Summary

Summary of Statistics | Managerial Statistics, Keller | RSM premaster

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Course
Statistics (BPN1101)

Institution
Erasmus Universiteit Rotterdam (EUR)

Book
CUSTOM MANAGERIAL STATISTICS (PART 1)

This document summarizes Chapters 1 through 4 and 6 through 12 from Keller's Managerial Statistics book (2014). The summary contains all the theory you need to know before the Statistics exam. Statistics is part of the premaster, RSM Erasmus University. The summary is in English.

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Summarized whole book? No
Which chapters are summarized? Chapter 1 -4, 6-12
Uploaded on March 25, 2023
Number of pages 7
Written in 2022/2023
Type Summary

statistics
statistics
premaster
pre master
rsm
university
erasmus
dutch premaster

Book Title:CUSTOM MANAGERIAL STATISTICS (PART 1)

Author(s):Keller

Edition:Unknown
ISBN:9781408098707
Edition:1

Institution
Erasmus Universiteit Rotterdam (EUR)
Education
-
Course
Statistics (BPN1101)

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STATISTICS SUMMARY

Chapter 1 – What is statistics?
- Statistics = is a way to get information from data
- Descriptive Statistics: summarizing and presenting data in effective way.
- Inferential Statistics: drawing conclusions about population based on sample data
- Key statistical concepts
 Population = group of all items of interest to a statistics practitioner
 Parameter is the descriptive measure of the population
 Sample = set of data drawn from the studied population
 Statistic is the descriptive measure of sample
 Statistical inference = the process of making an estimate, prediction or decision about a
population based on sample data – two measures of reliability
 Confidence level: proportion of times that an estimating procedure will be correct
 Significance level: how frequently the conclusion will be wrong

Chapter 2 – Graphical Descriptive Techniques I
- Types of data and information
 Variable = characteristic of a population or sample
 Values = possible observations of the variable
 Data = observed values of the variable (datum is singular)
- Four types of data
 Ratio = highest level, absolute point of zero - quantitative
 All calculations allowed: = ≠ < > + - * /  often average calculated
 Interval = numbers such as heights, weights or incomes – quantitative & numerical
 Ordinal = categories where order of values have meaning – ranking = ≠ < >
 No specific graphical technique: bar charts and pie charts can be used
 Nominal = categories such as single, married or divorced – qualitative & categorical
 Can only count frequency or percentage of occurrence (relative frequency): = ≠
 Frequency distribution presented in bar chart or pie chart (proportions)
- Higher-level data may be treated as lower-level data but not the other way around

Chapter 3 – Graphical Descriptive Techniques II
- Histogram is used for interval data
 Observations that fall into a series of intervals are classes
 Intervals don’t overlap, every observation is assigned and the intervals are equally wide
- Number of classes is based on the number of observations: # = 1 + 3.3log(n)
- With of class = (largest observation – smallest observation) / number of classes
- Shapes of a histogram
 Symmetric: two sides identical in shape and size
 Skewness: long tail extending to right (positively skewed \) or left (negatively skewed /)
 Modal class: class with the largest number of observations
 Unimodal histogram: has only one peak – bimodal histogram: has two peaks
 Bell Shape: a special type of symmetric unimodal histogram
- Stam-and-leaf display is similar a display as histogram but with actual observations
- Relative frequency distribution is created by dividing frequencies by number of observations

,  Total sum is always 1.0/100%
 Cumulative relative frequency distribution highlights observations below class limits
 Ogive is graphical representation of cumulative relative frequencies

Chapter 4 – Numerical descriptive techniques
- Measures of central location – three different measures
 Mean: μ = the average - only for interval and ratio data (formula sheet)
 Median = middle observations when placing all in order
 Not as sensitive to extreme values as the mean
 Best for either very small or extreme number of observations (ordinal, ratio, interval)
 Mode = observation that occurs with the greatest frequency
 For populations and large samples report modal class
- Measures of variability – spread of variability (only for interval and ratio data)
 Range = largest observation – smallest observation
 Variance: σ² (population) and s² (sample) – (formula sheet)
 Standard deviation: σ and s = related measure
 Mean absolute deviation (MAD) is average absolute value
 Standard Deviation: σ = √σ² and s = √s²
- Empirical Rule can be used when histogram is bell shaped
 68% of all observations fall within one standard deviation of the mean
 95% of all observations fall within two standard deviations of the mean
 99.7% of all observations fall within three standard deviations of the mean
- Chebysheff’s Theorem applies to all shaped of histograms: 1-(1/k²) for k>1
- Coefficient of Variation: CV = σ / μ and cv = s / x

Chapter 6 – Probability
- Probability provides a link between population and sample
- Random experiment = action/process that leads to one of several possible outcomes
 Example: flip a coin – either head or tail of grade on test – A, B, C, D or F
 List of outcomes includes all possibilities, and no two outcomes can occur twice
 Sample space: S = list of all possible outcomes – exhaustive and mutually exclusive
- Two requirements of probabilities – given sample space S = {O 1, O2, …., Ok}
 Probability outcome between 0 and 1: 0 ≤ P(Oi) ≤ 1 for each i
 Sum of all probabilities is 1
- Classical approach: calculate games of chance – head or tail is 50%
- Relative frequency approach: long-run relative frequency, look at past – 200 out of 1000 is 20%
 This method is always used to interpret the probability
- Subjective approach: define probability as degree of belief – analysing factors influencing stock
- Event = collection/set of one or more individual outcomes in a sample space
- Probability of an event = sum of probabilities of simple event that make the event
- Intersection of Events A and B: event that occurs when both A and B occur – A and B
 Probability of the intersection = joined probability
- Marginal probabilities: adding the probability across rows or down columns
- Conditional probability: probability of A given event B – P(A|B) = P(A and B) / P (B)
- Union of Events A and B: event that occurs when either A or B or both occur – A or B
- Probability rules

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Summary of Statistics | Managerial Statistics, Keller | RSM premaster

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