics. In this section we will generate a sample from a Normal Distribution and check the Normality of this sample.

First, simulate the selection of a random sample of heights of people using the Mean Value, µ, which is equal to 170 centimeters, and Standard Deviation, σ, whics is equal to 10 centimeters."

Sampling from a Column
02:33
06:58

From the lecture:

" In this tutorial we will simulate the process of sampling when we take more than one sample from the same population at a time. Suppose, we want to simulate an experiment when we take a random sample of 20 men, and measure their systolic blood pressure.

Use 3 different Samples, and compare the data measured in the different samples. We know that for the population of males systolic blood pressure is approximately normally distributed with Mean 130 and Standard Deviation of 20 millimeters of Mercury. "

05:47

From the lecture:

"In this tutorial we will see a useful technique for Simulation Studies. This technique is good to study the variation of different Sample Statistics even in the case when the theoretical approach is quite complex."

The Sampling Distribution of a Sample Mean and the Central Limit Theorem
23 pages
Distribution of the Sample Mean. Known and Unknown Variance.
09:31
The Sampling Distribution of the Sample Proportion and Sample Variance
9 pages
Distribution of the Sample Proportions. Large and Small Sample.
07:38
Distribution of Sample Variances. Normal Population. Large and Small Sample.
04:23
Test Yourself - Questions
3 pages
Test Yourself - Answers
1 page
Section 11: Inferences From One Sample - Estimation with Confidence Intervals
Identifying and Estimating the Target Parameter
10 pages
Confidence Interval for a Population Mean: Normal (z) Statistic
14 pages
Confidence Interval for a Population Mean: Student’s t-Statistic
12 pages
Large-Sample Confidence Interval for a Population Proportion
8 pages
Confidence Interval for a Population Variance
5 pages
Finite Population Correction for Simple Random Sample
7 pages
Simulation of Confidence Intervals for the Mean of a Normal Population
10:03
Simulation of Confidence Intervals for the Mean. Unknown Variance
09:41
Determining the Sample Size
8 pages

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Instructor Biography

László Csanaki Bognár, Professor of Applied Statistics

Engineer and mathematician teaching different subjects of Engineering and Mathematics for more than 20 years at a college in Hungary. Beside teaching he is intensively involved in industrial projects as a consultant or as a structural designer. His special fields are Quality Statistics, Statistical Process Control, Multivariate Statistical Analysis and Stochastic Processes. He was elected to the President of the Chamber of Engineers in Fejer county and holds several awards. He served as the rector of the College of Dunaujvaros for 8 years.

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