Samenvatting
Summary Business Environment - Q2 - Statistics
- Vak
- Statistics
- Instelling
- Avans Hogeschool (Avans)
Summary of 3 pages for the course Statistics at Avans
[Meer zien]Voorbeeld 2 van de 3 pagina's
Voorbeeld 2 van de 3 pagina's
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Voorbeeld van de inhoud
Statistics for People Who (Think They) HateStatistics: 4th Edition
Chapter 1:
Statistics: set of tools and techniques, which is used for describing, organizing,
and interpreting information or data.
Descriptive statistics: used to organize and describe the characteristics of a
collection of data. (data set)
Inferential statistics: used to make inferences from a smaller group of data to
a possibly larger one
Population: data with all the occurrences with certain characteristics (N)
Sample: smaller group of data, which is a portion, or a subset of a population (n)
Chapter 2:
Measures of central tendency: also referred as averages; value that best
represents an entire group of scores
Mean: average: = ∑X / n (µ)
: x-bar; mean value of groups of scores or mean
∑: Greek letter sigma; summation sign, add together
whatever follows it
X: each individual score in group of scores
n: size of sample
N: size of population
As used for numerical data: test scores, time lapses without extreme
scores; sensitive to outliers
Median: middle number of a set of data when set in order
To determine the median of sample or population:
1. Set data in order (rank the numbers)
2. Determine the middle one: (n + 1) / 2
3. Determine corresponding value
Mean is middle point of set of values, median middle point of set of
cases
Median cares about how many cases; not values of the cases,
extreme scores/outliers
As used for numerical data with extreme scores
Mode: number that appears the most
As used for categorical data: hair color, color of eyes, ethnicity
(Individual data – frequency distributions)
Skew: significantly distortion of the actually central point in set or distribution of
scores; if there are too many extreme scores
Outliers: extreme scores/values of cases
, Chapter 3:
Variability: reflects how scores differ from one another as individual scores
Range: highest score in distribution – lowest score in distribution: r = h – l
As used to get a very general estimate of how wide or different scores are
from one another; general indicator of variability
Exclusive range: r = h – l
Inclusive range: r= h – l + 1
Inter quartile range: Q3 – Q1;
1. Calculate the median
2. Name the median Q2
3. Calculate the median of first half (Q1) and second half (Q3)
4. Subtract Q1 of Q3 (Q3 – Q1)
Standard deviation:
As used for individual data or frequency table
s = standard deviation (s (sample; unbiased), SD, σ
(population; biased))
To determine the standard deviation of sample (or
population)
1. List each individual score of group
2. Compute mean of group
3. Subtract mean from each individual score
4. Square each individual difference (result of step 3)
5. Sum all squared deviations (sum results step 4)
6. Divide sum by n – 1 (unbiased) (or n; biased)
7. Compute square root of result of step 6
Variance: Standard deviation squared (standard deviation without step 7)
Important: as used both as concept and as practical measure of variability
Chapter 4:
Frequency distribution: method of tallying and representing how often certain
scores occur
Class interval: range of numbers; first step in creation of frequency distribution;
to define how large each interval will be
1. Select class interval with range of: 2, 5, 10, 15 or 20 data points
2. Select class interval 10 to 20 such intervals to cover entire range of data
(n / 10 or 20 = interval)
3. Begin listing class interval; start with 0 and go on with chosen class
interval
4. Finish listing class interval; top of frequency distribution
Histogram: visual representation of frequency distribution; represented by bars;
as used for numerical data: test scores, persons’ height or weight
Column/bar chart: as used for categorical data: gender, eye color, hair color
Frequency polygon:
1. Place class interval midpoint at top of each bar in histogram
2. Connect these midpoints with each other
Cumulative frequency distribution: visual representation of cumulative
frequency of occurrences by class intervals; add the frequency in a class interval
to all the frequencies below
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