Scheme of Work Statistics 1.

TERM CONTENT

Autumn Core 1

  • Not taught

Spring

Core 1 Examination 9th January 2008

  • Numerical Measures
  • Probability
  • Binomial Distribution
  • Normal Distribution
  • Confidence Intervals

Summer

  • Correlation
  • Regression
  • Revision

Exam MS1B 6th June

Statistics 1

Topic and Chapter

Time (weeks)

Objectives

These objectives relate to the main text objectives at the start of each chapter from the Heinemann text books.

Worthy of Note

These 'worthy of notes' are teacher observations that are worthy of a moment's thought.

Tasks

Any additions that teachers may feel are worthy of inclusion into the main scheme of work.

Introduction to Statistics  
  • Identify different types of variables, primary and secondary data.
  • Understand terms population, sample, parameter and statistic.
  • Understand the concept of a simple random sample.
   
Numerical Measures 2
  • Calculate the mode, median and mean.
  • Calculate the standard deviation, variation, range and interquartile range.
  • Use statistics to compare sets of data.
  • Select numerical measures appropriate to circumstances
  • Calculate mode, mean and median C2, pages 8 - 16
  • Use range, interquartile range and percentiles C2, pages 17 - 19
  • Calculate and use standard deviation C2, pages 19 - 26
  • Introduce scaling of measures; compare data sets C2, pages 26 - 31
 
Probability 2
  • Understand the concept of probability
  • Identify mutually exclusive events and independent events
  • Apply the additive 'and' law to mutually exclusive events
  • Apply the multiplicative 'or' law to independent events
  • Apply the conditional probability law to events which are not independent
  • Use simple tree diagrams to solve probability problems
  • Calculate the probability of equally likely events; identify exclusive and independent events
  • C3, pages 33 - 36
  • Introduce the language of probability
  • C3, pages 36 - 40
  • Use tree diagrams to calculate probability
  • C3, pages 40 - 43
  • Introduce conditional probability
  • C3, pages 43 - 45
  • Introduce the addition laws of probability
  • C3, pages 45 - 48
 
Binomial Distribution 3
  • Recognise when to use the Binomial Distribution
  • Recognise and state any assumptions necessary in order to use the Binomial Distribution
  • Apply the Binomial Distribution to real life problems
  • Define the binomial distribution; introduce binomial coefficients
  • C4, pages 57 - 63
  • Using the algebraic form of the binomial distribution; binomial tables
  • C4, pages 64 - 68
  • Creation of the binomial model and worked problems
  • C4, pages 72 - 78
 
The Normal Distribution 3
  • Use tables, or other, to find probabilities using the Normal Distribution
  • Use tables, or other, of percentage points of the Normal Distribution
  • Understand the meaning of distribution of the sample mean
  • Find probabilities using the sample mean
  • Introduce the conditions to use the Normal Distribution
  • C5, pages 81 - 85
  • Standardising normal varables
  • C5, pages 86 - 92
  • Modelling data using the normal distribution
  • C5, pages 93 - 95
  • Model data using the Central Limit Theorem
  • C5, pages 95 - 104
  • Modelling data
  • C5, pages 95 - 104
Confidence Intervals 2
  • Calculate a confidence interval for the mean of a Normal Distribution with a known standard deviation
  • Calculate a confidence interval for the mean of any distribution from a large sample
  • To determine a confidence interval of the mean, s.d. known
  • C6, pages 106 - 111
  • Use confidence intervals based on a large sample
  • C6, pages 112 - 123
 
Correlation 3
  • Investigate the strength of relationship between two variables
  • Evaluate and interpret the product moment correlation coefficient
  • To understand correlation and be able to use the PMCC
  • C7, pages 124 - 131
  • Limits of correlation and worked problems
  • C7, pages 131 - 144
Regression 2
  • Find the equation of regression lines
  • Interpret the gradient and coefficients of the gradient line equation
  • Plot a regression line on a scatter diagram
  • Calculate residuals and the fit of the regression line
  • Using and plotting a regression line
  • C8, pages 148 - 149
  • Residuals and predictions using the regression line
  • C8, pages 149 - 164
  • Predictions using regression
  • C8, pages 149 - 164
 

Last updated 20th November 2007