Chapter 12 Descriptive Statistic By the end of this lesson, you will be able to: 1. Depict four graphs of data representation 2. Differentiate between descriptive and inferential statistics 3. Define the normal distribution 4. Calculate three measures of central tendency 5. Calculate three measures of variation 6. Describe, calculate, and interpret a correlation coefficient 7. Describe and interpret a regression equation 8. Contrast mediating and moderating variables 9. State the function of a partial correlation 10. Describe and interpret a multiple correlation and multiple regression 11. Provide four potential issues with describing data Depicting Data Bar Graph: 1. A graph in which bars of varying height with spaces between them are used to display data for variables defined by qualities or categories. Pie Chart: 1. A graphic display in which a circle is cut into wedges, with the area of each wedge being proportional to the percentage of cases in the category represented by that wedge. 2. A pie chart generally works best when there are not many categories (with thin wedges) being shown. 3. A downside of the graphic is that it is not very efficient because it uses significant space to show the frequencies of a single variable. Histogram: 1. A graphical depiction of continuous data using bars of varying height, similar to a bar graph but with blocks on the x-axis adjoining one another so as to denote their continuous nature. 2. Display frequency data. Frequency Polygons 1. A graph depicting a statistical distribution, made up of lines connecting the peaks of adjacent intervals. 2. Graph can become messy. Is this graph is best? Does it contribute to the story of my data? Describing Data Types of Statistics Descriptive 1. Procedures for depicting the main aspects of sample data, without
necessarily inferring to a larger population. 2. Shows the summary of data, describe them only through numerical calculation 3. Measure of central tendency: a) Mean b) Median c) Mode 4. Measure of Variation: a) Range b) SD Inferential 1. A broad class of statistical techniques that allow inferences about characteristics of a population to be drawn from a sample of data from that population while controlling (at least partially) the extent to which errors of inference may be made. 2. Understand the data is represented you as a population scale and compare individual in some way. 3. Somehow make inference or prediction about a population based on sample of data. 4. Z-test 5. T-test 6. ANOVA 7. Chi-Square The Normal Distribution 1. A theoretical distribution in which values pile up in the center at the mean and fall off into tails at either end. 2. When plotted, it gives the familiar bell-shaped curve expected when variation about the mean value is random. 3. Many statistical models assume that data follow a normal distribution. 4. The normal distribution has several primary characteristics: a) It is symmetrical, b) It has both upper and lower asymptotes, and its mean, median, and mode are the same value. 5. Perhaps most