Worked examples and exercises are in the text STROUD PROGRAMME 27 STATISTICS

Worked examples and exercises are in the text STROUD Tetxtbook 6 th Ed, Programme 27: Statistics (Prog. 28 in 7 th Ed) Introduction Arrangement of data Histograms Measure of central tendency Dispersion (remaining topics listed in book will not be covered)

Worked examples and exercises are in the text STROUD Programme 27: Statistics Introduction Arrangement of data Histograms Measure of central tendency Dispersion

Worked examples and exercises are in the text STROUD Programme 27: Statistics Introduction Statistics is concerned with the collection, ordering and analysis of data. Data consists of sets of recorded observations or values. Any quantity that can have a number of values is a variable. A variable may be one of two kinds: (a) Discrete – a variable whose possible values can be counted (b) Continuous – a variable whose values can be measured on a continuous scale

Worked examples and exercises are in the text STROUD Programme 27: Statistics Introduction Arrangement of data Histograms Measure of central tendency Dispersion

Worked examples and exercises are in the text STROUD Programme 27: Statistics Arrangement of data Table of values Tally diagram Grouped data Grouping with continuous data Relative frequency Rounding off data Class boundaries

Worked examples and exercises are in the text STROUD Programme 27: Statistics Arrangement of data Table of values A set of data: Can be arranged in ascending order:

Worked examples and exercises are in the text STROUD Programme 27: Statistics Arrangement of data Table of values Once the data is in ascending order: It can be entered into a table. The number of occasions on which any particular value occurs is called the frequency, denoted by f ValueNumber of times

Worked examples and exercises are in the text STROUD Programme 27: Statistics Arrangement of data Tally diagram When dealing with large numbers of readings, instead of writing all the values in ascending order, it is more convenient to compile a tally diagram, recording the range of values of the variable and adding a stroke for each occurrence of that reading:

Worked examples and exercises are in the text STROUD Programme 27: Statistics Arrangement of data Grouped data If the range of values of the variable is large, it is often helpful to consider these values arranged in regular groups or classes.

Worked examples and exercises are in the text STROUD Programme 27: Statistics Arrangement of data Grouping with continuous data With continuous data the groups boundaries are given to the same number of significant figures or decimal places as the data:

Worked examples and exercises are in the text STROUD Programme 27: Statistics Arrangement of data Relative frequency If the frequency of any one group is divided by the sum of the frequencies the ratio is called the relative frequency of that group. Relative frequencies can be expressed as percentages:

Worked examples and exercises are in the text STROUD Programme 27: Statistics Arrangement of data Rounding off data If the value 21.7 is expressed to two significant figures, the result is rounded up to 22. similarly, 21.4 is rounded down to 21. To maintain consistency of group boundaries, middle values will always be rounded up. So that 21.5 is rounded up to 22 and 42.5 is rounded up to 43. Therefore, when a result is quoted to two significant figures as 37 on a continuous scale this includes all possible values between: … and …

Worked examples and exercises are in the text STROUD Programme 27: Statistics Arrangement of data Class boundaries A class or group boundary lies midway between the data values. For example, for data in the class or group labelled: 7.1 – 7.3 (a)The class values 7. 1 and 7.3 are the lower and upper limits of the class and their difference gives the class width. (b)The class boundaries are 0.05 below the lower class limit and 0.05 above the upper class limit (c)The class interval is the difference between the upper and lower class boundaries.

Worked examples and exercises are in the text STROUD Programme 27: Statistics Arrangement of data Class boundaries (d) The central value (or mid-value) of the class interval is one half of the difference between the upper and lower class boundaries.

Worked examples and exercises are in the text STROUD Programme 27: Statistics Introduction Arrangement of data Histograms Measure of central tendency Dispersion

Worked examples and exercises are in the text STROUD Programme 27: Statistics Histograms Frequency histogram Relative frequency histogram

Worked examples and exercises are in the text STROUD Programme 27: Statistics Histograms Frequency histogram A histogram is a graphical representation of a frequency distribution in which vertical rectangular blocks are drawn so that: (a)the centre of the base indicates the central value of the class and (b)the height of the rectangle represents the class frequency [i.e., the number of values found to fall in the class – J.A.B.]

Worked examples and exercises are in the text STROUD Programme 27: Statistics Histograms Frequency histogram For example, the measurement of the lengths of 50 brass rods gave the following frequency distribution:

Worked examples and exercises are in the text STROUD Programme 27: Statistics Histograms Frequency histogram This gives rise to the histogram:

Worked examples and exercises are in the text STROUD Programme 27: Statistics Histograms Relative frequency histogram A relative frequency histogram is identical in shape to the frequency histogram but differs in that the vertical axis measures relative frequency.

Worked examples and exercises are in the text STROUD Programme 27: Statistics Introduction Arrangement of data Histograms Measure of central tendency Dispersion

Worked examples and exercises are in the text STROUD Programme 27: Statistics Measure of central tendency Mean Mode of a set of data Mode of a grouped frequency distribution Median of a set of data Median with grouped data

Worked examples and exercises are in the text STROUD Programme 27: Statistics Mean The arithmetic mean of a set of n observations is their average: When calculating from a frequency distribution, this becomes: [Here x now means not the individual observations, but the different values for which frequencies are counted – J.A.B.]

Worked examples and exercises are in the text STROUD (Simple) Coding Method for Calculating a Mean Manually or Mentally [Slide added by J.A.B.] The textbook mentions a “coding” method for calculating the mean. In class I go through a simplified, very useful version of this. It’s easy: instead of averaging the values themselves directly, you take a convenient number, the “base”, that’s very roughly in the middle of or near to the values. You work out their (positive or negative) deviations from that base value, take the average of those deviations, and then add that average to the base. The result is the average of the original values. Exercise: try it with 8 values between, say, 50 and 85, using, say, 60 or 70 as the base. Compare the difficulty of doing this with adding the values and dividing by 8. Also check that it doesn’t matter what base you choose, leaving you free to pick a convenenient round number. Exercise: explain why the method works in general.

Worked examples and exercises are in the text STROUD to be continued in week 11