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In previous hypothesis tests, we constructed a test statistic of the following form:
point estimate − null value SE of point estimate
This construction was based on (1) identifying the difference between a point estimate and an expected value if the null hypothesis was true, and (2) standardizing that difference us... | {
"Header 1": "6.3.2 The chi-square test statistic",
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The chi-square distribution is sometimes used to characterize data sets and statistics that are always positive and typically right skewed. Recall the normal distribution had two parameters – mean and standard deviation – that could be used to describe its exact characteristics. The chi-square distribution has just one... | {
"Header 1": "6.3.3 The chi-square distribution and finding areas",
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In Section 6.3.2, we identified a new test statistic $(X^2)$ within the context of assessing whether there was evidence of racial bias in how jurors were sampled. The null hypothesis represented the claim that jurors were randomly sampled and there was no racial bias. The alternative hypothesis was that there was rac... | {
"Header 1": "6.3.4 Finding a p-value for a chi-square distribution",
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Section [3.3](#page-141-1) would be useful background reading for this example, but it is not a prerequisite.
We can apply our new chi-square testing framework to the second problem in this section: evaluating whether a certain statistical model fits a data set. Daily stock returns from the S&P500 for 1990-2011 can b... | {
"Header 1": "6.3.5 Evaluating goodness of fit for a distribution",
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In other words, we have sufficient evidence to reject the notion that
$<sup>\</sup>frac{^{18}X^2 = \frac{(1532 - 1569)^2}{1569} + \frac{(760 - 734)^2}{734} + \dots + \frac{(17 - 31)^2}{31} = 15.08 }{^{19}\text{There are } k = 7 \text{ groups, so we use } df = k - 1 = 6. }$
Figure 6.14... | {
"Header 1": "6.3.5 Evaluating goodness of fit for a distribution",
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Google is constantly running experiments to test new search algorithms. For example, Google might test three algorithms using a sample of 10,000 google.com search queries. Table [6.15](#page-292-2) shows an example of 10,000 queries split into three algorithm groups.[20](#page-292-3) The group sizes were specified befo... | {
"Header 1": "6.4 Testing for independence in two-way tables (special topic)",
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Example 6.35 From the experiment, we estimate the proportion of users who were satisfied with their initial search (no new search) as 7078/10000 = 0.7078. If there really is no difference among the algorithms and 70.78% of people are satisfied with the search results, how many of the 5000 people in the "current algorit... | {
"Header 1": "6.4.1 Expected counts in two-way tables",
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The chi-square test statistic for a two-way table is found the same way it is found for a one-way table. For each table count, compute
| General formula | (observed count $-$ expected count) <sup>2</sup> |
|------------------|--------------------------------------------------|
| General iorniula | expected count ... | {
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<sup>&</sup>lt;sup>26</sup>The test statistic is larger than the right-most column of the df = 2 row of the chi-square table, meaning the p-value is less than 0.001. That is, we reject the null hypothesis because the p-value is less than 0.05, and we conclude that Americans' approval has differences among Democrats i... | {
"Header 1": "6.4.2 The chi-square test for two-way tables",
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We want to identify the sampling distribution of the test statistic (ˆp) if the null hypothesis was true. In other words, we want to see how the sample proportion changes due to chance alone. Then we plan to use this information to decide whether there is enough evidence to reject the null hypothesis.
Under the null ... | {
"Header 1": "6.5.2 Generating the null distribution and p-value by simulation",
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The number of successes in n independent cases can be described using the binomial model, which was introduced in Section 3.4. Recall that the probability of observing exactly k successes is given by
$$P(k \text{ successes}) = \binom{n}{k} p^k (1-p)^{n-k} = \frac{n!}{k!(n-k)!} p^k (1-p)^{n-k}$$
(6.50)
where p is th... | {
"Header 1": "6.5.2 Generating the null distribution and p-value by simulation",
"Header 3": "6.5.3 Generating the exact null distribution and p-value",
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Simulation methods may also be used to test goodness of fit. In short, we simulate a new sample based on the purported bin probabilities, then compute a chi-square test statistic X<sup>2</sup> sim. We do this many times (e.g. 10,000 times), and then examine the distribution of these simulated chi-square test statistics... | {
"Header 1": "6.5.4 Using simulation for goodness of fit tests",
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Cardiopulmonary resuscitation (CPR) is a procedure commonly used on individuals suffering a heart attack when other emergency resources are not available. This procedure is helpful in maintaining some blood circulation, but the chest compressions involved can also cause internal injuries. Internal bleeding and other in... | {
"Header 1": "6.6 Hypothesis testing for two proportions (special topic)",
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There were 50 patients in the experiment who did not receive the blood thinner and 40 patients who did. The study results are shown in Table 6.22.
| | Survived | Died | Total |
|-----------|----------|------|-------|
| Control | 11 | 39 | 50 |
| Treatment | 14 | 26 | 40 |
| Total ... | {
"Header 1": "6.6 Hypothesis testing for two proportions (special topic)",
"Header 3": "6.6.1 Large sample framework for a difference in two proportions",
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The ideas in this section were first introduced in the optional Section [1.8 on page 42.](#page-51-0) For the interested reader, this earlier section provides a more in-depth discussion.
Suppose the null hypothesis is true. Then the blood thinner has no impact on survival and the 13% difference was due to chance. In ... | {
"Header 1": "6.6.2 Simulating a difference under the null distribution",
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We build up an approximation to the null distribution by repeatedly creating tables like the one shown in Table 6.24 and computing the sample differences. The null distribution from 10,000 simulations is shown in Figure 6.25.
**Example 6.58** Compare Figures 6.23 and 6.25. How are they similar? How are they different... | {
"Header 1": "6.6.3 Null distribution for the difference in two proportions",
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<sup>41</sup>Marist Poll, [Road Rules: Re-Testing Drivers at Age 65?,](http://maristpoll.marist.edu/34-road-rules-re-testing-drivers-at-age-65) March 4, 2011.
6.9 Life after college. We are interested in estimating the proportion of graduates at a mid-sized university who found a job within one year of completing t... | {
"Header 1": "6.7.1 Inference for a single proportion",
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Among a simple random sample of 331 American adults who do not have a four-year college degree and are not currently enrolled in school, 48% said they decided not to go to college because they could not afford school.[49](#page-311-0)
- (a) A newspaper article states that only a minority of the Americans who decide n... | {
"Header 1": "6.7.1 Inference for a single proportion",
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6.23 Social experiment, Part I. A "social experiment" conducted by a TV program questioned what people do when they see a very obviously bruised woman getting picked on by her boyfriend. On two different occasions at the same restaurant, the same couple was depicted. In one scenario the woman was dressed "provocatively... | {
"Header 1": "6.7.2 Difference of two proportions",
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6.41 Quitters. Does being part of a support group affect the ability of people to quit smoking? A county health department enrolled 300 smokers in a randomized experiment. 150 participants were assigned to a group that used a nicotine patch and met weekly with a support group; the other 150 received the patch and did n... | {
"Header 1": "6.7.4 Testing for independence in two-way tables",
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6.47 Bullying in schools. A 2012 Survey USA poll asked Florida residents how big of a problem they thought bullying was in local schools. 9 out of 191 18-34 year olds responded that bullying is no problem at all. Using these data, is it appropriate to construct a confidence interval using the formula ˆp±z ?p pˆ(1 − pˆ)... | {
"Header 1": "6.7.5 Small sample hypothesis testing for a proportion",
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6.51 Social experiment, Part II. Exercise [6.23](#page-312-3) introduces a "social experiment" conducted by a TV program that questioned what people do when they see a very obviously bruised woman getting picked on by her boyfriend. On two different occasions at the same restaurant, the same couple was depicted. In one... | {
"Header 1": "6.7.6 Hypothesis testing for two proportions",
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Linear regression is a very powerful statistical technique. Many people have some familiarity with regression just from reading the news, where graphs with straight lines are overlaid on scatterplots. Linear models can be used for prediction or to evaluate whether there is a linear relationship between two numerical va... | {
"Header 1": "Introduction to linear regression",
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Scatterplots were introduced in Chapter [1](#page-10-0) as a graphical technique to present two numerical variables simultaneously. Such plots permit the relationship between the variables to be examined with ease. Figure [7.4](#page-327-0) shows a scatterplot for the head length and total length of 104 brushtail possu... | {
"Header 1": "7.1.1 Beginning with straight lines",
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We want to describe the relationship between the head length and total length variables in the possum data set using a line. In this example, we will use the total length as the predictor variable, x, to predict a possum's head length, y. We could fit the linear relationship by eye, as in Figure [7.7.](#page-329-0) The... | {
"Header 1": "7.1.2 Fitting a line by eye",
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Residuals are the leftover variation in the data after accounting for the model fit:
$$Data = Fit + Residual$$
Each observation will have a residual. If an observation is above the regression line, then its residual, the vertical distance from the observation to the line, is positive. Observations below the line ha... | {
"Header 1": "7.1.3 Residuals",
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Correlation: strength of a linear relationship Correlation, which always takes values between -1 and 1, describes the strength of the linear relationship between two variables. We denote the correlation by R.
We can compute the correlation using a formula, just as we did with the sample mean and standard deviation. H... | {
"Header 1": "7.1.4 Describing linear relationships with correlation",
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We begin by thinking about what we mean by "best". Mathematically, we want a line that has small residuals. Perhaps our criterion could minimize the sum of the residual magnitudes:
$$|e_1| + |e_2| + \dots + |e_n| \tag{7.9}$$
which we could accomplish with a computer program. The resulting dashed line shown in Figur... | {
"Header 1": "7.2 Fitting a line by least squares regression",
"Header 3": "7.2.1 An objective measure for finding the best line",
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When fitting a least squares line, we generally require
- Linearity. The data should show a linear trend. If there is a nonlinear trend (e.g. left panel of Figure [7.13\)](#page-335-0), an advanced regression method from another book or later course should be applied.
- Nearly normal residuals. Generally the residual... | {
"Header 1": "7.2.2 Conditions for the least squares line",
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For the Elmhurst data, we could write the equation of the least squares regression line as
$$aid = \beta_0 + \beta_1 \times family\_income$$
Here the equation is set up to predict gift aid based on a student's family income, which would be useful to students considering Elmhurst. These two values, β<sup>0</sup> and... | {
"Header 1": "7.2.3 Finding the least squares line",
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Interpreting parameters in a regression model is often one of the most important steps in the analysis.
Example 7.19 The slope and intercept estimates for the Elmhurst data are -0.0431 and 24.3. What do these numbers really mean?
Interpreting the slope parameter is helpful in almost any application. For each additi... | {
"Header 1": "7.2.4 Interpreting regression line parameter estimates",
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We evaluated the strength of the linear relationship between two variables earlier using the correlation, R. However, it is more common to explain the strength of a linear fit using $R^2$ , called **R-squared**. If provided with a linear model, we might like to describe how closely the data cluster around the linear f... | {
"Header 1": "7.2.6 Using $R^2$ to describe the strength of a fit",
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Categorical variables are also useful in predicting outcomes. Here we consider a categorical predictor with two levels (recall that a level is the same as a category). We'll consider Ebay auctions for a video game, Mario Kart for the Nintendo Wii, where both the total price of the auction and the condition of the game ... | {
"Header 1": "7.2.7 Categorical predictors with two levels",
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In this section, we identify criteria for determining which outliers are important and influential.
Outliers in regression are observations that fall far from the "cloud" of points. These points are especially important because they can have a strong influence on the least squares line.
- Example 7.23 There are si... | {
"Header 1": "7.3 Types of outliers in linear regression",
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Elections for members of the United States House of Representatives occur every two years, coinciding every four years with the U.S. Presidential election. The set of House elections occurring during the middle of a Presidential term are called midterm elections. In America's two-party system, one political theory sugg... | {
"Header 1": "7.4.1 Midterm elections and unemployment",
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Just like other point estimates we have seen before, we can compute a standard error and test statistic for b1. We will generally label the test statistic using a T, since it follows the t distribution.
We will rely on statistical software to compute the standard error and leave the explanation of how this standard e... | {
"Header 1": "7.4.2 Understanding regression output from software",
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We considered the t test statistic as a way to evaluate the strength of evidence for a hypothesis test in Section [7.4.2.](#page-345-0) However, we could focus on R<sup>2</sup> . Recall that R<sup>2</sup> described the proportion of variability in the response variable (y) explained by the explanatory variable (x). If ... | {
"Header 1": "7.4.3 An alternative test statistic",
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7.1 Visualize the residuals. The scatterplots shown below each have a superimposed regression line. If we were to construct a residual plot (residuals versus x) for each, describe what those plots would look like.
**7.2** Trends in the residuals. Shown below are two plots of residuals r... | {
"Header 1": "7.5.1 Line fitting, residuals, and correlation",
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The estimated regression line is
$$head\_circumference = 3.91 + 0.78 \times gestational\_age$$
- (a) What is the predicted head circumference for a baby whose gestational age is 28 weeks?
- (b) The standard error for the coefficient of gestational age is 0.35, which is associated with df = 23. Does the model provid... | {
"Header 1": "7.5.4 Inference for linear regression",
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Multiple regression extends simple two-variable regression to the case that still has one response but many predictors (denoted x1, x2, x3, ...). The method is motivated by scenarios where many variables may be simultaneously connected to an output.
We will consider Ebay auctions of a video game called Mario Kart for... | {
"Header 1": "8.1 Introduction to multiple regression",
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Let's fit a linear regression model with the game's condition as a predictor of auction price. The model may be written as
$$\widehat{price} = 42.87 + 10.90 \times cond_{-}new$$
Results of this model are shown in Table 8.3 and a scatterplot for price versus game condition is shown in Figure 8.4.
| ... | {
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Sometimes there are underlying structures or relationships between predictor variables. For instance, new games sold on Ebay tend to come with more Wii wheels, which may have led to higher prices for those auctions. We would like to fit a model that includes all potentially important variables simultaneously. This woul... | {
"Header 1": "8.1.2 Including and assessing many variables in a model",
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We first used R<sup>2</sup> in Section [7.2](#page-333-0) to determine the amount of variability in the response that was explained by the model:
$$R^{2} = 1 - \frac{\text{variability in residuals}}{\text{variability in the outcome}} = 1 - \frac{Var(e_{i})}{Var(y_{i})}$$
where e<sup>i</sup> represents the residuals... | {
"Header 1": "8.1.3 Adjusted R<sup>2</sup> as a better estimate of explained variance",
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Table 8.6 provides a summary of the regression output for the full model for the auction data. The last column of the table lists p-values that can be used to assess hypotheses of the following form:
$H_0: \beta_i = 0$ when the other explanatory variables are included in the model.
$H_A: \beta_i \neq 0$ when the ... | {
"Header 1": "8.2 Model selection",
"Header 3": "8.2.1 Identifying variables in the model that may not be helpful",
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Two common strategies for adding or removing variables in a multiple regression model are called backward-selection and forward-selection. These techniques are often referred to
<sup>11</sup>The p-value for the auction duration is 0.8882, which indicates that there is not statistically significant evidence that the d... | {
"Header 1": "8.2.2 Two model selection strategies",
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#### Model selection strategies
The backward-elimination strategy begins with the largest model and eliminates variables one-by-one until we are satisfied that all remaining variables are important to the model. The forward-selection strategy starts with no variables included in the model, then it adds in variables... | {
"Header 1": "8.2.2 Two model selection strategies",
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Multiple regression methods using the model
$$\hat{y} = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \dots + \beta_k x_k$$
generally depend on the following four assumptions:
- 1. the residuals of the model are nearly normal,
- 2. the variability of the residuals is nearly constant,
- 3. the residuals are independent, a... | {
"Header 1": "8.3 Checking model assumptions using graphs",
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In this section we introduce **logistic regression** as a tool for building models when there is a categorical response variable with two levels. Logistic regression is a type of **generalized linear model** (GLM) for response variables where regular multiple regression does not work very well. In particular, the respo... | {
"Header 1": "8.4 Logistic regression",
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TIP: Notation for a logistic regression model
The outcome variable for a GLM is denoted by Y<sup>i</sup> , where the index i is used to represent observation i. In the email application, Y<sup>i</sup> will be used to represent whether email i is spam (Y<sup>i</sup> = 1) or not (Y<sup>i</sup> = 0).
The predictor var... | {
"Header 1": "8.4.2 Modeling the probability of an event",
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Error | z value | Pr(> z ) |
|-----------------|----------|------------|---------|----------|
| (Intercept) | -0.8362 | 0.0962 | -8.69 | 0.0000 |
| to<br>multiple | -2.8836 | 0.3121 | -9.24 | 0.0000 |
| winner | 1.7038 | 0.3254 | 5.24 | 0.0000 |
| format | -1.5902 | ... | {
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Examples [8.22](#page-380-2) and [8.23](#page-381-1) highlight a key feature of logistic and multiple regression. In the spam filter example, some email characteristics will push an email's classification in the direction of spam while other characteristics will push it in the opposite direction.
If we were to implem... | {
"Header 1": "8.4.3 Practical decisions in the email application",
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Logistic regression conditions There are two key conditions for fitting a logistic regression model:
- 1. The model relating the parameter p<sup>i</sup> to the predictors x1,i, x2,i, ..., xk,i closely resembles the true relationship between the parameter and the predictors.
- 2. Each outcome Y<sup>i</sup> is independ... | {
"Header 1": "8.4.4 Diagnostics for the email classifier",
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If we were building a spam filter for an email service that managed many accounts (e.g. Gmail or Hotmail), we would spend much more time thinking about additional variables that could be useful in classifying emails as spam or not. We also would use transformations or other techniques that would help us include strongl... | {
"Header 1": "8.4.5 Improving the set of variables for a spam filter",
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8.1 Baby weights, Part I. The Child Health and Development Studies investigate a range of topics. One study considered all pregnancies between 1960 and 1967 among women in the Kaiser Foundation Health Plan in the San Francisco East Bay area. Here, we study the relationship between smoking and weight of the baby. The va... | {
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and learner status (lrn: 0 - average learner, 1 - slow learner).<sup>18</sup>
| | Estimate | Std. Error | t value | $\Pr(> t )$ |
|-------------|----------|------------|---------|-------------|
| (Intercept) | 18.93 | 2.57 | 7.37 | 0.0000 |
| eth | -9.11 | 2.60 | -3.51 ... | {
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Note that male is coded as 1.
| - | Estimate | Std. Error | t value | $\Pr(> t )$ |
|-------------|----------|------------|---------|-------------|
| (Intercept) | 3.45 | 0.35 | 9.85 | 0.00 |
| studyweek | 0.00 | 0.00 | 0.27 | 0.79 |
| sleepnight | 0.01 | 0.05 ... | {
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8.7 Baby weights, Part IV. Exercise [8.3](#page-387-0) considers a model that predicts a newborn's weight using several predictors. Use the regression table below, which summarizes the model, to answer the following questions. If necessary, refer back to Exercise [8.3](#page-387-0) for a reminder about the meaning of e... | {
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#### 1 Introduction to data 1.1 (a) Treatment: 10/43 = 0.23 → 23%. Control: 2/46 = 0.04 → 4%. (b) There is a 19% difference between the pain reduction rates in the two groups. At first glance, it appears patients in the treatment group are more likely to experience pain reduction from the acupuncture treatment. (c) Ans... | {
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(b) The IQR is better; the standard deviation, like the mean, is substantially affected by the two high salaries.
**1.43** The distribution is unimodal and symmetric with a mean of about 25 minutes and a standard deviation of about 5 minutes. There does not appear to be any counties with unusually high or low mean tr... | {
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then one occurring does not preclude the other from occurring.
2.11 (a) 0.16 + 0.09 = 0.25. (b) 0.17 + 0.09 = 0.26. (c) Assuming that the education level of the husband and wife are independent: 0.25 × 0.26 = 0.065. You might also notice we actually made a second assumption: that the decision to get married is unrela... | {
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Quant: N(µ = 584, σ = 151). (b) ZV R = 1.33, ZQR = 0.57.
(c) She scored 1.33 standard deviations above the mean on the Verbal Reasoning section and 0.57 standard deviations above the mean on the Quantitative Reasoning section. (d) She did better on the Verbal Reasoning section since her... | {
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Therefore we are interested in the number of ways of orderings of the other k − 1 successes in the first n − 1 trials.
3.45 (a) Poisson with λ = 75. (b) µ = λ = 75, σ = √ λ = 8.66. (c) Z = −1.73. Since 60 is within 2 standard deviations of the mean, it would not generally be considered unusual. Note that we often use... | {
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the sample mean will be nearly normal, allowing for the method normal approximation described. (b) False. Inference is made on the population parameter, not the point estimate. The point estimate is always in the confidence interval. (c) True. (d) False. The confidence interval is not about a sample mean. (e) False. To... | {
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(d) If she makes a Type 1 error, she will continue taking medication that does not actually treat her disorder. If she makes a Type 2 error, she will stop taking medication that could treat her disorder.
4.29 (a) If the null hypothesis is rejected in error, then the regulators concluded that the adverse effect was hi... | {
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Next, identify the Z scores that would result in rejecting $H_0$ : $Z_{lower} = -1.96$ , $Z_{upper} = 1.96$ . In each case, calculate the corresponding sample mean cutoff: $\bar{x}_{lower} = 118.445$ and $\bar{x}_{upper} = 137.555$ . (c) Construct Z scores for the values from part (b) but using the supposed true ... | {
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If in fact the true population mean of the amount New Yorkers sleep per night was 8 hours, the probability of getting a random sample of 25 New Yorkers where the average amount of sleep is 7.73 hrs per night or less is between 0.025 and 0.05. (d) Since p-value <0.05, reject $H_0$ . The data provide strong evidence tha... | {
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There are differences in the actual computed standard deviations, but these might be due to chance as these are quite small samples. F5,<sup>65</sup> = 15.36 and the p-value is approximately 0. With such a small p-value, we reject H0. The data provide convincing evidence that the average weight of chicks varies across ... | {
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so we now conduct $K = 3 \times 2/2 = 3$ pairwise t tests that each use
#### 6 Inference for categorical data
6.1 (a) False. Doesn't satisfy success-failure condition. (b) True. The success-failure condition is not satisfied. In most samples we would expect $\hat{p}$ to be close to 0.08, the true population pro... | {
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The data provide strong evidence that the proportion of Americans who only use their cell phones to access the internet is different than the Chinese proportion of 38%, and the data indicate that the proportion is lower in the US. (b) If in fact 38% of Americans used their cell phones as a primary access point to the i... | {
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$E_{print} = 126 \times 0.25 = 31.5$ . $E_{online} = 126 \times 0.15 = 18.9$ . (c) Independence: The sample is not random. However, if the professor has reason to believe that the proportions are stable from one term to the next and students are not affecting each
other's study habits, independence is probably reas... | {
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(Students may have approximate results, and a small number of students may have a p-value of about 0.05.) Since the p-value is low, we reject $H_0$ . The data provide strong evidence that people react differently under the two scenarios.
7.9 (a) The relationship is positive, weak, and possibly linear. However, there... | {
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we can compute their husbands' ages as 29, 30, 31, 32, and 33. We can plot these points to see they fall on a straight line, and they always will. The same approach can be applied to the other parts as well.
7.17 (a) There is a positive, very strong, linear association between the number of tourists and spending. (b)... | {
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(c) H0: The true slope coefficient of height is zero (β<sup>1</sup> = 0). H0: The true slope coefficient of height is greater than zero (β<sup>1</sup> > 0). A two-sided test would also be acceptable for this application. The p-value for the two-sided alternative hypothesis (β<sup>1</sup> 6= 0) is incredibly small, so t... | {
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- 1.1 [Pharmacology, Interdisciplinary Teams, and Nursing Practice](#page-24-2)
- 1.2 [Drug Sources, Forms, and Names](#page-27-0)
- 1.3 [Drug Classifications and Prototypes](#page-38-0)
- 1.4 [Special Considerations](#page-40-0)
**INTRODUCTION** This book aims to provide a fundamental understanding of the pharmacolo... | {
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The word **pharmacology** (from two Greek words, pharmakon, which means "**drug**" or "medicine," and logos, which
means "study") essentially means the study of medicine; it could also be described as the study of the biological effects of chemicals on the body. The history of pharmacology dates back thousands of yea... | {
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"Header 2": "**History of Pharmacology**",
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The nurse administers medication as part of a team that includes other professions. The health care provider (physician, physician's assistant, or nurse practitioner) orders the drug indicated to treat the client. (The term client is interchangeable with patient in some settings.) The pharmacist evaluates the client an... | {
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"Header 2": "**Interdisciplinary Nature of Pharmacology**",
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Knowledge about pharmacology and the various drugs prescribed and administered to clients is a major part of the nurse's role. Even when not administering the medications directly to the client, it is crucial to the client's care. Nurses must understand the pharmacotherapeutic effects of the drugs in their clinical pra... | {
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"Header 2": "**Pharmacology and Clinical Nursing Practice**",
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Drugs are substances or compounds that prevent, treat, diagnose, or cure various conditions or diseases. As mentioned previously, drugs come from a variety of resources—plants, animal products, and inorganic substances. Ideally, these chemicals have desirable therapeutic effects without harmful properties, although man... | {
"Header 1": "CHAPTER 1 Introduction to Pharmacology",
"Header 2": "**Drug Sources and Forms**",
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One of the biggest challenges when learning pharmacology is that all drugs have multiple names and ways to be identified. There are three basic methods for identifying a drug—the chemical name, the generic name, and the **brand name**, or trade name. If more than one drug company supplies a drug to the market, then tha... | {
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"Header 2": "**Drug Sources and Forms**",
"Header 3": "**Drug Names**",
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When a pharmaceutical company first creates a drug, it is developed as a specific chemical substance. This substance is subject to approval by the U.S. Food and Drug Administration (FDA) once it has undergone rigorous testing with both animal and human subjects. Once approved by the FDA, the pharmaceutical company is g... | {
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"Header 2": "**Drug Sources and Forms**",
"Header 3": "**Generic and Brand Name Drug Equivalents**",
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Prescription medications are drugs available to the client only by an order (commonly known as a prescription) from a health care provider. The health care provider must have the training and license to prescribe the drug. The prescription communicates the provider's plan for the client and drug to the nurse or the pha... | {
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"Header 2": "**Prescription and Over-the-Counter Drugs**",
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The FDA is the government agency responsible for the regulation of the development, production, and sale of drugs. The FDA was given much closer control over the production of drugs after the drug thalidomide was prescribed to pregnant clients for the treatment of morning sickness and for sedation in the 1950s and 1960... | {
"Header 1": "CASE STUDY",
"Header 2": "**Drug Standards**",
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The Center for Drug Evaluation and Research (CDER) is a branch of the FDA that evaluates new drugs before they can be sold in the United States. It provides health care providers and consumers with the information needed to use drugs appropriately. One of the tasks of CDER is to ensure that both brand-name and generic ... | {
"Header 1": "CASE STUDY",
"Header 2": "**Drug Approval Process**",
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Some estimate the cost of developing just one new drug as ranging from less than \$1 billion to over \$2 billion (Wouters et al., 2020). The development of new drugs requires 10–15 years before the testing and drug studies are complete. Thousands of compounds are tested yearly, but only a few make it to clinical trials... | {
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"Header 2": "**Drug Approval Process**",
"Header 3": "**TABLE 1.2 Summary of the Phases of Drug Development**",
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Access multimedia content [\(https://openstax.org/books/pharmacology/pages/1-2-drug-sources-forms-and](https://openstax.org/books/pharmacology/pages/1-2-drug-sources-forms-and-names)[names\)](https://openstax.org/books/pharmacology/pages/1-2-drug-sources-forms-and-names)
In this video from the National Heart, Lung, a... | {
"Header 1": "CASE STUDY",
"Header 2": "Clinical Trials for Children",
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Off-label prescription drug use pertains to the utilization of a medication in ways that deviate from the specifications provided on the drug's label or within its FDA-approved package insert. Such usage encompasses employing the medication for divergent medical conditions, adjusting dosages, targeting varying client g... | {
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The United States' and Canada's drug laws have evolved in a similar manner. Any drug manufacturer must provide scientific evidence of the drug's safety, efficacy, and quality to Health Canada before the sale of that product is authorized. The federal review process by Health Canada was empowered by the Food and Drugs A... | {
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"Header 2": "**Canadian Drug Regulation**",
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The United States has one of the world's safest drug supplies, partly due to the FDA and USP-NF. Unfortunately, counterfeit drugs threaten that safety. **Counterfeit drugs** are products that are illegally manufactured or mislabeled with regard to their identity or source so that they appear to be a genuine product. Th... | {
"Header 1": "CASE STUDY",
"Header 2": "**Drug Counterfeiting**",
"token_count": 424,
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One helpful method for sorting out the sheer number of drugs on the market is by organizing them into different classifications. Most drugs are classified in two ways—therapeutic classification and pharmacologic classification.
- **Therapeutic classification** refers to a drug's therapeutic use or clinical indication... | {
"Header 1": "CASE STUDY",
"Header 2": "**Drug Classifications**",
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Once drugs are sorted into classifications, it is customary to utilize one drug within the class to compare to all the other drugs within that class. It becomes the "class representative," known as the **drug prototype**. Using a drug prototype is helpful for an individual learning pharmacology because it makes learnin... | {
"Header 1": "CASE STUDY",
"Header 2": "**Drug Prototypes**",
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Many different chemicals, substances, and drugs come under U.S. Drug Enforcement Administration (DEA) oversight (U.S. DEA, n.d.). In 1970, Congress passed the Controlled Substances Act, which led to the establishment of the DEA in 1973 and described the controls for manufacturing, distributing, and prescribing habit-fo... | {
"Header 1": "CASE STUDY",
"Header 2": "**Drug Schedules**",
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Without question, medication costs have been escalating for many years. Many factors encompass total health care costs: the up-front costs of prescription drugs, visits to providers and health care institutions, morbidity and mortality, diagnostic and interventional medicine, and suboptimal medical therapy, to name onl... | {
"Header 1": "CASE STUDY",
"Header 2": "**Pharmacology and Socioeconomic Factors**",
"token_count": 495,
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The FDA approved the first gene-therapy drug for hemophilia, a blood-clotting disorder, in November 2022. Hemgenix was developed by the drug company CSL Behring. This particular drug is helpful in treating clients with a factor IX deficiency. Even though only a single dose of the drug is needed to provide protection fr... | {
"Header 1": "CASE STUDY",
"Header 2": "The World's Most Expensive Drug...For Now!",
"token_count": 344,
"source_pdf": "datasets/websources/Med_v1/med_textbook/Pharmacology-WEB.pdf"
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The very young and the very old are predisposed to be the most sensitive to drugs. One factor causing this is the
differences in pharmacokinetics in these individuals. **Pharmacokinetics** is the movement of a drug through the body. It is easiest to think of it as "what the body does to the drug." Drug absorption, di... | {
"Header 1": "CASE STUDY",
"Header 2": "**Drugs as They Relate to Specific Populations**",
"token_count": 214,
"source_pdf": "datasets/websources/Med_v1/med_textbook/Pharmacology-WEB.pdf"
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Pregnancy and lactation are other areas of medicine where researchers exercise greater caution due to the risk of adverse effects of a drug or substance on the fetus. When giving a drug to someone who is pregnant, the provider must consider both the pregnant client and the fetus. In many cases, it is safest to postpone... | {
"Header 1": "CASE STUDY",
"Header 2": "CLINICAL TIP",
"Header 3": "**Pregnancy and Lactation**",
"token_count": 1055,
"source_pdf": "datasets/websources/Med_v1/med_textbook/Pharmacology-WEB.pdf"
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One of the newest areas of research in pharmacology is **pharmacogenetics**. This area of pharmacology studies the response to drugs based on a client's individual genetics, including therapeutic responses to drugs and the predisposition individuals might have to the adverse effects of a medication. With the identifica... | {
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"Header 2": "CLINICAL TIP",
"Header 3": "**Genetics**",
"token_count": 297,
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There are cultural considerations that the nurse must integrate into the care of the client:
- Acknowledge individual differences.
- Reflect on one's own potential inherent biases.
- Practice cultural humility.
- Embrace and respect diversity.
Accommodating the culturally diverse:
- Assess their ability to commun... | {
"Header 1": "CASE STUDY",
"Header 2": "Cultural Concerns",
"token_count": 289,
"source_pdf": "datasets/websources/Med_v1/med_textbook/Pharmacology-WEB.pdf"
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There is an increasing number of older adults around the world. One of the terms used to describe the aging population is "silver tsunami." This somewhat negative term refers to the combination of the large number of baby boomers who are reaching retirement age, the improved life expectancy for older adults, and a redu... | {
"Header 1": "CASE STUDY",
"Header 2": "**The Aging Client and Pharmacology**",
"token_count": 875,
"source_pdf": "datasets/websources/Med_v1/med_textbook/Pharmacology-WEB.pdf"
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**adverse drug event** when an individual is harmed by a drug
**adverse effect** an effect of a drug that is undesired **biologic** a drug isolated from natural resources and
- developed through biomolecular science, immunology, and genetic engineering and produced through biotechnological processes
- **biosimilar*... | {
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"Header 2": "**Key Terms**",
"token_count": 440,
"source_pdf": "datasets/websources/Med_v1/med_textbook/Pharmacology-WEB.pdf"
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- 2.1 [Drug Administration and the Nursing Process](#page-50-2)
- 2.2 [Pharmacokinetics and Pharmacodynamics](#page-60-0)
- 2.3 [Drug Administration Routes, Preparation, and Administration](#page-68-0)
- 2.4 [Dosage Calculations](#page-89-0)
**INTRODUCTION** This chapter will describe the process of drug administrati... | {
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"Header 2": "**CHAPTER OUTLINE**",
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