Session

Management Business and Economy

Description

This study examines how different factors, with a particular focus on pricing and advertising expenditure, affect the demand for frozen picas. Pica sales, expressed in units per week, are the dependent variable in this analysis; the independent variables are price (in euros) and advertisement (in euros). In order to support a multivariate regression analysis, data were gathered over a period of 15 weeks. The percentage of the overall variance in pica sales that can be attributed to the pricing and advertising variables taken together was evaluated using the coefficient of multiple determination (R²). This study's analysis of the overall impact of including additional independent variables is a crucial component. There is uncertainty about whether the extra explanatory power justifies the unavoidable loss of one degree of freedom that comes with adding a new X variable. In order to ensure that the explanatory power of the model is not unnecessarily inflated, the modified R² measure accounts for the number of predictors. The presence of a linear relationship between the independent variables (price and advertising) and the dependent variable (pica sales) was assessed using the F-test to determine the model's significance. Therefore, the study is elaborated by null and alternative hypothesis, the null hypothesis (H0) posits no linear relationship, implying that all β coefficients equal zero. In contrast, the alternative hypothesis (H1) suggests that at least one β coefficient is non-zero, indicating a significant effect of one or more independent variables on pica sales. The findings reveal that 44.2% of the variation in pica sales can be attributed to variations in price and advertising, after adjusting for sample size and the number of predictors. Further testing at an alpha level of .05 indicates that the price variable significantly enhances the model when advertising is included; underscoring the importance of both factors in predicting pica sales. This study provides valuable insights for the distributor, highlighting the critical roles of price and advertising in driving demand respectively sales for frozen picas.

Keywords:

Multiple Regression, Production (Frozen Pica), Distributor, Sales, Demand, Price, Advertising.

Proceedings Editor

Edmond Hajrizi

ISBN

978-9951-982-15-3

Location

UBT Kampus, Lipjan

Start Date

25-10-2024 9:00 AM

End Date

27-10-2024 6:00 PM

DOI

10.3107/ubt-ic.2024.14

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Oct 25th, 9:00 AM Oct 27th, 6:00 PM

The Impact of Price and Advertisement on Sales Enlighted through the Coefficient of the Multiple Regression Model for the Frozen Picas Factory

UBT Kampus, Lipjan

This study examines how different factors, with a particular focus on pricing and advertising expenditure, affect the demand for frozen picas. Pica sales, expressed in units per week, are the dependent variable in this analysis; the independent variables are price (in euros) and advertisement (in euros). In order to support a multivariate regression analysis, data were gathered over a period of 15 weeks. The percentage of the overall variance in pica sales that can be attributed to the pricing and advertising variables taken together was evaluated using the coefficient of multiple determination (R²). This study's analysis of the overall impact of including additional independent variables is a crucial component. There is uncertainty about whether the extra explanatory power justifies the unavoidable loss of one degree of freedom that comes with adding a new X variable. In order to ensure that the explanatory power of the model is not unnecessarily inflated, the modified R² measure accounts for the number of predictors. The presence of a linear relationship between the independent variables (price and advertising) and the dependent variable (pica sales) was assessed using the F-test to determine the model's significance. Therefore, the study is elaborated by null and alternative hypothesis, the null hypothesis (H0) posits no linear relationship, implying that all β coefficients equal zero. In contrast, the alternative hypothesis (H1) suggests that at least one β coefficient is non-zero, indicating a significant effect of one or more independent variables on pica sales. The findings reveal that 44.2% of the variation in pica sales can be attributed to variations in price and advertising, after adjusting for sample size and the number of predictors. Further testing at an alpha level of .05 indicates that the price variable significantly enhances the model when advertising is included; underscoring the importance of both factors in predicting pica sales. This study provides valuable insights for the distributor, highlighting the critical roles of price and advertising in driving demand respectively sales for frozen picas.