Explain the variation in ionization energies of carbon, as displayed in this graph:

Lecture 08/30/17 → Chapter 2: Graphs and Descriptive Statistics ← Listed Form: 3,4,7,9,10,10,10,14,15,17,17,19,20,20,20 Grouped (Frequency) Form: 3 | 1 4 | 1 7 | 1 9 | 1 10 | 3 14 | 1 15 | 1 17 | 2 19 | 1 20 | 3 ———— 15 Interval (Frequency) Form: Class: 3-9 10-16 17-23 LCL - UCL Lower Class Limit - Upper Class Limit LCB = LCL - tol/2 UCB = UCL + tol/2 Class Width: Length + 1 of the class so 7-7-7 Class Midpoint = (UCL + LCL)/2 9+3 = 12/2 = 6 10 + 16 = 26/2 = 13 17 + 23 = 40/2 = 20 Frequency: 4 5 6 —— 15 Sigma = Sum Ef = 15 = n (Sample Size) Tolerance = LCL of next class - UCL of previous class. Relative frequency = F/total f Symmetric = Bell-shaped Left-Shewed = left bell Right-Shewed = right bell Outliers = data that don’t act like the rest of the data, or not part of it. Histogram Frequency Polygon: Frequency Midpoint Section 2.1 EDA Exploratory Data Analysis (EDA) is a critical first step in analyzing the data from an experiment. Mostly a graphical approach. The four types of EDA are: o Univariate non-graphical o Multivariate non-graphical o Univariate graphical, and o Multivariate graphical. The main reasons we use EDA are: o To get an in depth insight of the data set. o To understand the behavior of the data. o To detect the mistakes/outliers that exists in the dataset. o To check the assumptions. o To extract important attributing variables. o To