There are 3 major questions that the logistic regression analysis answers – (1) causal analysis, (2) forecasting an outcome, (3) trend forecasting.
The first category establishes a causal relationship between one or more independent variables and one binary dependent variable.
Medicine: Do body weight calorie intake, fat intake, and age have an influence on heart attacks (yes vs. no)? To answer this question the researcher would measure body weight, fat and calorie intake, as well as age, and whether the patient got a heart attack during the time-frame of the study. The analysis can then show whether the independent variables have an effect on whether to get a heart attack or not (dependent variable).
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Biology: Does the herbicides and oxygen levels in water kill plants? The research team would measure different concentrations of herbicides and oxygen in the water used to water the plants and then observe whether the plants die or not. Logistic regression analysis establishes whether a causal relationship between the independent and dependent variables exist. Logistic regression analysis is particularly useful to test observations made in experimental conditions such as this – here the levels of oxygen, and herbicides in the water are deliberately manipulated to test their effects on growth.
Management: Do customer satisfaction, brand perception and price perception influence purchase decision? The research team would ask customers to rate their satisfaction and perceptions of brand and price, as well as observe their actual purchase (e.g., via a coupon’s bar code). The logistic regression analysis can then prove the assumed causal relationship of satisfaction and perceptions on purchase behavior. Logistic regression is especially suited for these questions, because logistic regression can handle ordinal data (satisfaction, loyalty, or perception are typically rated Likert-scaled).
Psychology: Is depression influenced by personality traits? To answer this question the team of researchers would measure anxiety (e.g. BDI) and personality trait (e.g., the big 5). As it is common in the clinical practice cut-off values are used to classify the results of BDI. Logistic regression analysis can be used to test whether there is a causal link between those variables. However, logistic regression does not prove that the causal direction is from depression to personality or the other way around.
Secondly, logistic regression can be used to forecast the outcome event.
Medicine: Will a subject who smokes X cigarettes a day and who works out for Y hours a day get lung cancer? The research team can observe smoking and activity habits as well as whether the subject gets or does not get lung cancer. Thus logistic regression can tell researchers what lifestyle is more likely to get lung cancer.
Biology: Will 5 additional weeks of sunshine and 100mm of rain burst the vine grapes? In a sample sunshine duration, the rainfall is measured and also whether a grape pops or not under these conditions. Logistic regression can then be used to estimate the logit function of the grapes bursting. The resulting logistic regression equation can tell what the probability of the grapes bursting is for which combination of rain and sunshine.
Thirdly, logistic regression analysis can be used to predict changes in probabilities.
Medicine: How does the probability of getting lung cancer change for every additional pound of overweight and for every X cigarettes smoked per day? The researchers observe average daily cigarette consumptions, overweight and whether the patient got lung cancer or not. Logistic regression analysis can then be used to predict the changes in probability, e.g., for every cigarette life increases the probability of lung cancer by +2%; every pound overweight by +32%. Statistics Solutions can assist with your quantitative analysis by assisting you to develop your methodology and results chapters. The services that we offer include:
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