An adjectival phrase or adjective phrase is a group of words in a sentence that functions in the same way a lone adjective would.

Adjectives are used to modify nouns or pronouns. They give an additional detail about the meaning of a noun. In the sentence "Mr. Clinton is a wealthy man." The question "What kind of man is Mr. Clinton?" is answered by the word ‘wealthy’.

To use an adjectival phrase, the word 'wealthy' is replaced with a group of words: 'of great wealth'. The sentence becomes "Mr. Clinton is a man of great wealth."

Both of these sentences convey the same meaning.

Examples

An example of a sentence with an adjectival: Jackson is a Friendless boy "Friendless" is the adjectival.

The same example with an adjective phrase:

"Without a friend" is the adjective phrase.

An example of a sentence with an adjective:

The Politician is a ''kind'' man.

"Kind" is the adjective.

The same example with an adjectival phrase:

"Of kindly nature" is the adjective phrase

An adpositional phrase is a linguistics term that includes prepositional phrases (usually found in head-first languages such as English) and postpositional phrases (usually found in head-final languages). The difference between the two is simply one of word order.

All types of adpositional phrases are a syntactic category: a phrase which is treated in certain ways as a unit by a language's rules of syntax. An adpositional phrase is composed of an adposition (in the head position, which is why it lends its name to the phrase) and usually a complement such as a noun phrase. ("Adposition" is similarly a generic term for either a preposition or a postposition.) These phrases generally act as complements and adjuncts of noun phrases and verb phrases.

Prepositional phrases

The bolded phrases are examples of prepositional phrases in English:

• She is on the computer. (She is using the computer.)
• Haziq could hear her across the room.
• David walked down the ramp.
• They walked to their school.
• Dylan ate in the kitchen.

Prepositional phrases have a preposition as the head of the phrase.

The first example could be diagrammed (using simplified modern notation):

     IP   /  \ NP    VP |     | \ N     V  \ |     |   PP She   is /  \         /    \         P     NP         |    /  \         on  Det  N             |    |             the  computer  

Where by convention:

• IP = Inflectional phrase (sentence)
• NP = Noun phrase
• N = Noun
• VP = Verb phrase
• V = Verb
• PP = Prepositional phrase
• P = Preposition
• Det = Determiner

The diagram shows that the prepositional phrase in this sentence is composed of two parts: a preposition and a noun phrase. The preposition is in the head position, and the noun phrase is in the complement position. Because English is a head-first language, we usually see the head before the complement (or any adjuncts) when we actually read the sentence. (However, the head comes after the specifier, such as the determiner "the" in the noun phrase above.)

See adposition for more examples of complements found in prepositional phrases.

Prepositional phrases generally act as complements and adjuncts of noun phrases and verb phrases. For example:

• The man from China was enjoying his noodles. (Adjunct of a noun phrase)
• She ran under him. (Adjunct of a verb phrase)
• He gave money to the cause. (Oblique complement of a verb phrase)
• A student of physics. (Complement of a noun phrase)
• She argued with him. (Complement of a verb phrase)

A prepositional phrase should not be confused with the sequence formed by the particle and the direct object of a phrasal verb, as in turn on the light. This sequence is structurally distinct from a prepositional phrase. In this case, "on" and "the light" do not form a unit; they combine independently with the verb "turn".

Another common point of confusion is that the word "to" may appear either as a preposition or as a verbal particle in infinitive verb phrases, such as "to run for president".

Postpositional phrases

Postpositions are usually found in head-final languages such as Basque, Estonian, Finnish, Japanese, Hindi, Urdu, Bengali and Tamil. The word or other morpheme that corresponds to an English preposition occurs after its complement, hence the name postposition. The following examples are from Japanese:

• mise 'ni ("'to the store")
• ie 'kara ("'from the house")
• hashi 'de ("'with chopsticks" or "on the bridge")

And from Finnish, where postpositions have further developed into case endings:

• kaupp'aan ("'to the store")
• talo'sta ("'from the house")
• puikoi'lla ("'with chopsticks")

Postpositional phrases generally act as complements and adjuncts of noun phrases and verb phrases.

Descriptive statistics describe the main features of a collection of data quantitatively. Descriptive statistics are distinguished from inferential statistics (or inductive statistics), in that descriptive statistics aim to summarize a data set quantitatively without employing a probabilistic formulation, rather than use the data to make inferences about the population that the data are thought to represent. Even when a data analysis draws its main conclusions using inferential statistics, descriptive statistics are generally also presented. For example in a paper reporting on a study involving human subjects, there typically appears a table giving the overall sample size, sample sizes in important subgroups (e.g., for each treatment or exposure group), and demographic or clinical characteristics such as the average age, the proportion of subjects of each sex, and the proportion of subjects with related comorbidities.

Inferential statistics

Inferential statistics tries to make inferences about a population from the sample data. We also use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one, or that it might have happened by chance in this study. Thus, we use inferential statistics to make inferences from our data to more general conditions; we use descriptive statistics simply to describe what's going on in our data.

Use in statistical analyses

Descriptive statistics provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of quantitative analysis of data.

Descriptive statistics summarize data. For example, the shooting percentage in basketball is a descriptive statistic that summarizes the performance of a player or a team. This number is the number of shots made divided by the number of shots taken. A player who shoots 33% is making approximately one shot in every three. One making 25% is hitting once in four. The percentage summarizes or describes multiple discrete events. Or, consider the scourge of many students, the grade point average. This single number describes the general performance of a student across the range of their course experiences.

Describing a large set of observations with a single indicator risks distorting the original data or losing important detail. For example, the shooting percentage doesn't tell you whether the shots are three-pointers or lay-ups, and GPA doesn't tell you whether the student was in difficult or easy courses. Despite these limitations, descriptive statistics provide a powerful summary that may enable comparisons across people or other units.

Univariate analysis

Univariate analysis involves the examination across cases of a single variable, focusing on three characteristics: the distribution; the central tendency; and the dispersion. It is common to compute all three for each study variable.

Distribution

The distribution is a summary of the frequency of individual or ranges of values for a variable. The simplest distribution would list every value of a variable and the number of cases who had that value. For instance, computing the distribution of gender in the study population means computing the percentages that are male and female. The gender variable has only two, making it possible and meaningful to list each one. However, this does not work for a variable such as income that has many possible values. Typically, specific values are not particularly meaningful (income of 50,000 is typically not meaningfully different from 51,000). Grouping the raw scores using ranges of values reduces the number of categories to something for meaningful. For instance, we might group incomes into ranges of 0-10,000, 10,001-30,000, etc.

Frequency distributions are depicted as a table or as a graph. Table 1 shows an age frequency distribution with five categories of age ranges defined. The same frequency distribution can be depicted in a graph as shown in Figure 2. This type of graph is often referred to as a histogram or bar chart.

Central tendency

The central tendency of a distribution locates the "center" of a distribution of values. The three major types of estimates of central tendency are the mean, themedian, and themode.

The mean is the most commonly used method of describing central tendency. To compute the mean, take the sum of the values and divide by the count. For example, the mean quiz score is determined by summing all the scores and dividing by the number of students taking the exam. For example, consider the test score values:

15, 20, 21, 36, 15, 25, 15

The sum of these 7 values is 147, so the mean is 147/7 =21.

The median is the score found at the middle of the set of values, i.e., that has as many cases with a larger value as have a smaller value. One way to compute the median is to sort the values in numerical order, and then locate the value in the middle of the list. For example, if there are 500 values, the median is the average of the two values in 250th and 251st positions. If there are 501 values, the value in 250th position is the median. Sorting the 7 scores above produces:

15, 15, 15, 20, 21, 25, 36

There are 7 scores and score #4 represents the halfway point. The median is 20. If there are an even number of observations, then the median is the mean of the two middle scores. In the example, if there were an 8th observation, with a value of 25, the median becomes the average of the 4th and 5th scores, in this case 20.5.

The mode is the most frequently occurring value in the set. To determine the mode, compute the distribution as above. The mode is the value with the greatest frequency. In the example, the modal value 15, occurs three times. In some distributions there is a "tie" for the highest frequency, i.e., there are multiple modal values. These are called multi-modal distributions.

Notice that the three measures typically produce different results. The term "average" obscures the difference between them and is better avoided. The three values are equal if the distribution is perfectly "normal" (i.e., bell-shaped).

Dispersion

Dispersion is the spread of values around the central tendency. There are two common measures of dispersion, the range and the standard deviation. The range is simply the highest value minus the lowest value. In our example distribution, the high value is 36 and the low is 15, so the range is 36 − 15 = 21.

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