Predicting Daily Television Viewing of Senior Citizens Using Education, Age and Marital Status

Paul R. Yarnold

Optimal Data Analysis, LLC

Daily television viewing (hours, 6-minute increments), marital status (0=not married; 1=married), age and education (years, integers) data were obtained for a randomly-selected sample of 25 senior citizens. Training analysis predicting viewing (dependent variable) as a linear function of the other (independent) measures by multiple regression analysis identified a statistically significant omnibus effect: F(3,21)=11.7, p<0.0001, R-squared=0.626. Partial F (variable entered last) statistics indicated that marital status [F(1,21)=13.9, p<0.0008] and education [F(1,21)=9.2, p<0.0062] had statistically reliable negative relationships with viewing hours. This model accounted for 5/8 of the variance in television viewing time, however it was unable to make statistically reliable point predictions of viewing times: ESS=7.1, D=194.9, ns. The globally-optimal (GO) novometric model predicting viewing times was: if education<=13 years, then predict viewing 0.7 hours. Training performance (stable in jackknife analysis) was very strong: ESS=90.1, D=0.20, p<0.0001. The model correctly classified 3 of 3 observations having 0.7 or fewer viewing hours, and 20 of 22 (90.1%) with 0.8 or more daily viewing hours.

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Novometric Models of Smoking Habits of Male and Female Friends of American College Undergraduates: Gender, Smoking, and Ethnicity

Paul R. Yarnold

Optimal Data Analysis, LLC

Novometric statistical analyses were used to model smoking habits of one’s male friends, and of one’s female friends, for samples of 3,289 Anglo-American, 944 Mexican-American, and 733 Indian-American college undergraduates. For both analyses the categorical attributes were ethnicity (a multicategorical attribute, dummy-coded using 1-3, respectively), and subject gender (0=female, 1=male) and smoking behavior (0=non-smoker, 1=smoker). The novometric findings are compared with results originally reported for this application obtained using disintegrated chi-square analysis.

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Would One’s Best Boy- or Girl-Friend be More Upset if One Began Smoking: An Exploratory GenODA Model for Anglo-, Mexican-, and Indian-American College Undergraduates

Paul R. Yarnold

Optimal Data Analysis, LLC

Samples of 1,171 male and 1,503 female Anglo-American, 291 male and 503 female Mexican-American, and 138 male and 361 female Indian-American, non-smoking college undergraduates were asked if their best boy-friend or their best girl-friend would be most upset if the subject began smoking. Original analysis using separate chi-square analyses (one design cell for the Indian-American students violated the minimum expectation assumption) concluded: “While the influence of boy-friends or girl-friends on their smoking or non-smoking partners seemed to be rather small, the opposite-sex friend was invariably perceived to be more upset by the possibility of the respondent’s taking up the habit: all these differences were significant beyond the .01 level”. An exploratory GenODA analysis was conducted treating ethnicity as the Gen variable: an ODA model is identified that, when simultaneously and independently applied to each of the Gen groups (dummy-coded as 1-3), explicitly maximizes the lowest ESS obtained across all of the Gen groups. Here the subject’s gender is the class variable, and the gender of one’s most-affected friend is a categorical attribute (gender variables were dummy-coded: female=0, male=1). The omnibus GenODA model was: if Friend=female, predict subject gender=male; otherwise predict subject gender=female: p<0.0001, strong ESS=77.7 (84.9% of actual female and 92.8% of actual male subjects were correctly classified). The GenODA model performed comparably for the Anglo-, Mexican-, and Indian-American samples: all p’s< 0.0001; strong ESS=77.3, 81.3, and 75.1, respectively.

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