8.1 iscribe trinkets6/18/2023 With cementing extending up to at least the shallowest problematic hydrocarbon-bearing, organic carbon rich or saline aquifer zone all onshore shale gas wells (including exploration wells constructed for the purposes of production testing) be constructed to at least a Category 9 standard (unless it can be demonstrated by performance modelling/assessment that an alternative design would give at least an equivalent level of protection),.The sign for the standardized values are depicted along the axes.įigure 8.That prior to the grant of any further exploration approvals, in consultation with industry and other stakeholders, the Government develops an enforceable code of practice setting out the minimum requirements that must be met to ensure the integrity of onshore shale gas wells in the NT. Because the mean is subtracted from observed values when standardizing, points that fall above the mean will have positive standardized values and points that fall below the mean will have negative standardized values. These scatterplots have dashed lines at the mean of both the x- and y-axis variables. The scatterplots in Figure 8.5 represent different associations. To better understand how r is a measure of association and strength, reconsider the steps in calculating r from Section 8.2.2. However, the correlation coefficient (r Section 8.2.2) is a measure of strength and association between two variables, if the form is linear. Strength is difficult to define from a scatterplot because it is a relative term. For example, if a linear form exists, then strength is how closely the points cluster around the line. Strength is a summary of how closely the points cluster around the general form of the relationship. The meaning and interpretation of r is discussed in more detail in the next section. The sample correlation coefficient, abbreviated as r, is calculated with For example, researchers likely wish to predict area of a lake (hard to measure) from depth of the lake (easy to measure). In many cases, the more difficult variable to measure will likely be the response variable. For example, if the researcher hypothesized that number of mice will be greater if there are more legumes, then number of mice is the response variable. For example, does the number of mice in the field depend on the number of legumes (lots of food=lots of mice) or the other way around (lots of mice=not much food left)? Similarly, does area depend on depth or does depth depend on area of the lake? In these situations, the context of the research question is needed to identify the response variable. In some situations it is not obvious which variable is the response. For example, in the car data, highway MPG is the response variable because gas mileage is most likely affected by the weight of the car (e.g., hypothesize that heavier cars get worse gas mileage), rather than vice versa. One may identify the response variable by determining which of the two variables most likely depends on the other. Thus, the response variable is often called the dependent variable, whereas the explanatory variable is often called the independent variable. In general, the response variable is thought to depend on the explanatory variable. The explanatory variable is the variable that may help explain or allow one to predict the response variable. The response variable is the variable that one is interested in explaining something (i.e., variability) or in making future predictions about. Table 8.1: Sample data from 1993 cars data set. Ultimately, the relationship between highway MPG and the weight of a car is described. Bivariate relationships between two categorical variables is described in Module 10.ĭata on the weight (lbs) and highway miles per gallon (HMPG) for 93 cars from the 1993 model year are used as an example throughout this module. This module is focused on describing the bivariate relationship between two quantitative variables. For example, you may measure (i) the height and weight of students in class, (ii) depth and area of a lake, (iii) gender and age of welfare recipients, or (iv) number of mice and biomass of legumes in fields.
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