However, such mean or median does not necessarily lie at the center of the histogram, as we will see in a few examples below. Remember, the center of the data is the average (mean) of the data or the median value of the data. Spread of the data is the variation between the lowest and highest value in the data. It also helps to understand the spread of the data. It helps in understanding where the center of the data is or where the data is more concentrated. Histograms are useful for Lean Six Sigma practitioners in understanding the shape, or distribution, of the data. More about continuous and discrete data type (opens in a new tab). Histograms are useful to analyse continuous data and not for discrete data. Thus, it is essentially a bar chart depicting the frequency of the values in your data. It rather plots the frequency, or number of time a particular data value is present in the data set, segregated into multiple ‘ intervals’ or ‘ bins’. However, this bar chart does not plot the data values from your data set. In simple words, it is a bar chart representing your complete data set.
#MINITAB HISTOGRAM HOW TO#
In this post, we will discuss various characteristics of a histogram, how to identify shape or distribution of your data set using histogram and how plot a histogram using Minitab as well as excel.
In section 12 we will discuss about measurement system analysis and how gage r and r studies are conducted in Minitab using theory and examples.įinally in section 13 we will talk about design of experiments including blocking and ceterpoints concepts using Minitab and examples.Histogram is a graphical analysis tool used to identify the shape of the data. Similarly in section 11 we will talk about correlation and regression concepts with theory and examples using Minitab. Next in In Section 10 one way and two way ANOVA concepts using hypothesis tests using F and P values both theory and case studies using Minitab. Next In section 8 and section 9 we have Process Capability where we will look at process capability indices like cp, cpk, pp, ppk, sigma level and parts per million for both normal data and non-normal data both theory and case studies using minitab. In Section 7 we have introduction where we will look at Agenda of course.
In this section we will look at horizontal lines in control charts like mean, upper control limit, lower control limit, and we will look at the pre-requisites for a process to be a stable process. Next in section 6, we will look at statistical process control charts. Next in section 5, we will look at hypothesis testing, in this section we will look at concepts like confidence intervals, hypothesis testing, null hypothesis, alternate hypothesis, type I and type II errors and we will look at hypothesis tests like, 1 sample t-test, 2 sample t-test, 2 variance test, paired t test, 1 proportion test, 2 proportion test, chi-square test etc. In this section we will look at concepts like inferential statistics, what is meant by random sampling, sampling distribution, central limit theorem and t-distribution introduction concepts. Next in section 4, we will look at inferential statistics. In this section we will look at what is mean, median, range, variance and standard deviation with formulas, once theoretical concepts are understood then we will look at how to calculate descriptive statistics in Minitab.
Next in In Section 3 we will look at descriptive statistics. Next In section 2 we have graphical Analysis where we will look at different types of data types, different types of graphs with both theoretical concepts and how to plot those graphs using a hypothetical data sets. In Section 1 we have introduction where we will look at Agenda of course.