2010 – 2011
Learning Outcomes
[Can be differentiated]
Teaching & Learning Activities
(All resources here are hyperlinked to the MEI website)
Exploring data 1: Introduction (Recap of statistics in GCSE Maths)
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Be able to use a tally chart to produce a frequency table (page 4 of the textbook).
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Be able to describe the shape of a distribution (e.g. symmetrical distribution, or positive or negative skew) (pages 5 and 6 of the
 textbook).
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Be able to construct stem-and-leaf diagrams (pages 6 - 8 of the textbook).
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Understand what is meant by categorical data (qualitative), numerical data, continuousdata and discrete data (pages 12, 13 of the textbook).
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Understand sigma notation (page 13 of the textbook).
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Know that the mean, mode, median and midrange are all measures of central tendency, know how to calculate them and when each should be used (pages 13 - 16 of the textbook).
Poster activities. Learners revise techniques known and present back to class
Study Plan
Notes and Examples
Crucial Points
HW and/or
Assessments
Multiple choice section test
Questions
Exploring Data 1 Section test solutions
Exploring Data 2: Frequency Distribution
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Be familiar with frequency tables and able to use them to calculate the mean (pages 17 - 19 of the textbook).
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Understand how data can be grouped into class intervals and how to deal with class boundaries for both continuous and discrete data (pages 22 - 24 of the textbook).
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Be able to use grouped data to estimate the mean (pages 25 -
29 of the textbook).
Poster activities. Learners revise techniques known and present back to class.
HE Applications project: Learners are given data on HE applications and have to present back and interpret their findings using statistics.
Learners can use Excel or Autograph
(Teacher can demonstrate basic use of each)
Study Plan
Notes and Examples
Crucial Points
Multiple choice section test
Questions
Exploring Data 2 Section test solutions
HE data presentation. Learners will be filmed!

Exploring Data 3: Measures of Spread
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Interactive Resources
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Mean and SD
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Know how to calculate the range of a set of data and be aware
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Other resources of its limitations as a measure of spread (pages 32, 33 of the
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Calculating measures of spread textbook).
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Know the meaning of mean square deviation (msd) and be fluent with both methods of calculating it (pages 35, 36 of the
 textbook).
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Know the meaning of root mean square deviation (rmsd) and be fluent at calculating it (pages 35, 36 of the textbook).
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Know the meaning of variance, be fluent at calculating it and know how it is different from the mean square deviation
(divisor n - 1 instead of n (pages 36 - 38 of the textbook).
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Know the meaning of standard deviation, be fluent at calculating it and know how it is different from the root mean square
Study Plan
Notes and Examples
Crucial Points deviation (square root of the variance, rather than square root of the msd) (pages 36 - 38 of the textbook).
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Be able to calculate the mean, msd, rmsd, variance and standard deviation of combined data sets (page 39, 40).
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Know that approximately 95% of data lie within two standard deviations of the mean for most data sets and that data values more than 2 standard deviations from the mean are therefore identified as outliers and should be investgated carefully to ensure they are valid (pages 40, 41 of the textbook).
Multiple choice section test
Questions
Exploring Data 3 Section test solutions
Exploring Data 4: Linear Coding
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Be able to use linear coding to simplify calculations of mean and standard deviation and convert them between different units (pages 46 - 48 of the textbook).
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Interactive Resources
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Linear Coding
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Active learning resources
Linear Coding puzzle
Linear Coding puzzle Solutions
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Other resources
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Study Plant
Notes and Examples
Crucial Points
Multiple choice section test
Questions
Exploring Data 4 Section test solutions
Exploring Data Chapter
Assessment
Exploring Data Chapter assessment solutions

Data presentation 1: Introduction
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
Be able to interpret and draw bar charts (pages 57 - 59 of the textbook).
Be able to interpret and draw vertical line charts (pages 57 - 59 of the textbook).
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Know that bar charts are best used to illustrate categorical
 data (page 57 of the textbook).
Know that vertical line charts are best used to

 illustrate discrete data (page 57 of the textbook).


Know the difference between a bar chart and a histogram (page
62 of the textbook).


Know that histograms are normally used to
HE Applications project: Learners are given data on HE applications and have to present back and interpret their findings using statistics.
Learners can use Excel or Autograph
(Teacher can demonstrate further use of each)
Study Plan
Notes and Examples
Crucial Points illustrate continuous data (page 62 of the textbook).
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Understand that the vertical axis of a histogram is frequency density, NOT frequency (page 64 of the textbook).
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Know that histograms can be used to represented grouped data with unequal class widths (page 65 of the textbook).
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Know how to calculate frequency densities (pages 63 - 64 of the textbook).
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Know how to construct a histogram; (pages 62 – 69 of the textbook).
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Understand that, for a histogram, the areas of the bars are proportional to the frequencies (Pages 62 – 63 of the textbook).
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Know and understand the circumstances in which it is acceptable to use histograms to illustrate discrete data (pages
66 - 68 of the textbook).
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Be aware of the problems with class boundaries when using histograms to illustrate discrete data (page 67 of the textbook).
Multiple choice section test
Questions
Section Test Solutions

Data Presentation 2: Quartiles, Box and Whisker Plots and
Cumulative Frequency Curves
HE Applications project: Learners are given data on HE applications and have to present back and interpret their findings using statistics.
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Know how to calculate the upper quartile, lower quartile and the interquartile range via calculation and using a cumulative
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Learners can use Excel or Autograph
 frequency curve; (pages 71 – 76).
Know how to construct a box-and-whisker diagram (boxplot);
(pages 73 and 77 of the textbook). out percentiles; (question 9 of Exercise 2D and the Notes and
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(Teacher can demonstrate further use of each.
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Know how to recognise outliers; (pages 73 – 74 of the textbook).
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Know how to construct a cumulative frequency table from a

Other resources grouped frequency table; (pages 74 – 75 of the textbook).
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Study Plan
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Know how to construct and interpret a cumulative frequency
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Notes and Examples curve; (pages 76 – 77 of the textbook).
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Crucial Points

Know how to use a cumulative frequency diagram to work
Examples).
Multiple choice section test
Questions
Section Test Solutions
Data Presentation Chapter
Assessment
Chapter assessment solutions
Probability 1: Introduction

Interactive Resources

Venn Diagrams Spreadsheet

Understand what is meant by the complement of an event and
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Active learning resources how to calculate its probability.
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Venn Diagram Matching Activity
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Understand and be able to calculate simple expectation. Venn Diagram Matching Activity
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Understand what is meant by mutually exclusive events.
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Be able to calculate the probability of either one event or
Solutions another.
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Study Plan
Notes and Examples
Crucial Points
Additional Exercise
Additional Exercise Solutions
Multiple choice section test
Questions
Probability 1 Section Test
Solutions

Probability 2: Probability from two or more trials

Understand how to use tree diagrams to calculate probabilities.
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Be able to use the addition law and multiplication law to
 calculate probabilities.
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Study Plan
Notes and Examples
Crucial Points
Additional Exercise
Additional Exercise Solutions
Multiple choice section test
Questions
Probability 2 Section test solutions
Probability 3: Conditional probability
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Active learning resources
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Probability Matching Activity
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Understand the concept of conditional probability; (pages 107 – 
Probability Matching Activity
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109 of the textbook).
Know the formula for conditional probability P( times P( B l A ); (page 109 of the textbook).
A and B ) = P( A )


Solutions
Probability Hexagonal Jigsaw
Probability Hexagonal Jigsaw
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Understand that for independent events: P( A l B ) = P( B ) so,
P( A and B ) = P( A ) times P( B ); (page 109 of the textbook). probability questions; (page 111 of the textbook).
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Understand how to use Venn diagrams when solving conditional
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Solutions
Other resources
Venn Diagrams Worksheet
Venn Diagrams Worksheet
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Understand how to use tree diagrams in solving conditional
Solutions probability questions; (page 117 of the textbook).
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Study Plan
Notes and Examples
Crucial Points
Additional Exercise
Additional Exercise Solutions
Discrete Random Variables 1: Introduction
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Know what a discrete random variable (DRV) is; (Pages 119 –
120 in the textbook).
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Know the notation and conditions for a DRV; (Page 120 in the


 textbook).
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Be able to construct a vertical line chart showing the probabilities of the possible outcomes of a DRV; (Pages 120 – 121 in the textbook).
Study Plan
Notes and Examples
Crucial Points
Additional Exercise
Additional Exercise Solutions
Multiple choice section test
Questions
Probability 3 Section test solutions
Probability Chapter Assessment
Probability Chapter assessment solutions
Multiple choice section test
Questions
Discrete Random Variables 1
Section Test Solutions

Discrete Random Variables 2: Expectation and variance
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Know what is meant by the expectation of a discrete random variable and the variance of a discrete random variable and be able to calculate these; (Pages 127 – 130 in the textbook).
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Other resources
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Discrete Random Variables 1
Discrete Random Variables 2
Study Plan
Notes and Examples
Crucial Points
Additional Exercise
Additional Exercise Solutions
Further Probability 1: Factorials, permutations and combinations

Know what a factorial is and how to calculate it. (Pages 139 – 
140 in the textbook).
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Be able to cancel efficiently when dividing factorials. (Examples
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
 on page 140 of the textbook).
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Know how to identify and calculate the number of permutations;
(Page 142 in the textbook).
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Know how to identify and calculate the number of combinations (Pages 143 – 144 in the textbook).
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Know how to calculate a binomial coefficient, and how to use
Pascal’s triangle to provide shortcuts in calculating probabilities.
(Pages 145 – 146 in the textbook).
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Be able to calculate probabilities in less simple cases. (Pages
147 – 148 in the textbook).

Know that: n C r
= n C n r
and n +1 C r +1
= n C r
+ n C r +1
; (Pages 145 - 146 in the textbook).
Study Plan
Notes and Examples
Crucial Points
Additional Exercise
Additional Exercise Solutions
Multiple choice section test
Questions
Discrete Random Variables 2
Section test solutions
Discrete Random Variables
Chapter Assessment
Chapter Assessment Solution s
Multiple choice section test
Questions
Further Probability 1 Section test solutions
Further Probability Chapter
Assessment
Further Probability Chapter assessment solutions

The Binomial Distribution 1: Introduction

Know the conditions required in order for the binomial distribution to be used to calculate probabilities and be able to apply it to a general probability case; (Pages 155 – 157 in the
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
 textbook).
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Know what a probability distribution is, and be able to calculate
 it; (Pages 155 – 156 in the textbook).
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Understand that the binomial distribution is an example of a probability distribution; (Pages 155 – 156 in the textbook).
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Be familiar with the notation B( n , p ) to denote a binomial distribution with n trials and probability of success p; (Page 156 in the textbook).
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Know and be able to use the formula: P( X = r ) = n C r p r q n r for 0
≤ r ≤ n ; (Page 156 in the textbook).
Study Plan
Notes and Examples
Crucial Points
Additional Exercise
Additional Exercise Solutions
The Binomial Distribution 2: Using the binomial distribution

Know how to find the expectation of the binomial distribution (Page 159 in the textbook).
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Be able to use the binomial distribution to work out probabilities in a given situation; (Pages 160 – 161 in the textbook).
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Active learning resources
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Binomial puzzle
Binomial puzzle solutions
Study Plan
Notes and Examples
Crucial Points
Additional Exercise
Additional Exercise Solutions

Multiple choice section test
Questions
Binomial Distribution 1 Section test solutions
Multiple choice section test
Questions
Binomial Distribution 2 Section test solutions
Binomial Distribution Chapter
Assessment
Binomial Distribution Chapter assessment solutions

Hypothesis testing using the binomial distribution 1: Introduction

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Be able to use tables of cumulative binomial probability (page
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174).
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Understand the process of hypothesis testing and the associated vocabulary (page 170 - 171).
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Be able to identify the null hypothesis and alternative hypothesis (H
0
and H
1
) when setting up a hypothesis test on a binomial probability model (page 170 – 171).
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Be able to conduct hypothesis tests at various significance levels
(page 171).
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Be able to draw a correct conclusion from the results of a hypothesis test on a binomial probability model (page 170 –
171).
Study Plan
Notes and Examples
Crucial Points
Additional Exercise
Additional Exercise Solutions
Hypothesis testing using the Binomial Distribution 2: More about hypothesis testing
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Interactive Resources
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Hypothesis testing
Instructions for using the

Be able to identify the critical region and acceptance region for
"Hypothesis Tester" a hypothesis test (pages 117 - 119).
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Understand when to apply one-tailed tests and two-tailed tests
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(pages 182 – 183).
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Know how to carry out a two-tailed test (pages 182 – 184).
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Study Plan
Notes and Examples
Crucial Points
Additional Exercise
Additional Exercise Solutions
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Multiple choice section test
Questions
Hypothesis testing 1 Section test solutions
Hypothesis Testing 2 Section
Test Questions
Multiple choice section test solutions
Hypothesis Testing Chapter
Assessment
Hypothesis testing Chapter assessment solutions