The Influence of the Cost of a Cookie on Its Taste Perception - Term Paper Example

The Effect of Cost on Taste Perceptions The current study deals with the effects of cost on the taste ratings of various cookie brands. The researcher then bought different cookie brands, and randomly chose respondents to undertake the taste test. Their consent was asked on whether or not they would like to participate in a study of taste perceptions. The cookie brands which were chosen are as follows: Starbucks, Dunkin Donuts, Millies Cookies, Mrs. Fields, Chips Ahoy, Oreo, and Kraft chocolate chip cookies. These are priced at $4, $3.50, $11, $16, $14, $18, and $17.50, respectively. 70 respondents were asked to taste test a cookie and give a taste rating on a 10-point Likert scale, with 1 representing the least pleasurable taste to 10 which indicates the best taste. The column of the independent variable was composed of the scores on cost, while the column for the dependent variable is made up of scores on taste. These were then analyzed using linear regression, utilizing SPSS software. The following is a quote about linear regression as a statistical tool: “Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. For example, a modeler might want to relate the weights of individuals to their heights using a linear regression model. Before attempting to fit a linear model to observed data, a modeler should first determine whether or not there is a relationship between the variables of interest. This does not necessarily imply that one variable causes the other (for example, higher SAT scores do not cause higher college grades), but that there is some significant association between the two variables. A scatterplot can be a helpful tool in determining the strength of the relationship between two variables. If there appears to be no association between the proposed explanatory and dependent variables (i.e., the scatterplot does not indicate any increasing or decreasing trends), then fitting a linear regression model to the data probably will not provide a useful model. A valuable numerical measure of association between two variables is the correlation coefficient, which is a value between -1 and 1 indicating the strength of the association of the observed data for the two variables. A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0)” (Yale Statistics, 2005). The study was thus designed to answer the following question: Is the cost of a cookie a predictor of the taste perception?” The following section discusses the results of the study: Results Table 1. Regression statistics. Regression Statistics Multiple R 0.24 R Square 0.06 Adjusted R Square 0.04 Standard Error 2.80 Observations 70.00 The regression table above shows that multiple R is 0.24, and R square is .06. The more important figure is adjusted R square at .04. This suggests that 4% of the variance in taste perceptions are accounted for by its perceived cost. The standard error is at 2.80. There are 70 respondents who participated in the study. Table 2. ANOVA results.   df SS MS F Significance F Regression 1 32.981 32.981 4.209 0.044 Residual 68 532.791 7.835 Total 69 565.771       The foregoing table shows that cost does have a significant effect on taste perceptions (F=4.209, p Read More