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Q:

Quantitative Variable

A:

takes numerical values for which arithemetic operations such as adding and averaging make sense

- interval scale
Q:

Cases

A:

objects described by a set of data (customers, companies, subjects in study, …)

Q:

residuals

A:

- difference between an observed value of the response variable and the value predicted by the regression line. That is, residual = observed y−predicted y =y−yˆ

- the mean of the least-squares residuals is always zero

Q:

Intercept

A:

the value of y when x = 0

b0= y¯-b1*x¯

Q:

Inter-rater reliability/ Cohen’s Kappa (K oder Ψ)

A:

- provides a measure of agreement between two observers coding on a nominal scale

- observed level of agreement relative to the level of agreement that would be expected by chance

- To what extent are the judgements similar?**Kappa (К) = (Ao–Ae) / (N -Ae)**

Ao= Agreement observed

Ae= Agreement expected**0.70 or higher considered‘good’** (0.40 ≤ К≤ 0.70 ‘reasonable’, К< 0.40 ‘bad’)

Q:

Outliers in regression

A:

- outliers in the y direction of a scatterplot have large regression residuals, but other outliers need not have large residuals

- Points that are out-liers in the x direction of a scatterplot are often influential for the least-squares regression line

Q:

Facts about least-squares regression

A:

1.

There is a close connection between correlation and the slope of the least-squares line.

- change of one standard deviation in x corresponds to change of r standard deviations in y
- correlation=0, slope= 0

The least-squares regression line always passes through the point (x¯,y¯)

3.

The distinction between explanatory and response variables is essential in regression

Q:

Correlation

A:

- measure direction& strength of linear relationship between 2 quantitative variables

- positive correlation when the association is positive and vice versa

- variables has to be quantitative

- because you use the z- score r does not change when you make a linear transformation

- always a number between 1 & -1

Q:

Histiogram

A:

- Stemplots are not suitable for large data sets
- displays only the count or percent of the observations that fall into each class
- large sets of data are usually presented in a
*frequency table*

Q:

Categorical Variable(qualititativ)

A:

places a case into one of several groups or categories(e.g. gender)

- nominal, ordinal

Q:

Inferential statistics

A:

conclusions about population based on limited number of elements (= sample) from that population

Q:

Quartils

A:

- describe spread by giving several percentiles

- median is the 50
^{th}percentile - upper
**quartile**is the median of the upper half of the data - lower
**quartile**is the median of the lower half of the data - The
**first quartile Q**is the median of one half of the distribution_{1}

The**third quartile Q**is the median of the other half of the distribution_{3}

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