Discussion:
Response 1:
Nearing the completion of their portfolio, Big D Incorporated has tasked me with clarifying the variables associated with the decision of expansion into a new market. To ensure the recommendations, applying a regression model to aid in forecasting the monthly sales on the expansion is needed for the Board of Directors to continue with the process of growth for the company. Confirming that Big D Incorporated is following the appropriate steps for this expansion is key. Within this discussion board, I will be providing my research on regression models and advising on which one is best for Big D Incorporated.
Regression models analyze estimates on the relationship among two or more variables. For instance, two variables for Big D Incorporated would be the estimated progression in sales based on the economic circumstances in the new location. We have discovered from our research the amount of household income in the new location to be greater in this new location. From utilizing regression analysis, we will find substantial relationships among independent and dependent variable, which will reveal the depth of influence of numerous independent variables on a dependent variable. Utilizing regression testing will let us to link and contrast the influences of variables calculated on dissimilar scales, such as the influence of price adjustments and the amount of advertising events. This will aid us in excluding and assessing the greatest set of variables to be utilized for creating projecting models (Ray, 2015).
There are several forms of regression methods available to aid in forecasting the monthly sales. For Big D Incorporated, I decided to utilize the linear regression technique. Within this model, the dependent variable is continual and independent variables can be discrete or continuous, and the nature of regression line is linear. Within this regression method, it creates an association among the dependent variable (Y) and one or several independent variables (X) by utilizing a regression line. In the equation Y = a + b * X + e, each element is represented by either a (intercept), b (slope of the line), and e (error). Utilized for predicting the significance of the target variable based on the given predictor variables, this equation compares the differences among simple and multiple linear regression (Linear Regression, 1998).
This regression allows us to estimate and make suggestions regarding population slope factors. Overall the main purpose of establishing a suitable and sound regression analysis tool is to estimate the fundamental effect on Y of a unit change in X. With regression, an equation is developed to calculate the values of a dependent variable. We could plot the monthly sales data by utilizing linear regression over a year to demonstrate and create a sales forecast. This can be achieved by displaying a relationship among two variables with a linear equation. The evaluation consists of graphing a line above a group of data points that generally fits the total shape of the data. A regression reveals the degree to which modifications in a dependent variable (y-axis), which can be attributed to the modifications in an explanatory variable (x-axis). This form of regression can be utilized to assess trends and make estimates or forecasts (Linear Regression, 1998). So, for Big D Incorporated, we will be focusing on the household income of residents within the region and economy within this region. With correlation, we compute to calculate the nature of the relationship among variables. For instance, we can see the potential sales for Big D Incorporate with the quarterly sales on the y-axis and time on the x-axis would create a line that would depict the rising trend in sales. After generating the trend line, the Big D Incorporate would utilize the slope estimated to project the sale in the upcoming months.
References
Linear Regression. (1998).
Response 2:
Big D Incorporated is nearing completion of its portfolio. They need some recommendations and identify the variables. According to Agravante,(2018) " a variable represents a measureable attribute that changes or varies across the experiment whether comparing results between multiple groups, multiples people or even when using a single person in an experiment conducted over time" (para.1). At the beginning of research, we have tried to figure out how they outdoor sporting goods can provide the new products and increase the sale revenue. So, the sale revenue will be the variable for Big D incorporation to make an experiment.
The sale revenue will be identified as the dependent variable. So, the independent variable might be the materials. For example, if we buy a shoe or the hat, we will be looking for some materials that make us feel more comfortable when we are using. Also, some of materials can have a water resistance and include the recycled materials which is make the shoes become more efficient and easier for using. One of the independent variables for outdoor sporting goods is the target market. For example, we want to open the new marketplace with the climbing shoe, so, we will want to know the connection of the target market and the new products. Who will buy these shoes? Age? Young or old? 20 to 30 or 35 -45 years old. And then we can conduct the results to understand the information.
In my opinion, I think the regression line is the most useful tool for this circumstance. It is because the regression line can show the relationship between the dependent variable and independent variables (Gallo, 2015). In this regression line, we can clearly understand the information, which is telling the trend, and predict the tendency of the sale. Before attempting to fit a model to determine the data, we can use this model to identify whether or not there is the relationship between independent and dependent variables. Also, the fitting a linear regression can measure the numerical connection of the data and value the strength of the connection.
References
Agravante, M. (2018). What is meaning of variables in research? Sciencing.
Gallo, A. (2015). A refresher on regression analysis. Harvard business review.