"A graphical display, when used appropriately, can be a powerful tool for organizing and summarizing data. By sacrificing some of the detail of a complete listing of a data set, important features of the data distribution are more easily seen and more easily communicated to others." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)
"A histogram for discrete numerical data is a graph of the frequency or relative frequency distribution, and it is similar to the bar chart for categorical data. Each frequency or relative frequency is represented by a rectangle centered over the corresponding value (or range of values) and the area of the rectangle is proportional to the corresponding frequency or relative frequency." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)
"A time-series plot (sometimes also called a time plot) is a simple graph of data collected over time that can be invaluable in identifying trends or patterns that might be of interest.A time-series plot can be constructed by thinking of the data set as a bivariate data set, where y is the variable observed and x is the time at which the observation was made. These (x, y) pairs are plotted as in a scatterplot. Consecutive observations are then connected by a line segment; this aids in spotting trends over time." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)
"A unimodal histogram that is not symmetric is said to be skewed. If the upper tail of the histogram stretches out much farther than the lower tail, then the distribution of values is positively skewed or right skewed. If, on the other hand, the lower tail is much longer than the upper tail, the histogram is negatively skewed or left skewed." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)
"A well-designed experiment requires more than just manipulating the explanatory variables; the design must also eliminate other possible explanations or the experimental results will not be conclusive." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)
"Be careful not to confuse clustering and stratification. Even though both of these sampling strategies involve dividing the population into subgroups, both the way in which the subgroups are sampled and the optimal strategy for creating the subgroups are different. In stratified sampling, we sample from every stratum, whereas in cluster sampling, we include only selected whole clusters in the sample. Because of this difference, to increase the chance of obtaining a sample that is representative of the population, we want to create homogeneous groups for strata and heterogeneous (reflecting the variability in the population) groups for clusters." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)
"Bias in sampling is the tendency for samples to differ from the corresponding population in some systematic way. Bias can result from the way in which the sample is selected or from the way in which information is obtained once the sample has been chosen. The most common types of bias encountered in sampling situations are selection bias, measurement or response bias, and nonresponse bias." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)
"Descriptive statistics is the branch of statistics that includes methods for organizing and summarizing data. Inferential statistics is the branch of statistics that involves generalizing from a sample to the population from which the sample was selected and assessing the reliability of such generalizations." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)
"Pie charts can be used effectively to summarize a single categorical data set if there are not too many different categories. However, pie charts are not usually the best tool if the goal is to compare groups on the basis of a categorical variable." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)
"Populations with no variability are exceedingly rare, and they are of little statistical interest because they present no challenge! In fact, variability is almost universal. It is variability that makes life (and the life of a statistician, in particular) interesting. We need to understand variability to be able to collect, describe, analyze, and draw conclusions from data in a sensible way." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)
"[… ] statistics is about understanding the role that variability plays in drawing conclusions based on data. […] Statistics is not about numbers; it is about data - numbers in context. It is the context that makes a problem meaningful and something worth considering." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)
"Statistics is the scientific discipline that provides methods to help us make sense of data. Statistical methods, used intelligently, offer a set of powerful tools for gaining insight into the world around us." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)
"The goal of random sampling is to produce a sample that is likely to be representative of the population. Although random sampling does not guarantee that the sample will be representative, it does allow us to assess the risk of an unrepresentative sample. It is the ability to quantify this risk that will enable us to generalize with confidence from a random sample to the corresponding population." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)
"The use of the density scale to construct the histogram ensures that the area of each rectangle in the histogram will be proportional to the corresponding relative frequency. The formula for density can also be used when class widths are equal. However, when the intervals are of equal width, the extra arithmetic required to obtain the densities is unnecessary." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)
No comments:
Post a Comment