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... 1 THE REGRESSION MODEL 10
7. ... ANALYSIS OF THE REGRESSION EQUATION 12
7. ... Descriptive analysis 12
7. ... Empirical analysis 13
8. ... INTRODUCTION AND BACKGROUND-
FIT ELEGANCE LTD. ... FIT ELEGANCE office is located on the third floor of the same building.
Background of the study is that the sales of Fit elegance over the last 23 months varies each months, so our goal is to analyze the sales of each month with respect of advertising cost, number of items and average customer satisfaction.
My research topic is to analyze the sales of Fit elegance of last 2 3 months; I will discuss the factors, which affects the sales. ... I will do the descriptive statistics and regression analysis to analyze the relationships. ...
The second objective of this applied research is to find the relationship customers satisfaction regarding FIT ELEGANCE products, services, sales person, shop interior and parking. ...
FIT ELEGANCE in its first year introduced three main product ranges, which are: Trousers, Blazers, and Suit. ...
FIT ELEGANCE has few product categories for their trousers and also for blazers and suits.
Understanding customers’ needs wants and expectations is a vital aspect for FIT ELEGANCE. ... Satisfaction regarding FIT ELEGANCE
o Quality satisfaction regarding products and services:
§ Satisfaction regarding Trousers
§ Satisfaction regarding Blazers and suits
§ Satisfaction regarding salesperson
§ Satisfaction regarding shop interior
§ Satisfaction regarding parking
For the ease of understanding of the study and to build clear research objectives, the stated factors have been averaged for each person and for each month. ... Primary source of my data was sales reports of Fit elegance, monthly order of stocks. ... For the simplicity of analysis I took data from the secondary sources. ... Fit elegance advertises through agent so from the file of the agent. ... 1 THE REGRESSION MODEL
the regression model is the probability model describing how Y is related to X. ...
The main goal in statistical regression analyses is to estimate the parameters of the regression equation and predict the variable of interest on the basis of my known or assumed values of independent variables. ...
Model-
The general form of the regression model is
Y = b0 + b1X1 + b2X2 + b3X3 + . ... + bkXk + e
Where
Y is the dependent variable,
b0 is the intercept,
bi are the regression coefficients, where i = 1, 2, . ... the equation that describes how the mean value of Y is related to X, is called the Linear Regression Equation
E(y)= = b0 + b1X1 + b2X2 + b3X3
The values b0, b1, b2, b3 are unknown so it must be estimated by using sample data. Sample statistics can be denoted as b0, b1, b2 and b3 so the estimated regression equation is
ŷ= b0+b1x1+b2x2+b3x3
The estimates of the parameters in the model are
b0 - the Y intercept or the value of Y when all the Xi values are zero,
bi – the regression coefficient or the measure of the rate of change in Y per unit change in Xi, holding the other variables constant. ... ANALYSIS OF THE REGRESSION EQUATION
7. ... Descriptive analysis
In descriptive statistics, each variable has a mean, standard error, median, mode, standard deviation, simple variance, minimum & maximum and range. ... Empirical analysis
In table-3 the value of b0, b1, b2 and b3 are expressed as the coefficient values of intercept, X1, X2 and X3 respectively. ... The computer generated matrix of correlation coefficient is as follows:
Interpretation of the results: - By looking the regression equation, which is mentioned above of the table, we can check to see if the regression equation agrees with the thesis proposed in the Conceptual model Section of the paper, Coefficient of X1 is b1 was assumed that it would be positive, which is found positive.
Approximate Word count = 2978
Approximate Pages = 11.9
(250 words per page double spaced)
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