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# A- Answer with 400 words, APA format and at least two peer reviewed We utilize sample data in many applications in business.

A-  Answer with 400 words, APA format and at least two peer reviewed

We utilize sample data in many applications in business. For example, if you are trying to explore or understand relationships between or among business variables to build a better model for sales.

Discuss how: (1) a straight-line relationship can be estimated; (2) how to model exponential growth; and (3) what Correlation is and what it can be used for? What is multiple regression and how it can be used for business decision making? How can you run in in Excel?

B-Answer with 400 word APA format and references;  What is PowerPivot & DAX? What can they be used for? How can you run each in Excel?

C-Reply to these forum posts with 250 words:

1-Linear relationships, as the name suggests are simply variable relationships that are correlated in a line. In order for a relationship to be linear, it must be a straight line, must be in the first power, and cannot have more than 2 variables (study, 2016). Linear relationships can be estimated by using the least-squares method, which squares the variance of the y variable and sums the squares. The resulting correlation allows the user to determine a linear relationship that best fits the patter for a pair of values. This method gives the smallest possible average of deviation for each data point on the line.

In Excel, the best way to accomplish this is to plot your independent variable on the x axis of a scatter plot, and your dependent variable on the y axis of your scatter plot. The resulting graph should give you a good idea of how your variables are related. Taking this one step further, the user can then add a trend line, which will represent your linear relationship. If you determine that your relationship between the two variables is exponentially related, then you can choose an exponential trend line instead which will then show a curved relationship scatter plot.

Linear relationships are a result of correlation, or in other words, how to variables are linked together. It is important to note though that correlation is not causation. Basically, there may be a relationship between two variables, but the relationship may not be explained by causation because other factors may be affecting the dependent variable. However, when determining correlation, it is important to note if the correlation is positive or negative. The correlation between two variables is always between -1 and 1. A correlation of -1 suggest a negative relationship, and a correlation of 1 suggest a positive correlation. A correlation of 0 suggest no relationship.

Correlation and multiple regressions are important business techniques for determining cause and effect. Correlation is useful for understanding the impact of one variable upon another, and multiple regressions is helpful when determining the cause of multiple independent variables upon a dependent variable.

For example, in my line of work, we use several factors to determine the cause of service calls. The factors include time of day, products the customer has purchased, model of the equipment they use, resolution codes given by the technician, and many other factors. Once inputted into the regression model, we can determine the strength of the correlation between each dependent variable to see what had the biggest impact on the volume of service calls.

2-A straight-line relationship can is simply a straight line and is usually between two variables and this can go from very simple to extremely complicated functions. Linear is generally used in mathematics to explain a model that is in a straight line. When attempting to predict the relationship in a linear regression or straight line the analyst is forming the dependent variable. The independent variable is what is used for the prediction process. If I were looking for a relationship in manufacturing tires, the amount made by the warehouse would be the independent variable and the cost of operating the warehouse would be the dependent variable. You would then graph this using the amount made on the x-axis and the cost of operating on the y-axis.

Exponential growth happens when the current value is proportional to the growth rate, its growth happens over time serving as the exponential function. Say, time and revenue, where time would be the x and revenue would be the y value. Over time the revenue will grow, or sometimes, decay. These types of situations to track and see where progress is being made or not being made can help aid in making strategic business decisions in a company. Correlation is a relationship between two variables such as looking at the linear relationship between x and y. The way to display this is always between negative 1 and positive one. Using correlations can show the relationship between time and revenue.

Linear regression creates an extension of itself into multiple regressions which will predict a value of two or more variables. This can be used to understand multiple relationships between independent and dependent variables. Here is a great video that shows how to do multiple regression in excel, I was going to list the specific ways to do it in words but I figured a video would help more than words do. Check out this video, https://www.youtube.com/watch?-bq4M. This is part one, it explains a lot, but to see more of how to do it in excel watch through part 3a and b.

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Impact of Corporate governance on firms performance
IMPACT OF CORPORATE GOVERNANCE ON FIRM'S PERFORMANCE Author’s Name Class Name
Professor’s Name
School
City and State
Date Table of...

BATY
Date
RETURN_ON_ASSET RETURN_TOT_EQY BOARD_SIZE
12/29/2006
12.5766
25.8331
16
12/31/2007
16.0096
31.2591
12
12/31/2008
11.037
24.4365
12
12/31/2009
4.5735
10.4657
13
12/31/2010
10.6438
21.6832...

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