Year 12 Core Module
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| Trend | Is present when there is a long-term upward or downward
movement in a time series.
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| Cycles | are present when there is a periodic movement in a time series.
The period is the time it takes for one complete up and down movement
in the time series plot. This term is generally reserved for periodic
movements with a period greater than one year.
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| Seasonality | is present when there is a periodic movement in a time
series that has a calendar related period – for example, a year, a month,
a week.
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| Irregular (random) fluctuations | are always present in any real-world
time series plot. They include all of the variations in a time series that
we cannot reasonably attribute to systematic changes like trend, cycles,
seasonality, structural change or the presence of outliers.
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| Smoothing | is a technique used to eliminate some of the irregular
fluctuations in a time series plot so that features such as trend are more
easily seen.
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| Seasonal indices | are used to quantify the seasonal variation in a time
series.
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| Deseasonalise | The process of accounting for the effects of seasonality in a time series
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| Reseasonalise | The process of a converting seasonal data back into its original form is
called
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| Bivariate Data | are data in which each observation involves recording
information about two variables for the same person or thing. An
example would be the heights and weights of the children in a
preschool.
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| Residuals | The vertical distance from a data point to the straight line
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| Interpolation | Predicting within the range of data
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| Extrapolation | Predicting outside the range of data
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| Slope | Gradient on a linear graph
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| Coefficient of determination | gives a measure of the predictive
power of a regression line
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| Residual plot | can be used to test the linearity assumption by plotting
the residuals against the EV.
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| Correlation coefficient | gives a measure of the strength of a
linear association
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| Scatterplot | is used to help identify and describe an association
between two numerical variables
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| Parallel box plots | can be used to display, identify and describe the
association between a numerical and a categorical variable
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| Segmented bar charts | can be used to graphically display the
information contained in a two-way frequency table. It is a useful tool
for identifying relationships between two categorical variables
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| Two-way frequency tables | are used as the starting point for investigating the association between two categorical variables
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| z-score | also known as standardised scores. The value of the standard score gives the distance and direction of a data
value from the mean in terms of standard deviations.
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| 68-95-99.7% rule | the rule for normal distribution
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| The normal distribution | Data distributions that have a bell shape can be modelled by
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| outliers | data points away from the majority of the data set
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| Box plots | a graphical representation of a five-number
summary
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| Five number summary | A listing of the median, M, the quartiles Q1 and Q3, and the smallest and largest data values of a distribution, written in the order - minimum, Q1, M, Q3, maximum
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| Interquartile range | gives the spread of the middle 50% of data values
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| Median | It is the midpoint of a distribution dividing an ordered
dataset into two equal parts.
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| Univariate Data | are generated when each observation involves
recording information about a single variable, for example a dataset
containing the heights of the children in a preschool
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| Categorical Variable | are used to represent characteristics of individuals
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| Nominal Variable | generate data values that can only be used
by name
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| Ordinal Variable | generate data values that can be used to both name and order
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| Numerical Variables | used to represent quantities.
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| Discrete Variables | represent quantities – e.g. the number of cars in a car park
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| Continuous Variables | represent quantities that are measured rather than counted –
for example, weights in kg.
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| Bar Charts | are used to display frequency distribution of categorical
data
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| Histograms | used to display the frequency distribution of a numerical variable. It is suitable for medium- to large-sized datasets.
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Review the information in the table. When you are ready to quiz yourself you can hide individual columns or the entire table. Then you can click on the empty cells to reveal the answer. Try to recall what will be displayed before clicking the empty cell.
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To hide a column, click on the column name.
To hide the entire table, click on the "Hide All" button.
You may also shuffle the rows of the table by clicking on the "Shuffle" button.
Or sort by any of the columns using the down arrow next to any column heading.
If you know all the data on any row, you can temporarily remove it by tapping the trash can to the right of the row.
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