m ario f. t riola 3rd e dition essentials of s tatistics
TRANSCRIPT
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MARIO F. TRIOLAMARIO F. TRIOLA3rd3rd EDITIONEDITION
Essentials of STATISTICS
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Programma vandaag
Organisatie en opzet van de cursus Waarom Statistiek? Vooruitblik op de stof hoofdstukken 1,2 en 3
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Website cursus:
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Website cursus:
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Boek
Literatuur:Mario Triola:
Essentials of Statistics, 3rd edition
Addison-Wesley Higher Education, 2008
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Rooster
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Organisatie en opzet (1)
Kijk zelf op website naar:– Introductie– Beoordeling en deadlines– Ziekteregeling– Rooster– Etc.
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Organisatie en opzet (2)
Uitdelen en inleveren:– Week 1: Opdrachten hoofdstuk 1, 2 en 3– Week 2: uitwerkingen hoofdstuk 1,2 en 3 en maak
een kopie voor de zelfbeoordeling bij de bespreking
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Organisatie en opzet (3)
Werkcolleges verplicht? Succes garantie?
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Samenhang?
relatie pr.cf en tnt.cf stat 0708
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tentamencijfer
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Reeks1
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Waarom Statistiek?
Lezen en schrijven artikelen vakgebied IK– Voorbeeld artikel MIS Quarterly
Lezen en schrijven in het dagelijks leven– Voorbeeld: tabel actiecommitee in de buurt
Baisvoorwaarde: logisch denken en redeneren– Voorbeeld: het Monty Hall-probleem
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Tabel (1) artikel MIS Quarterly
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Tabel (2) artikel MIS Quarterly
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Tabel buurtcomité
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Intuïtie is onbetrouwbaar
Monty Hall probleem– Quiz: er zijn 3 gesloten deuren, – Achter één deur staat een auto, achter beide
andere deuren is niets,– Jij mag een deur kiezen..– Welke kans op de hoofdprijs?
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Maar dan …
De quizmaster opent NA UW KEUZE een van de twee overgebleven deuren en laat zien dat daar niets achter zit.
Probleem: U mag nu nog van deur wisselen. Doet U dit?
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Analyse
Stel de hoofdprijs zit achter deur 1:
1. U koos deur 1 (auto). De quizmaster opent een andere deur waarachter niets staat. Ruilen levert verlies op…
2. U koos deur 2 (leeg). De quizmaster opent deur 3 waarachter niets staat. Ruilen levert hoofdprijs!
3. U koos deur 3 (leeg). De quizmaster opent deur 2 waarachter niets staat. Ruilen levert hoofdprijs!
1 2 3
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Aanpak hoorcolleges
Geen uitgebreide bespreking Wel vooruitblik op de stof en bespreking van
mogelijke knelpunten Nu: hoofdstuk 1, 2 en 3
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Sekties hfst 1, 2 en 3
1.1 Overzicht 1.2 Datatypen 1.3 Kritisch denken 1.4 Ontwerp experimenten
2.1 Overzicht 2.2 Frequentieverdeling 2.3 Histogrammen 2.4 Grafische weergave
3.1 Overzicht 3.2 Centrummaten 3.3 Variatiematen 3.4 Relatieve afwijking 3.5 Exploratieve data-
analyse
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Triola, hoofdstuk 1
Belangrijke definities voor gebruik bij de statistiek
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Sektie 1.1 Belangrijke definities
Data Statistiek Populatie Census Steekproef
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Definitie Statistiek
a collection of methods for- planning studies and experiments,- obtaining data, - and then organizing, summarizing, presenting, analyzing, interpreting, - and drawing conclusions based on the data
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Chapter Key Concepts
Sample data must be collected in an appropriate way, such as through a process of random selection.
• If sample data are not collected in an appropriate way, the data may be so completely useless that no amount of statistical torturing can salvage them.
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Sektie 1.2 Data typen
Definities:– Populatie parameter versus steekproef statistic– Kwantitatieve versus kwalitatieve data– Discrete versus continue data– Meetnivo’s: nominaal, ordinaal, interval, ratio
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Levels of Measurement
1. Nominal - categories only
2. Ordinal - categories with some order
3. Interval - differences but no natural starting point
4. Ratio - differences and a natural starting point
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Sektie 1.3 Kritisch denken
Misbruik, ondeskundig gebruik, verkeerd gebruik van de statistiek
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Misuse # 1- Bad Samples
Voluntary response sample (or self-selected sample)
- one in which the respondents themselves decide whether to be included. In this case, valid conclusions can be made only about the specific group of people who agree to participate.
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To correctly interpret a graph, you must analyze the numerical information given in the graph, so as not to be misled by the graph’s shape.
Misuse # 3- Graphs
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Loaded Questions Order of Questions Refusals Correlation & Causality Self Interest Study Precise Numbers Partial Pictures Deliberate Distortions
Other Misuses of Statistics
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Sektie 1.4 Ontwerp van het onderzoek
Soorten studies– Observationeel– Experimenteel– Retrospectief– Prospectief (longitudinaal, cohort)
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Confounding occurs in an experiment when the experimenter is not able to distinguish between the effects of different factors
Definition
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Voorbeeld: confounding effects
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Controlling Effects of Variables
Blinding– subject does not know he or she is receiving a
treatment or placebo Rigorously Controlled Design
– subjects are very carefully chosen Blocks
– groups of subjects with similar characteristics Completely Randomized Exp. Design
– subjects are put into blocks through a process of random selection
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Steekproeven
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Definitions
Random Sample– members of the population are selected in
such a way that each individual member has an equal chance of being selected
Simple Random Sample (of size n)– subjects selected in such a way that every
possible sample of the same size n has the same chance of being chosen
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Random
Systematic
Convenience
Stratified
Cluster
Methods of Sampling
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Triola, hoofdstuk 2
Statistiek voor het samenvatten en weergeven van data
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1. Center: A representative or average value that indicates where the middle of the data set is located.2. Variation: A measure of the amount that the values vary among themselves. 3. Distribution: The nature or shape of the distribution of data (such as bell-shaped, uniform, or skewed).4. Outliers: Sample values that lie very far away from the vast majority of other sample values.5. Time: Changing characteristics of the data over time.
Sektie 2.1 OverviewImportant Characteristics of Data
CVDOT
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Sektie 2.2 Frequentieverdelingen
Gewone (rechte) telling van waarden in een tabel
Samenvoegen van waarden in categorieën (classes)
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Frequency Distribution Ages of
Best Actresses
Frequency DistributionOriginal Data
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Samenhangende definities
Lower class limits Upper class limits Class boundaries Class midpoints Class width Relatieve frequenties Cumulatieve frequenties (cumulatieve percentages)
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Frequency Tables
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Sektie 2.3 Histogrammen
Grafische weergave van verdelingen
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HistogramA bar graph in which the horizontal scale represents the classes of data values and the vertical scale represents the frequencies
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Relative Frequency Histogram Has the same shape and horizontal scale as a histogram, but the vertical scale is marked with relative frequencies instead of actual frequencies
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One key characteristic of a normal distribution is that it has a “bell” shape. The histogram below illustrates this.
Critical ThinkingInterpreting Histograms
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Sektie 2.4 Statistical graphics
Andere vormen van visuele weergave– Polygon– Ogive– Dot plot– Stemplot– Pareto chart– Pie chart– Scatter plot– Time series
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Ogive
A line graph that depicts cumulative frequencies
Insert figure 2-6 from page 58
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Dot PlotConsists of a graph in which each data value is plotted as a point (or dot) along a scale of values
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Other Graphs
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Triola, hoofdstuk 3
Statistiek voor het beschrijven, verkennen en vergelijken van data
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Sektie 3.1 Overzicht
Descriptive Statistics– summarize or describe the important
characteristics of a known set of data
Inferential Statistics– use sample data to make inferences (or
generalizations) about a population
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Sektie 3.2 Centrummaten
Gemiddelde (mean) – Van steekproef (x-streep) en van populatie (mu)
Mediaan (x-tilde) Modus Midrange Gewogen gemiddelde
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Notation
µ is pronounced ‘mu’ and denotes the mean of all values in a population
x =n
x is pronounced ‘x-bar’ and denotes the mean of a set of sample values
Nµ =
x
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Carry one more decimal place than is present in the original set of values.
Round-off Rule for Measures of Center
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use class midpoint of classes for variable x
Mean from a Frequency Distribution
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Best Measure of Center
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Skewness
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Sektie 3.3 Variatiematen
Range Standaard deviatie
– steekproef (s) en populatie (sigma) Variantie (s-kwadraat)
Variatiecoëfficiënt (CV)
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Key Concept
Because this section introduces the concept of variation, which is something so important in statistics, this is one of the most important sections in the entire book.
Place a high priority on how to interpret values of standard deviation.
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Definition
The standard deviation of a set of sample values is a measure of variation of values about the mean.
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Sample Standard Deviation Formula
(x - x)2
n - 1s =
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Population Standard Deviation
2 (x - µ)
N =
This formula is similar to the previous formula, but instead, the population mean and population size are used.
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Standard Deviation - Important Properties
The standard deviation is a measure of variation of all values from the mean.
The value of the standard deviation s can increase dramatically with the inclusion of one or more outliers (data values far away from all others).
The units of the standard deviation s are the same as the units of the original data values.
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Variance - Notationstandard deviation squared
s
2
2
}Notation
Sample variance
Population variance
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Estimation of Standard DeviationRange Rule of Thumb
For estimating a value of the standard deviation s,
Use
Where range = (maximum value) – (minimum value)
Range
4s
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Estimation of Standard DeviationRange Rule of Thumb
For interpreting a known value of the standard deviation s, find rough estimates of the minimum and maximum “usual” sample values by using:
Minimum “usual” value (mean) – 2 X (standard deviation) =
Maximum “usual” value (mean) + 2 X (standard deviation) =
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The Empirical Rule
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Definition
The coefficient of variation (or CV) for a set of sample or population data, expressed as a percent, describes the standard deviation relative to the mean.
Sample Population
sxCV = 100%
CV =
100%
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Sektie 3.4 Maten van relatieve afwijking
Z-scores Quartielen Percentielen
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Key Concept
This section introduces measures that can be used to compare values from different data sets, or to compare values within the same data set. The most important of these is the concept of the z score.
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z Score (or standardized value)
the number of standard deviations that a given value x is above or below the mean
Definition
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Sample Population
x - µz =
Round z to 2 decimal places
Measures of Position z score
z = x - xs
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Interpreting Z Scores
Whenever a value is less than the mean, its corresponding z score is negative
Ordinary values: z score between –2 and 2 Unusual Values: z score < -2 or z score > 2
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Q1, Q2, Q3 divide ranked scores into four equal parts
Quartiles
25% 25% 25% 25%
Q3Q2Q1(minimum) (maximum)
(median)
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Percentiles
Just as there are three quartiles separating data into four parts, there are 99 percentiles denoted P1, P2, . . . P99, which partition the data into 100 groups.
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Sektie 3.5 EDA
Uitbijters (outliers) Boxplot
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Important Principles
An outlier can have a dramatic effect on the mean.
An outlier can have a dramatic effect on the standard deviation.
An outlier can have a dramatic effect on the scale of the histogram so that the true
nature of the distribution is totally obscured.
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Definitions
For a set of data, the 5-number summary consists of the minimum value; the first quartile Q1; the median (or second quartile Q2); the third quartile, Q3; and the maximum value
A boxplot ( or box-and-whisker-diagram) is a graph of a data set that consists of a line extending from the minimum value to the maximum value, and a box with lines drawn at the first quartile, Q1; the median; and the third quartile, Q3
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Boxplots
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Boxplots - cont
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Einde vooruitblik 1, 2 en 3
Volgende week: – Vragenuur hoofdstukken 1, 2 en 3– Vooruitblik hoofdstukken 4 en 5