General
INTRODUCTION TO STATISTICS
Esquema semanal
16 de febrero - 22 de febrero
Available ResourcesThe following are resources that you may find helpful as you travel through this course. A description is given for each.23 de febrero - 1 de marzo
What is statistics?
2 de marzo - 8 de marzo
Collecting data9 de marzo - 15 de marzo
How do you collect a simple random sample?16 de marzo - 22 de marzo
Graphical displays of data
Histograms
Histograms are a common way to display data. They are similar to bar graphs, which are generally used with categorical data. A histogram is generally used to display quantative data. The bars are connected and each one shows how many values fall in each range.
23 de marzo - 29 de marzo
Stemplots
Stemplots are another useful way to display quantative data and many times are used to build a histogram. If you take your stemplot and rotate 90 degrees and draw rectangles around your data, you will have a histogram.
30 de marzo - 5 de abril
Numerical Descriptions of data
Mean and Median
Mean is also known as average, which you are probably more familiar with. Median is your middle value, similar to the median of a highway being in the middle of the roads.
6 de abril - 12 de abril
Standard deviation
Standard deviation shows the variation or spread of a set of data. For a single set of data it may not show us a whole lot. It is very useful though in comparing sets of data. Such as a comparison of test scores of males versus females. We can tell which group has scores that are more spread out and then explore why one groups scores varied more than the others.
13 de abril - 19 de abril
Boxplots
Boxplots break your data into quarters. The box shows where the middle 50% of your data lies. It also shows if we have outliers (extreme values) in our data. Boxplots are very useful where comparing sets of data.
20 de abril - 26 de abril
Outliers
In this section we will look at how to determine if any of our observations are outliers. Outliers can affect many of our measures, but primarily our mean. If there is an outlier, our mean gets pulled towards it. For example, if a basketball player averages 8 points per game and scores 30 points in a game, his new average will rise considerably due to the outlying value.
