Kapitel 1-3

Question 1

elements

Answer 1

the entities on which data is collected’

Question 2

variable

Answer 2

is the different values of interest which gives us different outcomes

Question 3

nominal scale

Answer 3

A scale of measurement, when the data of a variable consits of label or names to identify an attribute of the element. Can also be numeric when the number stands for a label or name.

Question 4

ordinal scale

Answer 4

A scale of measurement, the same as nominal scale + the order or rank of the data is meaningful. Is non-numeric but can be numeric when the number stands for a label/name

Question 5

interval scale

Answer 5

A scale of measurement, the same as ordinal scale + the interval between values are expressed in terms of a fixed unit of measure. Always numeric. The differences between two numeric values has meaning.

Question 6

ratio scale

Answer 6

A scale of measurement, the same as interval scale + the ratio of two values are meaningful. Exampel: disrance, height, weight and time. The scale requires that a zero value means zero point for the value.

Question 7

quantatitive data

Answer 7

require numeric variables that indicate how much or how many

Question 8

cross-sectional data

Answer 8

data collected at the same or approximately the same time

Question 9

time series data

Answer 9

data collected over several time periods

Question 10

descriptive statistics

Answer 10

Summarize of data through tabular, graphs or numerical

Question 11

population

Answer 11

the set of all elements of interest in a particular study

Question 12

sample

Answer 12

subset of population

Question 13

frequency distribution

Answer 13

is a tabular data summary showing the frequency of items in each of several non-overlapping classes

Question 14

relative frequency distribution

Answer 14

tabular summary shoing relative frequency for each class. relative frequency of a class = frequency of the class / n

Question 15

percentage frequency distribution

Answer 15

summarizes the percentages frequency for each class

Question 16

bar chart

Answer 16

is a graphical representation of a frequency, relative frequency or percetages frequency with two axises (data and frequency)

Question 17

pie charts

Answer 17

a circel representing data for frequency, relative frequency or percentages frequency. Divided into sectors corresponding to its relative frequency.

Question 18

histogram

Answer 18

is a chart showing quantatitive data in a frequency distribution

Question 19

cumulative frequency distributions

Answer 19

Show the frequency of classes but divides the classes in “less than or equal to the upper class limit” of each class.

Question 20

dot plot

Answer 20

graphical summarizes data, equal many dots as the frequency

Question 21

stem-and-leaf display

Answer 21

shows both the rank order and shape of data set.

Question 22

cross tabulations

Answer 22

a tabular summary of data for two variables

Question 23

simpsons paradox

Answer 23

the problem which can occur when two variabels aggregates and gives reversed relation between the variables in comparsion when they are not aggregated

Question 24

clustered bar charts

Answer 24

shows joint distribution of two categorical variabels

Question 25

stacked bar charts

Answer 25

shows joint distribution of two cateogorical variabels

Question 26

scatter diagram

Answer 26

graphical representation of the relationships between two quantatitive variabels. Trend line provides a approximation of the relationship

Question 27

percentile

Answer 27

provides information about how the data are spread over the interval from the smallest to largest value. "the pth percentile is a value such that at least p percent of the observations are less than or equal to this value and at least (100-p) percent. of the observations are greater than or qual to this value"

Question 28

quartile

Answer 28

Division points: First quartile - 25th percentile Second quartile - 50 th percentile (also the median) Third quartile - 75th percentile

Question 29

range

Answer 29

Range = largest value - smallest value Highly affected by extrem high and low values.

Question 30

interquartile range

Answer 30

IQR = third quartile - first quartile

Question 31

variance

Answer 31

is a measure of variability that uses all data values and is based on the difference between each data value and the mean

Question 32

standard deviation

Answer 32

is defined as the positive square root of the variance

Question 33

coefficient of variation

Answer 33

describes how large the standrad deviation is relative to the mean --> standard deviation/mean x 100

Question 34

Distributional shape

Answer 34

histograms with relative frequency distributions shows the skewness, the distibutional shape

Question 35

negative, positive, zero skeweness

Answer 35

negative skeweness - skewed to the left positive skeweness - skewed to the right zero skeweness - symmetrical

Question 36

z-score

Answer 36

represents the number of standard deviations Xi from the sample mean, are standardized values

Question 37

Chebyshev´s theorem

Answer 37

enabels us to make a statements about the proportion of data values that lie within a specified numer of standrad deviations of the mean. "At least (1-1/z^2) x 100 of the data values must be within z standard deviations of the mean, where z is any value greater than 1"

Question 38

Cheabysthev´s theroem - percent

Answer 38

- at least 75% of the data values be within z=2 standard deviations of the mean - at least 89% of the data values be within z=3 standard deviations of the mean - at least 94% of the data values be within z=4 standard deviations of the mean

Question 39

empirical rule

Answer 39

When the approximation of a distributions is bell-shaped can the empirical rule be used to determine the approximate percentage of data values that lie within a specified number of standard deviations of the mean.

Question 40

Empirical rule - percent

Answer 40

For data with bell-shaped distribution: - approximately 68% of the data values lie within 1 standrad deviation of the mean - approximately 95% of the data values lie within 2 standrad deviation of the mean - allmost all data lies within 3 standard deviations of the mean

Question 41

outliers

Answer 41

Extreme values in a data set. Z-scores (standrdized values) can be used to identify outliers. In bell-shaped distributions, the empirical rule says that any data with z-scores less than -3 or more than 3 can be said to be outliers

Question 42

Five-number summary

Answer 42

Used to summarize data: 1. Smallest value 2. First quartile 3. Median 4. Third quartile 5. Largest value

Question 43

Box plot

Answer 43

Graphical version of the five-number summary. 1. A box is drawn with the box ends located at first and third quartile 2. A line is drawn across the box at the location of the median 3. By using the IQR, limits are located. The limits are 1.5(IQR) below first quartile and 1.5(IQR) above thrid quartile. Data outside these limit are considered outliers. 4. Whiskers (dashed lines) are drawn from the ends of the box to the smallest and largest value. 5. The outliers is shown with *

Question 44

Covariance

Answer 44

a descpritive measure of the linear association between two variables