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Level 3 Award in Mathematics for numeracy Teaching: Session 5: Handling data Gail Lydon & Jo Byrne

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### Transcript of Session 5 Handling Data Dec17 · PDF file · 2017-12-19standard deviation (σ)...

Level 3 Award in Mathematics for numeracy

Teaching:

Session 5: Handling data

Gail Lydon & Jo Byrne

to Jo. We will use that in a little while. https://drive.google.com/drive/folders/12QHkA0-FleJzkF_Zx70LPy_-0Js_O6gV?ths=true

Now:• https://ccpathways.co.uk/level-3-maths-online/• You'll need the password L3Maths16• on the Data Handling button• Read HO1 Data Handling and note on your ILP anything

you need to focus on.

Session aim

• To review & extend participants’ personal mathematics relating to statistics and probability

• To apply concepts of statistics and probability to solve problems

What do you need to know about statistics?

What do you need to know about statistics?

• Mean, median and mode• Sampling techniques• Data transformation (measures of

dispersion, curve fitting, spread, upper and lower quartiles, interquartile range, standard deviation)

• Statistical diagrams• Regression• Correlations• Probability

Review

• HO1

Frequency distributions

What’s the difference?

Measures of dispersion

• Range• Interquartile range• Variance• Standard deviation

Box & whisker plots

Group A

Group B

Variance and standard deviation

Normal distribution

Standard deviation

Standard deviation is used to assess if a data point is

OR

Standard and expected

(ie within expected variation)

Unexpected and unusual

(significantly above or below the average)

Standard deviation is represented by lower case sigma

68 – 95 – 99.7 rule

ΣUpper case sigma

• What does Σ mean?• It is the summation operator (so have a go at

calculating the following)

Upper case sigma

Standard deviation

Use the populationstandard deviation (σ) when data is relates to the entire population:

The standard deviation of a sample of the population (s) can be estimated using:

Note: • xi represents the individual values of x; • is the mean of all the values of x

(sometimes μ is used)

• N is the number of values ©greatlearning,2017

Let’s have a go at a standard deviation problem

• Have a look at HO2 (under R5)

Grouped and cumulative frequency distributions

Frequency diagram

Grouped frequency distribution

450

400

350

300

250

200

150

100

50

0

Score 0-10 11-20 21-30 31-40 41-50 51-60 61-70Frequency

10 60 230 420 250 28 2

Cumulative frequency table

Score 0-10 11-20 21-30 31-40 41-50 51-60 61-70

Frequency 10 60 230 420 250 28 2

Score £10 £20 £30 £40 £50 £60 £70

Cumulative frequency

10 70 300 720 1000998970

Cumulative frequency graph

Score £10 £20 £30 £40 £50 £60 £70

Cumulative frequency

10 70 300 720 970 998 1000

Box & whisker plot

Lower quartile

Upper quartile

Median

45

40

35

30

25

20

15

10

5

00 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150

Score

Freq

uenc

y

Score 0-10 11-20 21-30 31-50 51-100 101-150

Frequency 20 38 42 18 6 1

Frequenciesproportionaltoareaofbars

45

40

35

30

25

20

15

10

5

00 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150

Score

Freq

uenc

y

Score 0-10 11-20 21-30 31-50 51-100 101-150

Frequency 20 38 42 18 6 1

Bivariate analysis

Correlation

Negativecorrelation

Probability

Probability

P(a) = No. successful outcomesTotal possible outcomes

Combined probabilities

When two coins are tossed there are three possible outcomes: • Two heads• One head• No headsThe probability of two heads is therefore ⅓

Train punctuality data

On-time 84%

Up to 15 min late 7%

15-30 min. late 4%

More than 30 min. late 2%

Cancelled 3%

Scheduled services running in the last month:

Probability&frequencydata

450

400

350

300

250

200

150

100

50

00 10 20 30 40 50 60 70 80 90 100

Score

Freq

uenc

y

Score 0-10 11-20 21-30 31-40 41-50 51-60 61-70

Frequency 10 60 230 420 250 28 2

Probability – remember!

• Probability is: (Number of ways it can happen)

(Total number of outcomes)

• Dependent Events (such as removing marbles from a bag) are affected by previous events

• Independent events (such as a coin toss are not affected by previous events

• We can calculate the probability of 2 or more independent events by multiplying

• Not all coincidences are really unlikely (when you think about them).

Probability –dependent or independent?

Is it possible for the events to happen in any order?

Does one event in any way affect the outcome of the other event?

Dependent

Dependent

Independent

YES

YES

NO

NO

Pearson’s correlation coefficient

The Pearson product-moment correlation coefficient (or Pearson correlation coefficient, for short)

is a measure of the strength of a linear association (the relationship) between two variables and is denoted by r. Basically, a Pearson product-moment correlation attempts to draw a line of best fit through the data of two variables, and the Pearson correlation coefficient, r, indicates how far away all these data points are to this line of best fit (i.e., how well the data points fit this new model/line of best fit).

Pearson correlation coefficient – working it out

There are a number of variations of Pearson’s.

Pearson correlation coefficient cont

Pearson’s r is always between -1 and 1

Pearson correlation coefficient cont

Example 1 – a perfect positive relationship between x and y.

As x increases y increases exactly the same.

Pearson correlation coefficient cont

As x increases y decreases – perfect negative relationship

Pearson correlation coefficient cont

So what r=0 look like?

Pearson correlation coefficient cont

So what r=0 look like?

• Let’s look back at the equation we saw earlier

• What does Σ mean? Remember?• It is the summation operator and you calculated

Pearson correlation coefficient an example

• What does √ mean?

• So this isn’t a complicated equation – its just a long winded one – so you need to write down all of your steps.

Pearson correlation coefficient an example

Pearson correlation coefficient an example

Subject Age (x) Yearly income (y)

1 18 15,000

2 25 29,000

3 57 26,000

4 45 52,000

5 26 32,000

6 64 80,000

7 37 41,000

8 40 45,000

9 24 26,000

10 33 33,000

In this case what does n=?

n=10 so

Pearson correlation coefficient an example

Subject Age (x) Yearly income

(y)1 18 324 15,000

2 25 625 29,000

3 57 3249 26,000

4 45 2025 52,000

5 26 676 32,000

6 64 4096 80,000

7 37 1369 41,000

8 40 1600 45,000

9 24 576 26,000

10 33 1089 33,000

3692

=

136,161

15,629

Pearson correlation coefficient an example

Subject Age (x) Yearly income (y)

1 18 324 15,000

2 25 625 29,000

3 57 3249 26,000

4 45 2025 52,000

5 26 676 32,000

6 64 4096 80,000

7 37 1369 41,000

8 40 1600 45,000

9 24 576 26,000

10 33 1089 33,000

136,161 15,629

Pearson correlation coefficient an example

Subject Age (x) Yearly income (y)

1 18 324 15,000 15,0002

2 25 625 29,000 29,0002

3 57 3249 26,000 26,0002

4 45 2025 52,000 52,0002

5 26 676 32,000 32,0002

6 64 4096 80,000 80,0002

7 37 1369 41,000 41,0002

8 40 1600 45,000 45,0002

9 24 576 26,000 26,0002

10 33 1089 33,000 33,0002

136,161 15,629 421,0002

=177,241,000,000

Pearson correlation coefficient an example

Pearson correlation coefficientSubject Age

(x)Yearly income (y)

(x)(y)

1 18 15,000 270,000

2 25 29,000 725,000

3 57 68,000 3,876,000

4 45 52,000 2,340,000

5 26 32,000 832,000

6 64 80,000 5,120,000

7 37 41,000 1,517,000

8 40 45,000 1,800,000

9 24 26,000 624,000

10 33 33,000 1,089,000

Sum 369 421,000 18,193,000

Pearson correlation coefficient

So what is the 0.99 indicating?

That x (age) and y (yearly income) have a strong positive relationship.

Pearson correlation coefficient cont

Pearson’s r is always between -1 and 1

https://statistics.laerd.com/statistical-guides/pearson-correlation-coefficient-statistical-guide.php

is my favourite

Estimating from a scatterplot• https://www.youtube.com/watch?v=372iaWfH-Dg• https://www.youtube.com/watch?v=2B_UW-RweSE• This example is age and yearly income but can just as

easily be used to consider whether there is a correlation between maths and music

Pearson Product Moment Correlation Coefficient

Questions for you to work on:

• Probability• Statistics• Statistical distributions• Correlation and regression

Probability:Matthew is an A-level student studying English, History and

French.He plans to go to University and has received a conditional

offer from the University of Central Lancashire of ‘B C C’ in any combination of subjects

a) What are the chances that Matthew exactly meets the offer from the University of Central Lancashire?

b) What are the chances that Matthew meets or exceeds the University of Central Lancashire’s offer?

c) How would it affect your conclusions if your assumptions are wrong?

Matthew’shomeworkgradesEnglish B C C C D C D BHistory B C C B C D C BFrench C D C B C B C C

StatisticsIn April 2011 a survey of customer transaction values was carried out by a

retail business. The following data was collected

a) i) Plot the histogram and the Cumulative Frequency Curve for this dataii) Estimate the median transaction value and its Inter-quartile range from

the graphs.b) For the above data calculatei) The mean transaction value (stating clearly any assumptions made)ii) The standard deviationc) A similar survey was carried out in October 2010. The resulting data gave a mean value of £27.90 and a standard deviation of £14.30.Comment on how the spending pattern of customers has changed between the two months.

Transactionvalue(£’s) No.ofcustomersBelow10.00 13710.00to19.99 25920.00to29.99 29730.00to39.99 37840.00to49.99 19350.00to59.99 8460.00to69.99 52

Statistical distributions

Applicants for a certain job are given an aptitude test. Past-experience shows that the scores from the test are normally distributed with a mean of 60 and standard deviation of 12 marks.

What percentage of applicants would be expected to pass the test if the minimum score required was 75?

What would the pass mark need to be if the company wanted only 4% of applicants to pass?

What would be the percentage failing if the standard deviation were 20 points?

What practical use could the company make of this information?

Correlation and regressionThe number of customers in different regions and the corresponding

monthly sales of a product are:

1. Plot a scatter diagram of ‘Sales Volume’ against ‘Number of customers’2. Calculate the regression line and the coefficient of determination r23. Check your answers to b) in ONE way by using Excel functions or its Regression

facility

4. Plot the regression line on the same graph as the scatter diagram

What information does the coefficient of determination give you about the regression line?

Comment on the match between the data-points and the regression line. Is this consistent with the r2 value?

5. Use the regression line to predict ‘Sales Volume’ for Regions having 1000, 4000 and 7000 customers. In each case comment critically on the reliability of these predictions

SalesRegion 1 2 3 4 5 6 7 8 9 10

Numberofcustomers(00’s) 26 22 50 43 48 32 30 34 40 50

Salesvolume((£000s) 146 149 325 252 312 188 195 196 260 298