Research Methods for Managerial Decisions

a) The regression model was used by Laura to determine predictive values of advertising expenditures on coffee time's price index. Bivariate Regression includes two variables, one explanatory and one response; this regression model determines the predicted relationship between two variables. For example when two variables such as an advertising expenditures and coffee time are found to correlate, the presence of one will predict the presence of another and the regression statistic determines the significance of this effect. Assumptions are that the underlying relationship is linear. Dots in the scatterplot tend to be dispersed equally about all parts of the prediction line referred to the assumption of homoscedasticity. Regression generates a least squares regression line using the following least squares regression equation that produces the best fit linking X to Y: y = a + bX, where a and b are constants, y is the predicted value of Y and X is a specific value of the independent variable. Once a line of best fit is calculated, a score for y can be predicted, based on any score for x. By using regression, the score obtained on x can be matched to a point on the regression line.  This point can then matched to y to obtain the predicted score on y (Witte, 1980). Multiple regression allows for one response variable and several explanatory variables and their predicted relationship. For example

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r. Of those, 110 visited a small café. In percentage terms, those 110 café patrons represent 8.9% of the total tourist population. Laura has claimed that 10% of the tourist population will visit the café and has used this number to justify an increase in the advertising budget. Clearly, the observed value of 8.9% visitation is less than the claimed 10%. The question is whether the observed value is significantly less than the expected value. To determine this, we must first set our level of significance. The standard level for assessing significance is p = .05, meaning that there is a 5% probability that the differences between the observed and expected values are the result of mere chance. However, a level of .05 also means that there is a 95% chance that the observed differences are genuine. Next, we must choose our statistic. The most common statistic in assessing the differences between observed and expected values is the chi square statistic. Chi square analysis examines the differences between observed and expected values and places them on a probability distribution curve to see if the differences are statistically significant. In our case, the observed value was 110 visitors. The expected value was 10%, or 1
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Some common words found in the essay are:
Coffee Time's, Bivariate Regression, Moore McCabe, Regression Model, price index, advertising expenditures, coffee time's price, time's price index, visit café, time's price, chi square, coffee time's, observed value, advertising costs, expected value, cost coffee, Co Witte, , differences observed expected, observed expected values, advertising costs recommended, Rinehart Winston,
Approximate Word count = 1422
Approximate Pages = 6 (250 words per page)