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Affordable Housing Resource Center

Fed officials, including Chairman Jerome Powell , have raised interest rates twice this year and have pointed to two more before the end of 2018.

Trump, in an interview with CNBC, said he does not approve, even though he said he “put a very good man in” at the Fed in Powell.

“I’m not thrilled,” he told CNBC’s Joe Kernen in an interview to air in full Friday at 6 a.m. ET on “ Squawk Box .” “Because we go up and every time you go up they want to raise rates again. I don’t really — I am not happy about it. But at the same time I’m letting them do what they feel is best.”

Totally aside from the fact that he’s not supposed to say that sort of thing, Trump’s ambivalent expressions about interest rates over time, but also sometimes (as today), within the same breath, are highly disruptive to markets for whom monetary policy is extremely important. But he doesn’t seem to care about that:

“Now I’m just saying the same thing that I would have said as a private citizen,” he said. “So somebody would say, ‘Oh, maybe you shouldn’t say that as president.’ I couldn’t care less what they say, because my views haven’t changed.”

They have changed, of course, and it’s alarming that the president doesn’t understand his wandering opinions on this sensitive topic matter more than they did when he was a mere real estate mogul and reality-show host.

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Table 1. List of U.S. states that participated in BRFSS 1984–2015 and administered physical activity questionnaire.

https://doi.org/10.1371/journal.pone.0168175.t001

Individual characteristics

Various individual characteristics have been linked to physical activity, such as sex [ 20 ], age [ 21 ], race/ethnicity [ 22 ], education level [ 23 ], employment status [ 24 ], and marital status [ Outlet Best Sale Ruffled Yoke Striped Cotton Shirt Womens Blue White Sea New York Buy Cheap Reliable Finishline 100% Authentic Cheap Online opAknB
]. Temporal changes in the distributions of these individual characteristics among the state population may confound in the estimated prevalence of physical inactivity. Therefore, we controlled these individual characteristics in logistic regressions: a dichotomous variable for female (male as the reference group), 2 continuous variables for age in years and age in years squared (to account for potential nonlinear relationship between leisure-time physical inactivity and age), 4 categorical variables for race/ethnicity (non-Hispanic black, non-Hispanic Asian or Pacific Islander, non-Hispanic other race or multi-race, and Hispanic, with non-Hispanic white as the reference group), 4 categorical variables for education attainment (some high school, high school graduate or equivalent, some college or equivalent, and college graduate or higher, with primary school or lower as the reference group), 6 categorical variables for employment status (unemployed for a year or less, unemployed for over a year, homemaker, student, retired, and unable to work, with employed as the reference group), and 2 categorical variables for marital status (divorced or widowed or separated, and never married, with married as the reference group).

Statistical analyses

Prevalence of leisure-time physical inactivity was estimated for each U.S. state and survey year, accounting for the BRFSS sampling design. Based on its empirical distribution, the estimated state- and year-specific prevalence of leisure-time physical inactivity was classified into 5 categories—lower than 25%, 25% to less than 30%, 30% to less than 35%, 35% to less than 40%, and 40% or higher.

Separate logistic regressions were conducted to estimate the changes in the prevalence of leisure-time physical inactivity over the BRFSS survey period for each U.S. state, adjusting for individual characteristics (i.e., sex, age, race/ethnicity, education, employment status, and marital status). The key independent variables were a series of categorical variables for survey years, with the first survey year as the reference group (i.e., baseline). For instance, Virginia was surveyed by the BRFSS regarding leisure-time physical activity during 1989–2015, and thus had 26 categorical variables denoting each survey year from 1990 to 2015, with the survey year of 1989 as the baseline. Average marginal effects were calculated based on the estimated coefficients from logistical regressions. The use of average marginal effect converted odds ratio to change in probability (i.e., prevalence) relative to the baseline.

All statistical analyses were conducted using Stata 14.2 SE version (StataCorp, College Station, TX). The BRFSS sampling design was accounted for in both descriptive statistics and regression analyses. More specifically, Stata’s survey functions “svy” were used to incorporate BRFSS sampling strata, primary sampling unit, and sampling weight in estimation. Average marginal effects were calculated using the Stata function “margins”.

Human subject protection

The BRFSS was approved by the National Center for Health Statistics Research Ethics Review Board. This study used the BRFSS de-identified public data and was deemed exempt from human subjects review by the University of Illinois at Urbana-Champaign Institutional Review Board.

Fig 1 illustrates the prevalence of leisure-time physical inactivity in U.S. states by survey year from 1984 to 2015. In general, the prevalence of leisure-time physical inactivity declined over the survey period. During 1988–1998, 8–12 states had a prevalence of 35% or higher, whereas during 2005–2015, only 0–3 states had a prevalence of 35% or higher. There were substantial disparities in the prevalence of leisure-time physical inactivity both across states and survey years. For example, in 1990, the prevalence of leisure-time physical inactivity was 51.9% in the District of Columbia, nearly three folds of that (18.0%) in Montana. In 1996, the prevalence of leisure-time physical inactivity was 51.3% in Georgia, over three folds of that (17.0%) in Utah. The prevalence of leisure-time physical inactivity in DC increased from 38.4% in 1984 to 51.9% in 1990, and then gradually declined to 18.8% in 2015. The prevalence of leisure-time physical inactivity in Arizona increased from 20.4% in 1984 to 51.3% in 1998, and then gradually declined to its survey-inception level (24.4%) in 2015. Tables 2 Doublebreasted Grain De Poudre Wool Blazer Red Balmain Best Store To Get For Sale qSVlLV
report point estimates and corresponding 95% confidence intervals for the prevalence of leisure-time physical inactivity in each U.S. state by survey year.

A router behaves like middleware itself, so you can use it as an argument to app.use() or as the argument to another router’s use() method.

The top-level express object has a Router() method that creates a new router object.

Once you’ve created a router object, you can add middleware and HTTP method routes (such as get , put , post , and so on) to it just like an application. For example:

You can then use a router for a particular root URL in this way separating your routes into files or even mini-apps.

This method is just like the router.METHOD() methods, except that it matches all HTTP methods (verbs).

This method is extremely useful for mapping “global” logic for specific path prefixes or arbitrary matches. For example, if you placed the following route at the top of all other route definitions, it would require that all routes from that point on would require authentication, and automatically load a user. Keep in mind that these callbacks do not have to act as end points; loadUser can perform a task, then call next() to continue matching subsequent routes.

Another example of this is white-listed “global” functionality. Here the example is much like before, but it only restricts paths prefixed with “/api”:

The router.METHOD() methods provide the routing functionality in Express, where METHOD is one of the HTTP methods, such as GET, PUT, POST, and so on, in lowercase. Thus, the actual methods are router.get() , router.post() , router.put() , and so on.

The function is automatically called for the HTTP method in addition to the method if was not called for the path before .

You can provide multiple callbacks, and all are treated equally, and behave just like middleware, except that these callbacks may invoke next('route') to bypass the remaining route callback(s). You can use this mechanism to perform pre-conditions on a route then pass control to subsequent routes when there is no reason to proceed with the route matched.

The following snippet illustrates the most simple route definition possible. Express translates the path strings to regular expressions, used internally to match incoming requests. Query strings are not considered when performing these matches, for example “GET /” would match the following route, as would “GET /?name=tobi”.

You can also use regular expressions—useful if you have very specific constraints, for example the following would match “GET /commits/71dbb9c” as well as “GET /commits/71dbb9c..4c084f9”.

Adds callback triggers to route parameters, where name is the name of the parameter and callback is the callback function. Although name is technically optional, using this method without it is deprecated starting with Express v4.11.0 (see below).

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