Week 1 ML

Question 1

Scientific Approach

Answer 1

• Systematic pursuit of knowledge
• Logical steps: Problem - Hypotheses
• Data collection: Observation of behaviour or experimentation
• Test hypotheses and draw conclusions

Question 2

Research Methods

Answer 2

The systematic approach to answering questions

Question 3

Statistics

Answer 3

• Numbers that summarise observations

- Mathematical procedures to produce those numbers

Question 4

Scientific Method

Answer 4

• Theory
• Hypothesis
• Exp/Observe (research methods)
• Evidence (statistics)
• Theory

Question 5

Testing hypothesis

Answer 5

• Reproducible observation of the hypothesised effect in action
• Controlled (reproducible) circumstances
• Empirically observed
• Variables measured and/or controlled
• Alternative explanations controlled/eliminated
• Observation/interpretation is unbiased

Question 6

The scientist practitioner model

Answer 6

Furthers understanding through research
• Consumers of research
• Evidence-based practice
• Inform own practice and methodology

Question 7

Scientific Enquiry

Answer 7

1. Choose something to observe
2. Choose method of observation
3. Describe observations
4. Identify variation in observations
5. Explain variations

Question 8

Types of data

Answer 8

Categorical

• > Nominal
• > Ordinal

Continuous

• > Interval
• > Ratio

Question 9

Nominal data

Answer 9

refers only to identity information, that is values are ascribed that have no inherent order, or magnitude.
For example, gender, nationality, or the number assigned in a race are all types of nominal data
-> names of things without meaning

Question 10

Ordinal data

Answer 10

describes identity, but has magnitude.
For example, medal positions in a race are types of ordinal data. They have a sequential order (the gold medalist beat the silver medalist beat the bronze medalist), but this measure doesn’t tell us anything about the interval between each competitor, they are categorised as 1 - 2 - 3
-> data that is ordered without fixed intervals

Question 11

Interval data

Answer 11

a continuous type of variable, measuring identity and magnitude and fixed intervals between units of measurement.
For example, temperature. Here, the difference between 20 degrees and 30 degrees is the same as between 60 degrees and 70 degrees. We can order our data points by magnitude as we do with ordinal data, but we can also quantify the amount of difference between data points.
-> data that has fixed intervals allowing us to order it

Question 12

Ratio data

Answer 12

identity, magnitude, fixed interval and there is a true zero.
For example, the time a race is run cannot be a negative value.
-> time, height, where there are fixed intervals but there is a “true zero” which the data can not run under

Question 13

Descriptive statistics

Answer 13

• Each observation is a “Datum”: Plural is “Data”
• A bunch of data is often called a “Data Set”
• Different types of data are analysed in different ways
• Most basic description is how frequently similar observations occurred
• Easiest description to follow is a picture

Question 14

Data through pictures

Answer 14

• Bar graph
• Line graph
• Pie chart
• Scatter Plot

Question 15

Categorical data through pictures

Answer 15

• Pie chart

- Bar graph

Question 16

Continuous data through pictures

Answer 16

• Histogram

- Box plot

Question 17

Histogram components

Answer 17

• Title
• X label and data (bins/classes of variable)
• Y label and data (frequency/total)

Question 18

Negative skew

Answer 18

skewed to the left.
There are more points towards the higher end of the x axis.
-> right side higher

Question 19

Positive skew

Answer 19

skewed to the right.
There are more points towards the lower end of the x axis
-> left side higher

Question 20

Kurtosis

Answer 20

• Kurtosis refers to the ‘peakedness’ of a distribution, the relative concentration of values at the centre, tails or shoulders of the histogram.
• Normal distributions have the distinctive bell shaped curve.
• Positive kurtosis have an overly high concentration of values at the centre, giving a pronounced central peak.
• Negative kurtosis are much flatter, and have a less distinctive peak.

Question 21

Number of peaks

Answer 21

1: unimodal
2: bimodal
3+: multimodal

Question 22

Spread

Answer 22

We can have distributions that give the histogram a thinner or thicker appearance. This would tell us that our participants vary very little, or vary more. Again - we can use pictures with numbers to describe the spread of data.

Question 23

Deviations from pattern

Answer 23

• Outliers

Question 24

Shape

Answer 24

• skew
• kurtosis
• ## peaks

Question 25

Deviations from mean

Answer 25

above the mean -> positive deviation below the mean -> negative deviation *sum of the deviations is equal to zero

Question 26

Skewed distribution reporting

Answer 26

- do not use the mean because it is effected by outliers whereas median is a resistant measure of centre - mode does not always report centre accurately - typically report using median

Question 27

Which average to use

Answer 27

categorical: mode continuous and skewed: median continuous and normal dist: mean

Question 28

Spread

Answer 28

- variability - minimum and maximum - quartiles

Question 29

Variability

Answer 29

when people are quite similar to each other - we have low variability in scores when people are quite different from each other - there is high variability in the data.

Question 30

Minimum and maximum

Answer 30

probably the easiest measure of spread to calculate. Quite simply, the lowest and highest values. This gives an indication of the range over which the scores occur - not robust to outliers

Question 31

Quartiles

Answer 31

- arranging the data from least to most value and then splitting into quarters with the median being in the centre - Q1 and Q2 are below the median and Q3 and Q4 are above the median - fairly robust to outliers, a useful measure of spread

Question 32

Standard Deviation

Answer 32

- a rough measure of the average amount by which scores deviate from the mean - majority of data will fall between one standard deviation of mean - minority of data will be outside two standard deviations of mean

Question 33

Keeping outliers

Answer 33

Reason: Can’t pick and choose your data Pro: You are considering all of your data set Con: Your measure of the mean and standard deviation will be overly influenced by the outlier

Question 34

Disregarding outliers

Answer 34

Reason: This individual is not representative Pro: Your mean and standard deviation are less influenced by one individual Con: Your statistics will not apply to all of the individuals

Question 35

Pie charts

Answer 35

- Divide up circles (the pie) into different areas (the slices). - Each slice represents the percentage values or counts for categorical variables - The size of each slice of pie should be proportional to the percentage or count - difficult to make by hand - work for 10 categories or less - useful for proportional categorical data

Question 36

Bar graph

Answer 36

- can show data across more than one variable - can show error bars for SD - difficult with many categories - difficult with categories close in value - useful for counts (frequency) or summary of data

Question 37

Stem and leaf plots

Answer 37

- similar to histograms | - ten's on the left and singles after it

Question 38

Box plots

Answer 38

- box and whisker plots - can show distribution of continuous variable - show median surrounded by box of Q1 and Q3 - "whiskers" can be min and max points - "whiskers" typically min and max points within upper and lower fences - upper and lower fences are Q1 - 1.5xIQR and Q3 + 1.5xIQR - can show skew through where the median line sits - > median line lower when positive skew - > median line higher when negative skew

Question 39

Showing data

Answer 39

- shape of data - this is best achieved using pictures, such as histograms of the data. - describing the centre and spread of data are best achieved using numbers, such as our means, medians and modes for centre - our minimum and maximum values, quartiles, variance and standard deviations for spread. - describing deviations from what is ‘normal’ in our sample require both pictures (to show where the data is) and numbers (to describe how it deviates based on rules such as the Interquartile range rule).