The effects of ethnicity, ethnic salience and ethnic identification on consumers\u27 sources of information and purchase behavior

The purpose of this study was to determine if there were any significant differences in the search and purchase behavior between white and black consumers due to ethnicity, ethnic identification, ethnic salience and ethnic situation. The sample was chosen from two universities, one predominately white, the other predominately black. Of the 360 questionnaires administered, 345 were usable. The sample was representative of the student population of each university with respect to age, gender, and ethnicity. Statistical techniques used were ANOVAs, t-tests and paired comparisons. The findings indicate that ethnicity plays an important role in an individual\u27s sources of information used for purchase decisions and purchase behavior. Statistically significant differences were found between black and white consumers in the sources of information used for purchase decisions and the products they purchased. Black consumers used different sources of information when making purchase decisions, relying heavily on store-related sources and advertising such as television and newspaper. Differences between black and white respondent\u27s perceptions of each other\u27s expenditures were also reported. The study found that black respondents predicted white respondents\u27 purchase behavior better than white respondents predicted black respondents\u27 purchase behavior. Although, ethnic identification was expected to affect an individual\u27s search and purchase behavior no statistically significant differences were found. The effect of ethnic situation on ethnic salience was not statistically significant, but was in the right direction, offering partial support for distinctiveness theory. Limited support for the effect of ethnic situation on purchase behavior was reported

Technology in Statistics Education: Where Have We Been? Where Are We? Where Are We Going?

Three of the revised GAISE guidelines for statistics education are: 1a. Teach statistics as an investigative process of problem-solving and decision-making 2. Integrate real data with context and purpose. 5. Use technology to explore concepts and analyze data. In the beginning, there was no technology (well, there were slide rules…); students were “forced” to add columns of data to compute means (data might be presented in sorted order to find a median). Calculating means and standard deviations literally “by hand” was time- and labor-intensive (and prone to error). This gave rise to “statistics” that are no longer in vogue (midrange and pseudo-standard deviation, anyone?) as well as “realistic” data that made these computations easier. Graphics were only what could be produced by pencil and paper. Along came calculators (that could even compute a linear regression!), computers, and statistical packages. Access was still an issue, however. Today, practically everyone has a computer or smartphone, either of which have more computing power than mainframe computers of the past. Graphics have come a long way and “visualizations” are a current vogue. Web-scraping is possible as a source of “real” data. The internet is bursting with “big” data. How has the accessibility of technology changed how, what, and (especially) who we teach, in introductory statistics courses? This talk will be a look back at the development of technology, courses of the past, a brief survey of where we are now, and some prognostications about the future. [9-12 Teachers, High Ed faculty

Martingales in mark-recapture experiments with constant recruitment and survival

Thesis (Ph.D.) University of Alaska Fairbanks, 1995The method known as mark-recapture has been used for almost one hundred years in assessing animal populations. For many years, these models were restricted to closed populations; no changes to the population were assumed to occur through either migration or births and deaths. Numerous estimators for the closed population have been proposed through the years, some of the most recent by Paul Yip which make use of martingales to derive the necessary estimates. The independently derived Jolly-Seber model (1965) was the first to address the open population situation. That method as originally proposed is cumbersome mathematically due to the large number of parameters to be estimated as well as the inability to obtain estimates until the end of a series of capture events since some of the "observed" variables necessary are prospective. It also is cumbersome for the biologist in the field as individual marks and capture histories are required for each animal. Variations have been proposed through the years which hold survival and/or capture probabilities constant across capture occasions. Models based on log-linear estimators have also been proposed (Cormack 1989). This paper builds on the closed population work of Yip in using martingale-based conditional least squares to estimate population parameters for an open population where it is assumed recruitment of new individuals into the population is constant from one capture occasion to the next, and capture and survival probabilities are constant across capture occasions. It is an improvement over most other methods in that no detailed capture histories are needed; animals are simply noted as marked or unmarked. Performance of the estimator proposed is studied through computer simulation and comparison with classical estimators on actual data sets