Advertisements playa very important role in forming attitudes/preferences for brands. Often advertisers use celebrity spokespersons as a credible source to influence consumers’ altitudes and purchase intentions.

Another type of source credibility is corporate credibility. which can also influence consumer reactions to advertisements and shape brand attitudes. In general. it has been found that forlow-involvement products, attitude toward the advertisement mediates brand cognition.(beliefs about the brand) and attitude toward the brand. What would happen to the effect of this mediating variable when products are purchased through a home shopping network? Home Shopping Budapest in Hungary conducted research to assess the impact of advertisements toward purchase.Asurvey was conducted where several measures were taken, such as attitude toward the product, attitude toward the brand. attitude toward the ad characteristics, brand cognitions, and so on. It was hypothesized that in a home shopping network. advertisements largely determined attitude toward the brand. In order to find the degree of association of altitude toward the ad with both attitude toward the brand and brand cognition. a partial correlation coefficient could be computed. The partial correlation would be calculated between altitude toward the brand and brand cognition after controlling for the effects of attitude toward the ad on the two variables. If attitude toward the ad is significantly high. then the partial correlation coefficient should be significantly less than the product moment correlation between brand cognition and attitude toward the brand. Research was conducted that supported this hypothesis.

Then Saatchi & Saatchi designed the ads aired on Home Shopping Budapest to generate positive attitude toward’ the advertising, and this turned out to be a major competitive weapon for the net Work

The partial correlation coefficient is generally viewed as more important than the part correlation coefficient because it can be used to determine spurious and suppressor effects. The product moment correlation. partial correlation. and the part correlation coefficients all assume that the data are interval or ratio scaled. If the data do not meet these requirements. the researcher should consider the use of non metric correlation.

**Nonmetric Correlation**

At times. the researcher may have to compute the correlation coefficient between two variables that are non metric it may be recalled that non metric variables donot have interval or ratio scale properties and do not assume a normal distribution. If the non metric variables are ordinal and numeric. Spearman’s rho, Ps. and Kendall’s tau. T, are two measures of non metric correlation that can be used to examine the correlation between them. Both these measures use rankings rather than the absohlte values of the sariables and the basic concepts underlying them are quite similar. Both vary from -1.0 to 1.0

The product moment as well as the partial and part correlation coefficients provide a conceptual foundation for bivariate as well as multiple regression analysis

**Regression Analysis**

Regression analysis is a powerful and flexible procedure for analyzing associative relationships between a metric dependent variable and one or more independent variables. It can be used in the following ways

Although the independent variables may explain the variation in the dependent variable. this does not necessarily imply causation. The use of the terms dependent or criterion variables. and independent or predictor variables, in regression analysis arises from the mathematical relationship between the variables. These terms do not imply that the criterion variable is dependent on the independent variables in a causal sense. Regression analysis is concerned with the nature and degree of association between variables and does not imply or assume any causality

**Conducting Bi variate Reqression Anolysis**

The steps involved in conducting bivariate regression analysis are described in Figure 17.2. Suppose the researcher wants to explain attitudes toward the city of residence in terms of the duration of residence (see Table 17.1). In deriving such relationships. it is often useful to first examine a scatter diagram.

**Plot the Scatter Diagram**

A scatter diagram. or scatter gram. is a plot of the values of two variables for all the cases or observations. It is customary to plot the dependent variable on the vertical axis and the independent variable on the horizontal axis. A scatter diagram is useful for determining the form of the

relationship between the variables. A plot can alert the researcher to patterns in the data, or to possible problems. Any unusual combinations of the two variables can be easily identified.

A plot of Y (attitude toward the city) against X (duration of residence) is given in Figure 17.3. The points seem to be arranged in a band running from the bottom left to the top right. One can see the pattern: As one variable increases, so does the other. It appears from this scatter gram that the relationship between X and Y is linear and could be well described by a straight line. However, as seen in Figure 17.4, several straight lines can be drawn through the data. How should the straight line be fitted to best describe the data?

The most commonly used technique for fitting a straight line to a scatter gram is the least-squares procedure. This technique determines the best-fitting line by minimizing the square of the vertical distances of all the points from the line and the procedure is called ordinary least squares (OLS) regression. The best-fitting line is called the regression line. Any point that does not fallon the regression line is not fully accounted for. The vertical distance

from the point to the line is the error, ej (see Figure 17.5). The distances of all the points from the line are squared and added together to arrive at the sum of squared errors, which is a measure of total error, Ie}. In fitting the line, the least-squares procedure minimizes the sum of squared errors. If Yis plotted.on the vertical axis and X on the horizontal axis, as in Figure 17.5, the best-fit ling line is called the regression of Yon X, because the vertical distances are minimized.

The scatter diagram indicates whether the relationship between Yand X can be modeled as a straight line and, consequently, whether the bivariate regression model is appropriate.

**Formulate the Bivariate Regression Model**

In the bivariate regression model, the general form of a straight line is

where

Y = dependent or criterion v,ariable

X = independent or predictor variable .

130= intercept of the line

131= slope of the line

This model implies a deterministic relationship, in that Yis completely determined by X The 0 value of Ycan be perfectly predicted if f30 and f31 are known. In puuketing research, however, very few relationships are deterministic. So the regression procedure adds an error term to account for, the probabilistic or stochastic nature of the relationship. The basic regression equation becomes

where ej is the error term associated with the ith observation.s Estimation of the regression parameters, f30 and (3)o is relatively simple

**Estimate the Parameters**

-In most cases, f30 and f31 are unknown and are estimated-from the sample observations using the equation

where fj is the estimated or predicted value of Y;oand a and b are estimators of f30 and f3I’ respectively. The constant b is usually referred to as the nonstandardized regression coefficient. It is the slope of the regression line and it indicates the expected change in Ywhen X is changed by one unit. The formulas for calculating a and b are simple.? The slope. b, may be computed in terms of the covariance between X and Y. (COY.\)’). and the variance of X as:

Note that these coefficients have been estimated on the raw (un transformed) data. Should standardization of the data be considered desirable. the calculation of the standardized coefficients is also straightforward