Test of Independence: Contingency Tables. Goodness-of-Fit Test for Discrete Random Variables. Difference Between Mean of Two Populations. Test of Hypothesis Concerning the Population Mean. " variance" or " mean" If the first appearance of the word/phrase is not what you are looking for, try F ind Next. Enter a word or phrase in the dialogue box, e.g. Driving speed and gas mileage - as driving speed increases, you'd expect gas mileage to decrease, but not perfectly.To search the site, try Edit | Find in page. Vital lung capacity and pack-years of smoking - as amount of smoking increases (as quantified by the number of pack-years of smoking), you'd expect lung function (as quantified by vital lung capacity) to decrease, but not perfectly. Alcohol consumed and blood alcohol content - as alcohol consumption increases, you'd expect one's blood alcohol content to increase, but not perfectly. Height and weight - as height increases, you'd expect weight to increase, but not perfectly. Some other examples of statistical relationships might include: Indeed, the plot exhibits some " trend," but it also exhibits some " scatter." Therefore, it is a statistical relationship, not a deterministic one. Two p values on excel linear regression which one skin#
There appears to be a negative linear relationship between latitude and mortality due to skin cancer, but the relationship is not perfect. The scatter plot supports such a hypothesis. You might anticipate that if you lived in the higher latitudes of the northern U.S., the less exposed you'd be to the harmful rays of the sun, and therefore, the less risk you'd have of death due to skin cancer. is included in the data set even though it is not technically a state.) ( skincancer.txt) (The data were compiled in the 1950s, so Alaska and Hawaii were not yet states, and Washington, D.C. The response variable y is the mortality due to skin cancer (number of deaths per 10 million people) and the predictor variable x is the latitude (degrees North) at the center of each of 49 states in the U.S. Here is an example of a statistical relationship. Instead, we are interested in statistical relationships, in which the relationship between the variables is not perfect. This course does not examine deterministic relationships.
Boyle's Law: For a constant temperature, P = α/ V, where P = pressure, α = constant for each gas, and V = volume of gas.įor each of these deterministic relationships, the equation exactly describes the relationship between the two variables. Ohm's Law: I = V/ r, where V = voltage applied, r = resistance, and I = current. 
Hooke's Law: Y = α + β X, where Y = amount of stretch in a spring, and X = applied weight. Here are some examples of other deterministic relationships that students from previous semesters have shared: That is, if you know the temperature in degrees Celsius, you can use this equation to determine the temperature in degrees Fahrenheit exactly. As you may remember, the relationship between degrees Fahrenheit and degrees Celsius is known to be: Note that the observed ( x, y) data points fall directly on a line. Here is an example of a deterministic relationship. Types of relationshipsīefore proceeding, we must clarify what types of relationships we won't study in this course, namely, deterministic (or functional) relationships. In contrast, multiple linear regression, which we study later in this course, gets its adjective "multiple," because it concerns the study of two or more predictor variables. Simple linear regression gets its adjective "simple," because it concerns the study of only one predictor variable. The other terms are mentioned only to make you aware of them should you encounter them. The other variable, denoted y, is regarded as the response, outcome, or dependent variable.īecause the other terms are used less frequently today, we'll use the " predictor" and " response" terms to refer to the variables encountered in this course. One variable, denoted x, is regarded as the predictor, explanatory, or independent variable. Simple linear regression is a statistical method that allows us to summarize and study relationships between two continuous (quantitative) variables:
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