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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.
Simulate random data, calculate probabilities, and construct graphs of different distributions.
Learn how to generate random data to simulate repeated sampling to study different sample statistics.
Get the skills of conducting hypothesis tests and constructing confidence intervals.
This course is comprehensive and covers the introductory chapters of both the Descriptive and Inferential Statistics.
Enjoy the benefit of the wellstructured, 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, wellstructured 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.
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Section 1: Introduction to the Course  

Lecture 1 
Introduction and Data Files to Download
Preview

03:55  
Section 2: Introduction to Statistics, Data and Statistical Thinking  
Lecture 2 
Statistics, Data and Statistical Thinking

40 pages  
Section 3: Managing Data in a Minitab Worksheet  
Lecture 3 
Getting Started with Minitab

04:44  
Lecture 4 
Summarizing Cases, Row Statistics

06:48  
Lecture 5 
Summarizing Columns, Using Session Commands

05:47  
Lecture 6 
Coding Data

02:38  
Lecture 7 
Ranking and Sorting of Data

04:06  
Lecture 8 
Standardizing Data

02:55  
Lecture 9 
Creating Subsets of Worksheet

02:03  
Lecture 10 
Combining Data using the Stack Option

03:19  
Lecture 11 
Separating Data using the Unstack Option

02:40  
Section 4: Descriptive Statistics  Data Analysis For One Variable  Qualitative Data  
Lecture 12 
Describing Qualitative Data
Preview

11 pages  
Lecture 13  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. " 

Lecture 14  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. " 

Lecture 15 
Creating Pie Charts
Preview

02:00  
Lecture 16 
Test Yourself  Questions

7 pages  
Lecture 17 
Test Yourself  Answers

4 pages  
Section 5: Descriptive Statistics  Data Analysis For One Variable  Quantitative Data  
Lecture 18 
Numerical Measures of Central Tendency
Preview

17 pages  
Lecture 19 
Numerical Measures of Variability
Preview

18 pages  
Lecture 20 
Using the Mean and Standard Deviation to Describe Data
Preview

9 pages  
Lecture 21 
Numerical Measures of Relative Standing
Preview

16 pages  
Lecture 22  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." 

Lecture 23 
Graphical Methods for Describing Quantitative Data
Preview

13 pages  
Lecture 24  05:27  
From the lecture: "Minitab offers a number of graphs designed to display quantitative data. In this section we will examine histograms." 

Lecture 25  05:39  
From the lecture: "StemandLeaf 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. " 

Lecture 26  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. " 

Lecture 27 
Methods for Detecting Outliers: Box Plots and zscores
Preview

12 pages  
Lecture 28  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 5number 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 nonoutlying data. If there no outlier values, the whiskers extend to the smallest and the largest values in the data set." 

Lecture 29 
Distorting the Truth with Descriptive Techniques
Preview

9 pages  
Lecture 30 
Test Yourself  Questions
Preview

10 pages  
Lecture 31 
Test Yourself  Answers
Preview

3 pages  
Section 6: Descriptive Statistics  Data Analysis for Comparing Groups  
Lecture 32  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 twoway table of counts called a Contingency Table. Sometimes this table is called a Cross Tabulation Table. " 

Lecture 33  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 socalled 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." 

Lecture 34  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." 

Lecture 35  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." 

Lecture 36  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  
Lecture 37 
Bivariate Relationships

16 pages  
Lecture 38  06:02  
From the lecture: "Generally, the best way to begin an exploration of the relationship between the quantitative variables is to construct a cocalled scatterplot. This is a twodimensional graph in which each value is represented by a single dot." 

Lecture 39 
Adding a Grouping Variable to a Scatterplot

03:03  
Lecture 40  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." 

Lecture 41  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. " 

Lecture 42  04:55  
From the lecture: "While Correlation Coefficient measures the strength of the linear relationship between two quantitative variables, the Regression Line or LeastSquares Line summarizes the form of this relationship." 

Lecture 43 
Test Yourself  Questions

3 pages  
Lecture 44 
Test Yourself  Answers

2 pages  
Section 8: Probability  
Lecture 45 
Events, Probability, and Sample Spaces

17 pages  
Lecture 46 
Unions and Intersections

12 pages  
Lecture 47 
Complementary Events

4 pages  
Lecture 48 
Conditional Probability

6 pages  
Lecture 49 
The Additive Rule and Mutually Exclusive Events

7 pages  
Lecture 50 
The Multiplicative Rule and Independent Events

7 pages  
Lecture 51 
Bayes's Rule

5 pages  
Lecture 52 
Test Yourself  Questions

7 pages  
Lecture 53 
Test Yourself  Answers

2 pages  
Section 9: Random Variables and Probability Distributions  
Lecture 54 
Two Types of Random Variables

13 pages  
Lecture 55 
Probability Distributions for Discrete Random Variables

10 pages  
Lecture 56 
The Binomial Distributions

9 pages  
Lecture 57 
Other Discrete Distributions: Geometric, Poisson and Hypergeometric

23 pages  
Lecture 58  05:46  
From trhe lecture: "Binomial Distribution comes up when we repeat the socalled 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. " 

Lecture 59  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. " 

Lecture 60 
Probability Distributions for Continuous Random Variables

2 pages  
Lecture 61 
The Normal Distribution

41 pages  
Lecture 62 
Descriptive Methods for Assessing Normality

6 pages  
Lecture 63 
Other Continuous Distributions: Uniform and Exponential

11 pages  
Lecture 64  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. " 

Lecture 65  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%. " 

Lecture 66 
Test Yourself  Questions

4 pages  
Lecture 67 
Test Yourself  Answers

1 page  
Section 10: Random Data  Sampling Distributions  
Lecture 68 
The Concept of Sampling Distributions

15 pages  
Lecture 69 
Properties of Sampling Distributions: Unbiasedness and Minimum Variance

6 pages  
Lecture 70  04:50  
From the lecture: "The family of normal distributions, sometimes we call them BellShaped 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." 

Lecture 71 
Sampling from a Column

02:33  
Lecture 72  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. " 

Lecture 73  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." 

Lecture 74 
The Sampling Distribution of a Sample Mean and the Central Limit Theorem

23 pages  
Lecture 75 
Distribution of the Sample Mean. Known and Unknown Variance.

09:31  
Lecture 76 
The Sampling Distribution of the Sample Proportion and Sample Variance

9 pages  
Lecture 77 
Distribution of the Sample Proportions. Large and Small Sample.

07:38  
Lecture 78 
Distribution of Sample Variances. Normal Population. Large and Small Sample.

04:23  
Lecture 79 
Test Yourself  Questions

3 pages  
Lecture 80 
Test Yourself  Answers

1 page  
Section 11: Inferences From One Sample  Estimation with Confidence Intervals  
Lecture 81 
Identifying and Estimating the Target Parameter

10 pages  
Lecture 82 
Confidence Interval for a Population Mean: Normal (z) Statistic

14 pages  
Lecture 83 
Confidence Interval for a Population Mean: Student’s tStatistic

12 pages  
Lecture 84 
LargeSample Confidence Interval for a Population Proportion

8 pages  
Lecture 85 
Confidence Interval for a Population Variance

5 pages  
Lecture 86 
Finite Population Correction for Simple Random Sample

7 pages  
Lecture 87 
Simulation of Confidence Intervals for the Mean of a Normal Population

10:03  
Lecture 88 
Simulation of Confidence Intervals for the Mean. Unknown Variance

09:41  
Lecture 89 
Determining the Sample Size

8 pages 
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.