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Q:

What is the difference between a bar chart and a histogram?

A:

- bar chart: bars are not connected ( qualitative variables)

- histogram: bars are continuous with the next one (quantitative variables)

Q:

What is the difference between a bar chart and a histogram?

A:

- bar chart: bars are not connected ( qualitative variables)

- histogram: bars are continuous with the next one (quantitative variables)

Q:

Describe the measures of central tendency. For which variables are they appropriate?

A:

- mean: sum of the values divided by the number of values (-> quantitative variables)

- median: middle values of ordered data (-> ordinal and quantitative variables)

- mode: most common value (-> quantitative and qualitative variables)

Q:

Explain the measures of variability.

A:

- range: maximum - minimum (IQR: range of the middle 50%, between first and third quartile)

- variance: average of the difference between each value and the mean squared

- standard deviation: square root of the variance -> approximately the average deviation

Q:

What are the characteristics of a normal distribution?

A:

- mean, median and mode are equal

- unimodal (only one peak)

- the 1sigma (68%), 2sigma (95%) and 3sigma (99,7%) rule applies

Q:

What is a characteristic of a positively/negatively skewed distribution?

A:

- positively skewed: mean > median > mode

- negatively skewed: mean < median < mode

Q:

What is the z-score?

A:

- the z-score standardizes, to see how far an observation is from the mean in terms of standard deviation, it has always the mean 0 and standard deviation 1

Z = x-µ/SD

Q:

How does the mean and the variance act if a distribution is shifted (x added or multiplied by x)?

A:

a) multiplied by x:

- mean is multiplied by x

- variance is multiplied by x^2

b) x added:

- mean added with x

- variance remains the same

Q:

What is the Pearson correlation coefficient?

A:

- tells about the strength and direction of a correlation

- -1<r<0 (negative correlation) or 0<r1 (positive correlation)

- r = 0 --> no correlation

- r = 1 or -1 --> perfect correlation

Q:

What is the R-square? How to interpret it?

A:

- the R-square compares the distance of observed values and estimated values to the mean and tells how well the regression line predicts the actual values. 0≤Rsquare≤1

- Interpretation: R-square is the percentage of variability of Y explained by the regression model.

Q:

What happens to the shape of a distribution after a Z-transformation?

A:

- the shape remains the same

- the mean will be 0 and the standard deviation will be 1

Q:

What are the different types of variables?

A:

- qualitative variables: nominal & ordinal

- quantitative variables: interval & ratio

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