Graphs. Every graph is a figure but not every figure is a graph. Graphs are a particular set of figures that display quantitative relationships between variables.

Describing language of a graph

• UP: increase / rise / grow / went up / soar / double / multiply / climb / exceed /
• DOWN: decrease / drop / fall / decline / plummet / halve / depreciate / plunge.
• UP & DOWN: fluctuate / undulated / dip /
• SAME: stable (stabilised) / levelled off / remained constant or steady / consistent.

How to Choose Which Type of Graph to Use?

1. When to Use . . .
2. . . . a Line graph. Line graphs are used to track changes over short and long periods of time. When smaller changes exist, line graphs are better to use than bar graphs.
3. . . . a Pie Chart.
4. . . . a Bar Graph.
5. . . . an Area Graph.
6. . . . an X-Y Plot.

Scatter plots are best for showing distribution in large data sets.

It can provide information on the degree of variation of the data and show the distribution pattern of the data by bar graphing the number of units in each class or category. A histogram takes continuous (measured) data like temperature, time, and weight, for example, and displays its distribution.

The primary use of a Histogram Chart is to display the distribution (or “shape”) of the values in a data series. For example, we might know that normal human oral body temperature is approx 98.6 degrees Fahrenheit. To test this, we might sample 300 healthy persons and measure their oral temperature.

Classifying distributions as being symmetric, left skewed, right skewed, uniform or bimodal.

The shape of a distribution is described by its number of peaks and by its possession of symmetry, its tendency to skew, or its uniformity. (Distributions that are skewed have more points plotted on one side of the graph than on the other.) PEAKS: Graphs often display peaks, or local maximums.

normal distribution or normal curve. It is most appropriate to report the mean for such a distribution.

Types of Frequency Distribution

• Normal Distribution. The normal distribution, also known as a Gaussian distribution or “bell curve” is the most common frequency distribution.
• Skewed Distribution.
• Bimodal/Multimodal Distribution.
• Uniform Distribution.
• Logarithmic/Pareto.
• PERT/Triangular.

Statistics Chapter 2 Section 2-2 Page 43 Problems 1-18

A normal frequency distribution is a theoretical continuous, symmetrical, bell-shaped distribution function. Its mean, mode and median are all the same; and both the tails of the bell curve are infinitely long. Simple normal distributions are frequently used for modelling uncertainty.

Here, we see the four characteristics of a normal distribution. Normal distributions are symmetric, unimodal, and asymptotic, and the mean, median, and mode are all equal. A normal distribution is perfectly symmetrical around its center.

A normal distribution is a common probability distribution . It is a statistic that tells you how closely all of the examples are gathered around the mean in a data set. The shape of a normal distribution is determined by the mean and the standard deviation.

Properties of a normal distribution

1. The mean, mode and median are all equal.
2. The curve is symmetric at the center (i.e. around the mean, μ).
3. Exactly half of the values are to the left of center and exactly half the values are to the right.
4. The total area under the curve is 1.

The normal distribution is the most important probability distribution in statistics because it fits many natural phenomena. For example, heights, blood pressure, measurement error, and IQ scores follow the normal distribution. It is also known as the Gaussian distribution and the bell curve.

Any normal distribution can be standardized by converting its values into z-scores….Standardizing a normal distribution

1. A positive z-score means that your x-value is greater than the mean.
2. A negative z-score means that your x-value is less than the mean.
3. A z-score of zero means that your x-value is equal to the mean.