0:03 hi everyone welcome back in this section
0:05 we will study further data
0:07 and this time what we call measurement
0:09 scales there are different data types
0:10 that are available
0:12 in our practice of lean six sigma data analysis
0:14 analysis
0:17 so let's look on the data types on the
0:17 left side
0:20 we can see categorical data and on the
0:23 right side we can see numerical data
0:26 under categorical data we still have nominal
0:26 nominal
0:30 and ordinal and under numerical data
0:32 we have interval and ratio when we say
0:34 categorical data
0:36 these are data which came from categories
0:37 categories
0:40 if we can categorize them therefore we
0:41 can count them
0:44 however numerical data these are data
0:46 coming from measurements
0:48 continuous measurements from continuum scale
0:49 scale
0:51 it's important for us to know the data
0:54 type because it will dictate on what
0:57 tools are we using for the analyze phase later
0:58 later
1:00 on so we will be discussing nominal ordinal
1:01 ordinal
1:04 interval and ratio measurement scales
1:05 moving forward
1:08 what is a nominal data nominal values
1:10 represent discrete units
1:12 and are used to label variables that
1:14 have no quantitative value
1:17 and order does not matter in short they
1:18 are just
1:21 labels to our data example what is your gender
1:22 gender
1:25 answers would be male or female you have
1:26 been categorized
1:29 as a male or female in this particular example
1:30 example
1:32 but there is no quantitative value to
1:34 each option
1:37 next what languages do you speak is it
1:40 english french german or spanish
1:44 so you are giving a label but you're not
1:46 giving a quantitative value
1:48 but afterwards what you can do is you
1:49 can count
1:52 how many are females how many are males
1:55 or how many are speaking english french
1:58 german and spanish so this is
2:01 nominal data next we have
2:04 ordinal data ordinal values represent discrete
2:05 discrete
2:07 and ordered units it is therefore nearly
2:09 the same as nominal data
2:12 except that its ordering matters in this case
2:13 case
2:17 order matters for example first second
2:20 third fourth and fifth also we have a
2:23 particular example coming from a survey
2:25 asking what is your educational
2:27 background is it elementary
2:31 high school undergraduate or graduate
2:34 we know that elementary to graduate can
2:36 also be considered as nominal but because
2:36 because
2:39 chronological order is important
2:40 therefore we assign
2:43 1 2 3 and 4 to designate the order matters
2:44 matters
2:48 so this is an example of ordinal data
2:52 next is interval data interval values
2:54 represent ordered units that have the
2:55 same difference
2:59 for example temperature in interval data
3:03 there is an absolute value of zero ratio data
3:04 data
3:07 ratio values are also ordered units that
3:09 have the same difference
3:11 ratio values are the same as interval
3:13 values with the difference that they have
3:13 have
3:17 and absolute zero again we have several
3:18 data types
3:20 that are being considered in our lean
3:22 six sigma data analytics
3:25 we have categorical and numerical
3:28 under categorical false nominal and ordinal
3:29 ordinal
3:32 and under numerical we have interval and ratio
3:32 ratio
3:35 in the next section you will be asked to
3:37 answer a practice quiz
3:39 so that we can test if you really
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