Assignment #1: Problems 6.1.2, 6.1.4 , 6.1.7 , 6.2.4 (paper & calculator), 6.2.11 (MINITAB or Excel), 6.3.3 (paper & calculator), 6.3.8 (MINITAB). Note: data sets are available from CD included with textbook in both Excel (xls) and Minitab (MTW) formats. They can be downloaded from here also:

D.S.6.1.2 - Television Set Quality (Excel MTB)

D.S 6.1.4 - Restaurant Service Times (Excel MTB)
D.S. 6.1.7 - Paving Slab Weights (Excel MTB)
D.S. 6.2.4 - Physical Course Training Completion Times (Excel MTB)

Supplemental Video Clips (.avi files) are available to explain many of the topics in IME 301. You can download the video clips below. Note that you also need to download and install the TSCC.exc codec in order to view the video clips.

TSCC.exe - Codec must be installed to run video clips. Video clips are .avi files
Introduction to Sampling - Relationship between a population and a sample (ppt file)
Histogram Construction using Excel - How to make histograms using Excel
Sturges Rule - Choosing the optimal number of bins for a histogram
Boxplots - How to construct a boxplot if you know the quartiles (ppt file)
Binwidth Demo - An applet is used to show the impact of changing bin width on a histogram

Optional: Relevant video clips from Introduction to Business Statistics by Weiers
Basic Statistics - mean, median, mode, variance, standard deviation, quartiles (ppt file)
Descriptive Statistics 1 - data arrays, frequency distributions, historgrams, Sturges Rule (ppt file)
Descriptive Statistics 2 - scatter diagrams (ppt file)
Descriptive Statistics 3 - example showing construction of various descriptive statistics (ppt file)

Chapter 6 Reading Notes:

It is very important that you read chapter 6. It is well written and helps you establish a good foundation for the course and the data collection project.

6.1 – Experimentation

It is important to understand the difference between a population and a sample. A sample is a set of data collected from a population that we can use in different ways.

Inferential Statistics - When we use a sample to make inferences about the underlying population, that is called “inferential statistics.”

Descriptive Statistics – When we use sample statistics (e.g., the sample mean and sample standard deviation), or charts and graphs (e.g., histograms or box plots) to describe our sample data, they are called “descriptive statistics.”

Supplemental Video Clip: Introduction to Sampling

6.2 – Data Presentation

In my experience, histograms, bar charts (and a very useful version called the Pareto Chart), and box plots (6.3.7) are the work horses for data presentation. Pie charts are not as useful as they seem because are more difficult to read than a bar chart, especially in a PowerPoint presentation. Note that Pareto Charts are widely used in the world of quality and “continuous improvement.”

Histograms – Making histograms is an art. I have supplemented the course here. See the supplemental video clips for making histograms and using Sturges’ Rule to determine the number of intervals. Note that making histograms using Excel is not easy.

Supplemental Video Clips: Histograms Using Excel, Sturges Rule Video Clip

Box Plots – Probably one of the best ways to represent data graphically. See the supplemental video clip which explains how to make a proper box plot.

Supplemental Video Clips: Boxplots

6.3 – Sample Statistics

Sample Mean – Most widely used sample statistic and is called x-bar ( ).

Sample Median – Very widely used to describe the center of a population. Is not sensitive to the impact of extreme values

Sample Trimmed Mean – Not widely used, but is usually between the mean and median. You do see in athletics where they discard the high and low judge’s scores and average the rest.

Sample Mode – Another type of average, but not widely used.
Sample Variance – The square root of the variance is called the Standard Deviation. These two sample statistics are the workhorse statistics for describing variation. You must know how to calculate these sample statistics!!

Sample Quantiles (particularly quartiles) – Note the strange terminology in this section. Get familiar with it. Quartiles are a type of quantile where each quartile represents one-quarter (25%) of the data. The books explanation of how to calculate quartiles is not standard and I do not teach this method. Instead use the method taught in class. We need quartiles to develop box plots. Learn the definition of “inter-quartile range.” Note that software programs vary in the algorithms used to define quartiles.

Box Plots – Really a graph, but it uses the quartiles for construction. Mentioned earlier.

Supplemental Video Clip: Boxplots

Coefficient of Variation – Not widely used by statisticians because it has no real meaning. Used in a few places, but not popular.