To be given to you next time:

•Short Project, What do students drive?

•AP Problems

DATA –

Who

What

Why

Categorical vs. Quantitative Variables

The distribution tells us what values the variables take and how often they take them…

Fundamentals…

Every Graph should have:

PIE CHARTS

What it is:A pie chart is used to show visually the proportions of parts of something being studied. The area of each slice of the pie shows the slice’s proportion to the entire category being studied and to the other slices. A pie chart shows data at one point in time, like a snapshot; it does not show change in data over time like a line chart does.

DOTPLOTS

STEMPLOTSex: heights..

Would this be better split into two?

WATCH YOUR SOCS…

SHAPE

The shape of a distribution is described by the following characteristics.Symmetry. When it is graphed, a symmetric distribution can be divided at the center so that each half is a mirror image of the other.

•Number of peaks. Distributions can have few or many peaks. Distributions with one clear peak are called unimodal, and distributions with two clear peaks are called bimodal. When a symmetric distribution has a single peak at the center, it is referred to as bell-shaped.

•Skewness. When they are displayed graphically, some distributions have many more observations on one side of the graph than the other. Distributions with most of their observations on the left (toward lower values) are said to be skewed right; and distributions with most of their observations on the right (toward higher values) are said to be skewed left.

•Uniform. When the observations in a set of data are equally spread across the range of the distribution, the distribution is called a uniform distribution. A uniform distribution has no clear peaks.

Examples of Each?

OUTLIERS

Gaps. Gaps refer to areas of a distribution where there are no observations. The first figure below has a gap; there are no observations in the middle of the distribution.

Outliers. Sometimes, distributions are characterized by extreme values that differ greatly from the other observations. These extreme values are called outliers. The second figure below illustrates a distribution with an outlier. Except for one lonely observation (the outlier on the extreme right), all of the observations fall between 0 and 4. As a "rule of thumb", an extreme value is often considered to be an outlier if it is at least 1.5 interquartile ranges below the first quartile (Q1), or at least 1.5 interquartile ranges above the third quartile (Q3).

CENTER

Graphically, the of a distribution is located at the median of the distribution. This is the point in a graphic display where about half of the observations are on either side. In the chart to the right, the height of each column indicates the frequency of observations.

SPREAD

The spread of a distribution refers to the variability of the data. If the observations cover a wide range, the spread is larger. If the observations are clustered around a single value, the spread is smaller.

Bar Charts

A bar chart is made up of columns plotted on a graph. Here is how to read a bar chart.

HISTOGRAMS

Like a bar chart, a histogram is made up of columns plotted on a graph. Usually, there is no space between adjacent columns. Here is how to read a histogram.

•The columns are positioned over a label that represents a quantitative variable. •The column label can be a single value or a range of values. •The height of the column indicates the size of the group defined by the column label.

Here is the main difference between bar charts and histograms. With bar charts, each column represents a group defined by a categorical variable; and with histograms, each column represents a group defined by a quantitative variable.

One implication of this distinction: it is always appropriate to talk about the skewness of a histogram; that is, the tendency of the observations to fall more on the low end or the high end of the X axis.

With bar charts, however, the X axis does not have a low end or a high end; because the labels on the X axis are categorical - not quantitative. As a result, it is less appropriate to comment on the skewness of a bar chart.

Percentiles

Assume that the elements in a data set are rank ordered from the smallest to the largest. The values that divide a rank-ordered set of elements into 100 equal parts are called percentiles

An element having a percentile rank of Pi would have a greater value than i percent of all the elements in the set. Thus, the observation at the 50th percentile would be denoted P50, and it would be greater than 50 percent of the observations in the set. An observation at the 50th percentile would correspond to the median value in the set.

Comparing Distributions

Discrete vs. Continuous Variables Quantitative variables can be further classified as discrete or continuous. If a variable can take on any value between two specified values, it is called a continuous variable; otherwise, it is called a discrete variable.

Some examples will clarify the difference between discrete and continuous variables.

Suppose the fire department mandates that all fire fighters must weigh between 150 and 250 pounds. The weight of a fire fighter would be an example of a continuous variable; since a fire fighter's weight could take on any value between 150 and 250 pounds.

Suppose we flip a coin and count the number of heads. The number of heads could be any integer value between 0 and plus infinity. However, it could not be any number between 0 and plus infinity. We could not, for example, get 2.5 heads. Therefore, the number of heads must be a discrete variable.

Statistical data is often classified according to the number of variables being studied.

Univariate data. When we conduct a study that looks at only one variable, we say that we are working with univariate data. Suppose, for example, that we conducted a survey to estimate the average weight of high school students. Since we are only working with one variable (weight), we would be working with univariate data.

Bivariate data. When we conduct a study that examines the relationship between two variables, we are working with bivariate data. Suppose we conducted a study to see if there were a relationship between the height and weight of high school students. Since we are working with two variables (height and weight), we would be working with bivariate data

Statisticians use summary measures to describe patterns of data. Measures of central tendency refer to the summary measures used to describe the most "typical" value in a set of values.

The most common of these is the MEDIAN and the MEAN

As measures of central tendency, the mean and the median each have advantages and disadvantages. Some pros and cons of each measure are summarized below.

To illustrate these points, consider the following example...

Suppose we examine a sample of 10 households to estimate the typical family income. Nine of the households have incomes between $20,000 and $100,000; but the tenth household has an annual income of $1,000,000,000. That tenth household is an outlier. If we choose a measure to estimate the income of a typical household, the mean will greatly over-estimate family income (because of the outlier); while the median will not.

Thus, we say the Median is RESISTANT and the Mean is NOT RESISTANT…

The range is the difference between the largest and smallest values in a set of values.

For example, consider the following numbers: 1, 3, 4, 5, 5, 6, 7, 11. For this set of numbers, the range would be 11 - 1 or 10.

The interquartile range (IQR) is the difference between the largest and smallest values in the middle 50% of a set of data.

For example, consider the following numbers:

1, 3, 4, 5, 5, 6, 7, 11.

1, 3, 4, 5, 5, 6, 7, 11

In a population, variance is the average squared deviation from the population mean, as defined by the following formula:

where σ2 is the population variance, μ is the population mean, Xi is the ith element from the population, and N is the number of elements in the population.

The variance of a sample, is defined by slightly different formula, and uses a slightly different notation:

where s2 is the sample variance, x is the sample mean, xi is the ith element from the sample, and n is the number of elements in the sample.

Using this formula, the sample variance can be considered an unbiased estimate of the true population variance. Therefore, if you need to estimate an unknown population variance, based on data from a sample, this is the formula to use.

The standard deviation is the square root of the variance…

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5-Number Summary and Boxplots…

Boxplot splits the data set into quartiles. The body of the boxplot consists of a "box" (hence, the name), which goes from the first quartile (Q1) to the third quartile (Q3).

Within the box, a vertical line is drawn at the Q2, the median of the data set. Two horizontal lines, called whiskers, extend from the front and back of the box. The front whisker goes from Q1 to the smallest non-outlier in the data set, and the back whisker goes from Q3 to the largest non-outlier.

OUTLIERS…

Range-

IQR-

Median-

Sometimes, researchers change units (minutes to hours, feet to meters, etc.). Here is how measures of variability are affected when we change units.

CHANGING UNITS

If you add a constant to every value, the distance between values does not change. As a result, all of the measures of variability (range, interquartile range, standard deviation, and variance) remain the same.

On the other hand, suppose you multiply every value by a constant. This has the effect of multiplying the range, interquartile range (IQR), and standard deviation by that constant. It has an even greater effect on the variance. It multiplies the variance by the square of the constant.

Choosing a summary of your data…