Weekly Recap Nov. 2

This week we dove head first in Chapter 5. This mostly encompasses going over OLS regression and Logistic regression. The week before, with the Widgets data we received, we were able to get a first look at OLS regression by looking at a series of linear regressions outing marketing strategies affecting unit sales. OLS regression formally known as ordinary least squares is a way of showing how the dependent variable depends on the movement of independent variable and is minimizes the sum of squared errors. The dependent variables of OLS regressions are continuous. There are two purposes to OLS regression; to explain a relationship between variables and to allow prediction of what might happen in the future. After going through what OLS is and all that is carries, we looked at logistic regression. Logistic regression is very similar in the fact that it has variables, constants, and error but the main different is that its dependent variable is binary which means that the dependent variable can only be one of two options. Most logistic regression dependent variables use the fact that it can be either 1 or 0, no in between or else it is undefined. Logistic regression also uses the aspect of logit which is the likelihood of an event to occur and is measured by dividing 'event' buy (1-event). When calculating the logit, you must compare it to 1 because this means a 50/50 chance of occurring. With all of this insight gained about the two types of regression, we were able to move forward in our Widgets case. This time around our research problem is whether or not a customer is going to buy and what aspects are effecting that purchase. When looking at logistic regression data, there are the same summaries as in OLD; descriptive statistics, correlations, ANOVA, and co-linearity. All of this data is looked at and interpreted the same. In this case about 60% of the dependent variable is explained by the independent variable due to an R2 of 0.582, there is high statistically significance due to a Sig of 0.00, and the regression line for the most part follows the data configuration. Many of the dependent variables are binary or have two choices; Purchaser is did not purchase/purchase, OwidgetB is bought/not bought, PwidgetB is bought/not bought, and NwidgetB is bought/not bought etc. Being binary means the the variable is nominal which further means categorical. The opposite of binary is continuous or a scale variable which could be dollars in this case.
Since I am a pharmaceutical business major, the logistic regression is used in many different ways. It could be whether a drug work or not, if a doctor prescribed or didn't prescribe, or if a patient is sick or isn't sick. I can see that using a logistic regression would be very helpful in this aspect because it is comparing two options to a specific variable effect rather then just comparing them separate or scattered. It creates a connection between option A and B which is more clear and can help be more predictive for future use.

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