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In our memory experiment buy discount zyban 150mg on-line anxiety 6 letters, the variable of recall errors is a ratio variable that is as- sumed to form an approximately normal distribution cheap zyban 150 mg with mastercard depression nos dsm 5. Therefore, we compute the mean score in each condition by computing the mean of the scores in each column. There- fore, to interpret the mean in any study, simply envision the scores that would typi- cally produce such a mean. For example, when X 5 3, envision a normal distribution of scores above and below 3, with most scores close to 3. Likewise, for each mean, essentially envision the kinds of raw scores shown in our columns. Thus, the means show that recalling a 5-item list resulted in one distribution located around three er- rors, but recalling a 10-item list produced a different distribution at around six errors, and recalling a 15-item list produced still another distribution at around nine errors. Further, we use the mean score to describe the individual scores in each condition. In Condition 1, for example, we’d predict that any participant would make about three errors. Most important is the fact that, by looking at the means alone, we see that a rela- tionship is present here: as the conditions change (from 5 to 10 to 15 items in a list), the scores on the dependent variable also change (from around 3, to around 6, to around 9 errors, respectively). For example, we might find that only the mean in the 5-item condition is different from the mean in the 15-item condition. We still have a relationship if, at least sometimes, as the conditions of the independent variable change, the dependent scores also change. For example, say that we study political party affiliation as a function of a person’s year in college. Our dependent variable is political party, a nominal variable, so the mode is the appropriate measure of central tendency. We might see that freshmen most often claim to be Republican, but the mode for sophomores is Democrat; for juniors, Socialist; and for seniors, Communist. These data reflect a relationship because they indicate that as college level changes, political affiliation tends to change. This tells us that the location Participants Recalling a of the distribution of incomes is dif- 5-, 10-, or 15-Item List ferent for each class, so we know The mean of each condition 4 that the income “scores” of individ- is under each column. Summarizing Research 75 Graphing the Results of an Experiment Recall that the independent variable involves the conditions “given” to participants so it is plotted on the X axis. However, be- cause we want to summarize the data, usually we do not plot the individual scores. Rather, we plot either the mean, median, or mode of the dependent scores from each condition. Note: Do not be confused by the fact that we use X to represent the scores when computing the means. The type of graph to select is determined by the characteristics of the independent variable. Line Graphs Create a line graph when the independent variable is an interval or a ratio variable. We use straight lines to connect the data points here for the same reason we did when producing polygons: Anytime the variable on the X axis in- volves an interval or ratio scale, we assume that it is a continuous variable and there- fore we draw lines. The lines show that the relationship continues between the points shown on the X axis. For example, we assume that if there had been a 6-item list, the mean error score would fall on the line connecting the means for the 5- and 10-item lists. Each mean implies a sample of scores and their corresponding data points are around—above and below—the mean’s data point. Because the vertical positions of the means change as the conditions change, we know that the raw scores also change, so a relationship is present. Notice that you can easily spot such a relationship because the different means pro- duce a line graph that is not horizontal. On any graph, if the summary data points form a line that is not horizontal, it indicates that the individual Y scores are changing as the X scores change, so a relationship is present. This implies that (as in the figure on the right) the individual scores stay the same regardless of the condition, so no relationship is present. Thus, on any graph, if the summary data points form a horizontal line, it indicates that the indi- vidual Y scores do not change as the X scores change, and so a relationship is not present. Bar Graphs Create a bar graph when the independent variable is a nominal or an ordinal variable.

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The greater the number of metal–ligand bonds zyban 150 mg for sale depression symptoms physical, the more stable the complex and the greater the efficiency of the chelator 150mg zyban visa lexapro depression test. Dimercaprol is an oily, foul-smelling liquid administered intramuscularly as a 10% solution in peanut oil. Dimercaprol interacts with metals, reactivating or preventing the inactivation of cellular sulfhydryl-containing enzymes. This agent is useful in arsenic, inorganic mercury, and organic mercury poisoning (and lead poisoning). The adverse effects of dimercaprol include tachycardia, hypertension, gastric irritation, and pain at the injection site. Succimer (Chemax) is a derivative of dimercaprol that can be taken orally and is approved for use in children to treat lead poisoning. The adverse effects of succimer are generally minor and include nausea, vomiting, and anorexia. Unithol (Dimaval) is another analogue of dimercaprol over which it has advantages (few adverse effects) for treatment of mercury, arsenic, and lead poisoning. This agent is used primarily to chelate excess copper in individuals with Wilson disease. Penicillamine is also used for copper and mercury poisoning and as an adjunct for the treat- ment of lead and arsenic poisoning. Allergic reactions and rare bone marrow toxicity and renal toxicity are the major adverse effects. Deferoxamine is a specific iron-chelating agent that on parenteral administration binds with ferric ions to form ferrioxamine; it also binds to ferrous ions. Deferoxamine can also Chapter 13 Toxicology 319 remove iron from ferritin and hemosiderin outside bone marrow, but it does not capture iron from hemoglobin, cytochromes, or myoglobin. Deferoxamine is metabolized by plasma enzymes and excreted by the kidney, turning urine red. Deferoxamine may cause allergic reactions and rare neurotoxicity or renal toxicity. Defer- oxamine therapy is contraindicated in patients with renal disease or renal failure. More than a million cases of acute poisoning occur each year in the United States, many in children and adolescents. The symptoms of most drug and chemical poisonings are extensions of their pharmacologic properties. Measures to support vital functions, slow drug absorption, and promote excretion are generally sufficient for treatment. Comatose patients may die as a result of airway obstruction, respiratory arrest, or aspiration of gastric contents into the tracheobronchial tube. Induction of vomiting with ipecac orally is no longer recommended for routine use at home, and is con- traindicated in children under 6 years. Urinary excretion can be enhanced by the admin- istration of agents such as sodium bicarbonate, which raises urinary pH and decreases renal reabsorption of certain organic acids such as aspirin and phenobarbital. Hemodialysis is an efficient way to remove certain low molecular weight, water-soluble tox- ins and restore electrolyte balance. Salicylate, methanol, ethanol, ethylene glycol, paraquat, and lithium poisonings are effectively treated this way; hemoperfusion may enhance the whole-body clearance of some agents (carbamazepine, phenobarbital, phenytoin). Drugs and poisons with large volumes of distribution are not effectively removed by dialysis. Antidotes (see respective agents) are available for some poisons and should be used when a specific toxin is identified. Some examples include naloxone, acetylcysteine, physostigmine, metal chelators (see above), atropine, pralidoxime, and ethanol. What treatment would be appropriate in a 3- What is the most appropriate agent to adminis- year-old boy with a dramatically elevated blood ter at this point? A 56-year-old chronic alcoholic is brought to gency room because he recently ingested the emergency room with altered mental status numerous ‘‘iron pills’’ his mother was taking for and complains of not being able to see. The child now has severe abdomi- reports running out of ‘‘whiskey’’ and ingesting nal pain, bloody diarrhea, nausea, and vomit- wood alcohol (methanol).

Outliers may be errors in data recording discount 150 mg zyban amex depression in older adults, incorrect data entry values that can be corrected buy zyban 150 mg without prescription mood disorder gala winnipeg, or genuine values. When outliers are from participants from another population with different characteristics to the intended sample, they are called contaminants. This happens, for example, when a participant with a well-deﬁned illness is inadvertently included as a healthy participant. Occa- sionally, outliers can be excluded from the data analyses if they are contaminants or biologically implausible values. However, deleting values simply because they are out- liers is usually unacceptable and it is preferable to ﬁnd a way to accommodate the values without causing undue bias in the analyses. It is important that the methods used to accommodate outliers are reported so that the generalizability of the results is clear. The Tests of Normality table provides the results of two tests: a Kolmogorov–Smirnov statistic with a Lilliefors signiﬁcance correction and a Shapiro–Wilk statistic. A limitation of the Kolmogorov–Smirnov test of normality without the Lilliefors correction is that it is very conservative and is sensitive to extreme values that cause tails in the distribu- tion. The Shapiro–Wilk test has more statistical power to detect a non-normal distribution than the Kolmogorov–Smirnov test. The Shapiro–Wilk test is based on the correlation between the data and the corresponding normal scores. The values of the Shapiro–Wilk statistic range between zero, which indicates non-normality of the data and a value of one which indicates normality. A distribution that passes these tests of normality provides extreme conﬁdence that parametric tests can be used. However, variables that do not pass these tests may not be so non-normally distributed that parametric tests cannot be used, especially if the sample size is large. This is not to say that the results of these tests can be ignored but rather that a considered decision using the results of all the available checks of normality needs to be made. Birth weight marginally fails the Shapiro–Wilk test but the P values for gestational age Descriptive statistics 35 and length of stay show that they have potentially non-normal distributions. The Kolmogorov–Smirnov test shows that the distribution of birth weight is not signiﬁ- cantly different from a normal distribution with a P value greater than 0. However, the Kolmogorov–Smirnov test indicates that the distributions of both gestational age and length of stay are signiﬁcantly different from a normal distribution at P < 0. These tests of normality do not provide any information about why a variable is not normally distributed and therefore, it is always important to obtain skewness and kur- tosis values using Analyze → Descriptive Statistics → Explore and to request plots in order to visually inspect the distribution of data and identify any reasons for non-normality. Histograms also show whether there are any gaps in the data which is common in small data sets, whether there are any outlying values and how far any outlying values are from the remainder of the data. The normal Q–Q plot shows each data value plotted against the value that would be expected if the data came from a normal distribution. The values in the plot are the quantiles of the variable distribution plotted against the quantiles that would be expected if the distribution was normal. If the variable was normally distributed, the points would fall directly on the straight line. The detrended normal Q–Q plots show the deviations of the points from the straight line of the normal Q–Q plot. If the distribution is normal, the points will cluster ran- domly around the horizontal line at zero with an equal spread of points above and below the line. If the distribution is non-normal, the points will be in a pattern such as J or an inverted U distribution and the horizontal line may not be in the centre of the data. The box plot shows the median as the black horizontal line inside the box and the inter-quartile range as the length of the box. The inter-quartile range indicates the 25th to 75th percentiles, that is, the range in which the central 25–75% (50%) of the data points lie. If values are outside this range, they are plotted as outlying values (circles) or extreme values (asterisks). Extreme values that are more than three box lengths from the upper or lower edge of the box are shown as asterisks. Extreme and/or outlying values should be checked to see whether they are univariate outliers.

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