I’m working on a accounting multi-part question and need an explanation to help me learn.
Respond to the following in a minimum of 175 words:
Models help us describe and summarize relationships between variables. Understanding how process variables relate to each other helps businesses predict and improve performance. For example, a marketing manager might be interested in modeling the relationship between advertisement expenditures and sales revenues.
Consider the dataset below and respond to the questions that follow:
Coleen Maciel
Respond to the following in a minimum of 125 words:
Hello Professor and Class-
What is meant by variable being related to each other is by the value of variable communicating to the value of another variable for each of the case in the data set. If you will, knowing the variable of one case can help one guess the value of the other variable. The fitted regression model is sales =-25.1682 + 4.9216 advertisement. The slope will come out to 4.9216 for every dollar that is used on advertising. The corresponding would be 4.9216 in increase sales. The seize would be 25.1682. Say that there are no dollars being used on advertisement then the sales that were predicted will come out to -25.1682. The regression coefficient value r 0.8237. The coefficient determination r^2 is 0.6785. This r^2 show that 67.85% variation in sales is explained by the listed model. This show the sales and company spends about $950,000 in advertisement. The calculated sales =-25.1682+4.9216*950=4650.3518. Model is underestimated.
Thanks,
Coleen
Lauren Saenz
NEW
For this week’s discussion we are to summarize the relationship between variables. Knowing this process helps us relate variables to one another to predict business outcomes and improve the business’s performance. For this week – the fitted regression model is sales = -25.1682+4.9216advertisement. We can calculate the slope is 4.9216 for every dollar that is spent on advertising. With that being said there is a corresponding 4.9216 increase in the sales. The intercept of this is 25.1682. I no dollar is ending up being spent on advertisement then the sales we predicted will be -25.1682. The regression coefficient value r is 0.8237. The coefficient determination r^2 is 0.6785. This r^2 lets us know that about 67.85% variation in sales is explained by the above model. This predicts sales and business spends about $950,000 in advertisement alone. This is calculated sales = -25.1682+4.9216*950 = 4650.3518. This model underestimated.
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