Statistics for Decision Making Bivariate Descriptive Statistics QM Fall 2003 Instructor: John Seydel, Ph.D.

Student Objectives Plot two related quantitative variables on a scatterplot Estimate the equation of the best fitting line through a set of points Relate the equation of a line through a set of points to the relationship between the variables being plotted Use Excel to Determine the best fitting regression line Quantify the fit of a straight line through points Examine the relationship between two qualitative variables

Administrative Chores Homework from last week Return Comments This week’s homework Not being collected Use to study for exam Basis for discussion tonight First exam is next week (descriptive statistics) Chapters 1-3 (selected portions, per assignments) Chapter 11 (pages , ) Excel procedures as discussed in class

Review: Data and Analysis The analyses can be univariate (one variable at a time), bivariate, or multivariate

Let’s Look at the Homework Chapter 1 questions At this point, all you can do is quote the text However, these are good final exam questions Chapter 3: Let’s look at #15 Any others? How about other aspects of the assignment?

Bivariate Analyses: Quantitative Variables Informal analysis Scatterplot Can infer relationship by looking at points Formal analysis Regression Involves formal means of describing plotted line through points Example: Great Northern Insurance

Bivariate Analyses: Qualitative Variables Informal analysis Joint frequency tables (crosstabulation) Pie & bar charts Formal analysis Requires understanding of basic probability Beyond our scope for the moment Example: KIVZ Television

Less important but need to be familiar with: Location  Median  Mode  Quantiles (percentiles, quartiles) Variation  Range  Min and Max  CV Both (?)  Z-score  Empirical Rule Other: skew Let’s look at the homework related to this Miscellaneous Statistics

Another Notation Quiz Mean Sample Population Standard deviation Sample Population Variance Range Number of Observations Elements in the population Slope coefficient Intercept Proportion of variation explained by relationship Average error from using regression equation for prediction

Plot two related quantitative variables on a scatterplot Estimate the equation of the best fitting line through a set of points Relate the equation of a line through a set of points to the relationship between the variables being plotted Use Excel to Determine the best fitting regression line Quantify the fit of a straight line through points Examine the relationship between two qualitative variables SummarySummary of Objectives

Appendix

What is statistics all about? It’s about dealing with variation Summarizing information (description) Making decisions based upon that summarization Type of analysis depends on data type Quantitative Qualitative Description Formal  Numeric data: average and standard deviaiton  Categorical data: percentages Informal: frequency tables and charts data Recall Our Purposes Here