Describing & Visualising Data

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Describing & Visualising Data by Mind Map: Describing & Visualising Data

1. Tips

1.1. Always see them graphically, numeric summary dont tell everything

1.1.1. shapes

1.1.2. trends

1.1.3. gaps

2. Data Types

2.1. Categorical/Qualitative

2.1.1. No sense of ordering or difference in importance

2.1.2. Classify/Label

2.1.3. E.g. Gender Products

2.2. Numerical/Quantitative

2.2.1. Types Continuous Measured Can take finer granularity Discrete Counted Whole numbers

2.2.2. Sense of comparability for importance

2.3. So what

2.3.1. Different ways to Collect Analyse Control charts Present Interpret

3. Understanding Quantitative data

3.1. Typical value

3.1.1. Based on the distribution or shape of data

3.1.2. Mean Preferred - thats how we think

3.1.3. Median Based on where the data is nor the value Skewed Outliers presented

3.2. data variabily/distance b/n data

3.2.1. S.D affected by outliers

3.2.2. Variance

3.2.3. Relative comparability Coefficient of variation

3.2.4. Quartile ranges not affected by outliers

3.3. Shape

3.3.1. Symmetric/Bell

3.3.2. Modality

3.3.3. Skewness

3.4. Extremes/Oddness

3.4.1. Are they usual

3.4.2. Or something going wrong

4. Histograms

4.1. Size (∴ number) class intervals alter • interpretation • shape • perception of data

4.1.1. Rule of thumb

4.1.2. IQR

4.1.3. make the interval human friendly

4.2. Not recommended for <40 data points

4.2.1. too abstract/rough/cant make sense

4.2.2. alternatives box plot stem and leaf plots

4.3. Why

4.3.1. shape/distribution of data Bell - Shaped Double - Peaked bimodal combo of two bells two data sets Comb up down up down Plateau/uniform Skewed right left Truncated no tail on one side Isolated - Peaked like double peaked but one set has fewer observations Edge - Peaked

5. Box plot

5.1. box = 50 % data

5.1.1. has both mean mediam

5.2. whiskers

5.2.1. up to 1.5 IQR

5.2.2. if not stops with actual data point

5.3. outliers => cautious interpretation

5.3.1. If the data is long tailed there will be outliers => but they are part of the data and they are not unusual

5.4. Cant tell if there is a bimodality

5.4.1. fat box

5.4.2. short whiskers

5.5. Shape

5.5.1. Look for relative size of left/right boxes length of whiskers side of mean to the median mean is pulled towards the skewness of the data

5.5.2. Left/Right Skewed/Symmetric

5.6. Interpretation

5.6.1. Boxes (ignore whiskers) DO overlap no stat. sig. dif

5.6.2. Boxes DONT overlap stat. sig. dif How much stat. dif => Calculate difs in Medians Means