- PROVIDE AND ANALYSIS ON Statistical Techniques in Business & Economics, Ch. 13: Correlation and Linear Regression
2. 2. Chapter 13 of Statistical Techniques discusses correlation and linear regressions. By comparing several variables we are able to see if there is a relationship between the data or if it is unrelated. Using a scatter diagram would be the easiest method to visually see this. This is very useful chapter for me to help analyze our sales. For example, I can look into if length of appointment times has any correlations to our optical sales and if they affect our unit sale per patient numbers. While we want to ensure the best care, we need to be mindful of our patients time while also meeting our sales goals.
3. PROVIDE AND ANALYSIS ON Statistical Techniques in Business & Economics, Ch. 14: Multiple Regression Analysis
4. 4. In the stats in action article of the text on page 470 it addresses that women make 70% of what men make for equal roles. I am not a feminist but I have studied why women make less than men and my conclusion is that it’s a woman fault in most cases that she makes less than a man. Women accept less and they don’t negotiate without fear when they negotiate their salary. I read a book by Sheryl Sandburg called “Lean In” and it states women lean out of their careers rather than lean into their careers and they say that its because they value flexibility to raise a family the reason they accept less. It’s a great book that changes the way I think about this topic. I have worked for an employer that underpaid me in comparison to my male counterparts and I myself accepted this because I was pregnant and wanted the flexibility to leave when I needed to take care of a child. Right or wrong I was the person who accepted this and didn’t leave the company till I wanted more.
5. 5. The weekly video on correlation and causality points out how as a reader of a study we need to understand the difference between cause and effect relationships. The article in the video stated a few claims that were correlated not causalities. For example people who ate more breakfast were less overweight then breakfast skippers. This statement is implying that skipping breakfast causes overweight or obesity. That is not what this research concluded. This research concluded that there is a correlation between skipping breakfast and weight gain. These two variables are observed at the same time but certainly the study does not prove one cause the other. It is a very different way to interpolate the article.V
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Statistical Techniques discusses correlation and linear regressions. By comparing several variables we are able to see if there is a relationship between the data or if it is unrelated.