### 统计代写|Generalized linear model代考广义线性模型代写|Reflection Questions: Comprehension

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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 统计代写|Generalized linear model代考广义线性模型代写|Summary

Before conducting any statistical analyses, researchers must decide how to measure their variables. There is no obvious method of measuring many of the variables that interest social scientists. Therefore, researchers must give each variable an operationalization that permits them to collect numerical data.

The most common system for conceptualizing quantitative data was developed by Stevens (1946), who defined four levels of data, which are (in ascending order of complexity) nominal, ordinal, interval, and ratio-level data. Nominal data consist of mutually exclusive and exhaustive categories, which are then given an arbitrary number. Ordinal data have all of the qualities of nominal data, but the numbers in ordinal data also indicate rank order. Interval data are characterized by all the traits of nominal and ordinal data, but the spacing between numbers is equal across the entire length of the scale. Finally, ratio data are characterized by the presence of an absolute zero. This does not mean that a zero has been obtained in the data; it merely means that zero would indicate the total lack of whatever is being measured. Higher levels of data contain more information, although it is always possible to convert from one level of data to a lower level. It is not possible to convert data to a higher level than it was collected at.

It is important for us to recognize the level of data because, as Table $2.1$ indicates, there are certain mathematical procedures that require certain levels of data. For example, calculating an average requires interval or ratio data; but classifying sample members is possible for all four levels of data. Social scientists who ignore the level of their data risk producing meaningless results (like the mean gender in a sample) or distorted statistics. Using the wrong statistical methods for a level of data is considered an elementary error and a sign of flawed research.

Some researchers also classify their data as being continuous or discrete. Continuous data are data that have many possible values that span the entire length of a scale with no large gaps in possible scores. Discrete data can only have a limited number of possible values.

## 统计代写|Generalized linear model代考广义线性模型代写|Reflection Questions: Application

1. Classify the following variables into the correct level of data (nominal, ordinal, interval, or ratio):
b. Height, measured in centimeters
c. Reaction time
d. Kelvin temperature scale
e. Race/ethnicity
f. Native language
g. Military rank
h. Celsius temperature scale
i. College major
j. Movie content ratings (e.g., $\mathrm{G}, \mathrm{PG}, \mathrm{PG}-13, \mathrm{R}, \mathrm{NC}-17$ )
k. Personality type
2. Hours spent watching TV per week
$\mathrm{m}$. Percentage of games an athlete plays without receiving a penalty
n. Marital status (i.e., single, married, divorced, widowed)
o. Fahrenheit temperature scale.
3. Label each of the examples in question $6(a-o)$ as continuous or discrete data.
4. Kevin has collected data about the weight of people in his study. He couldn’t obtain their exact weight, and so he merely asked people to indicate whether they were “skinny” (labeled group 1) “average” (group 2), or “heavy” (group 3).
a. What level of data has Kevin collected?
b. Could Kevin convert his data to nominal level? Why or why not? If he can, how would he make this conversion?
c. Could Kevin convert his data to ratio level? Why or why not? If he can, how would he make this conversion?
5. At most universities the faculty are – in ascending seniority – adjunct (i.e., part-time) faculty, lecturers, assistant professors, associate professors, and full professors.
a. What level of data would this information be?
b. If a researcher instead collected the number of years that a faculty member has been teaching at the college level, what level of data would that be instead?
c. Of the answers to the two previous questions ( $9 \mathrm{a}$ and $9 \mathrm{~b})$, which level of data is more detailed?
6. What is the minimal level of data students must collect if they want to
a. classify subjects?

## 统计代写|Generalized linear model代考广义线性模型代写|SPSS

SPSS permits users to specify the level of data in the variable view. (See the Software Guide for Chapter 1 for information about variable view.) Figure $2.1$ shows five variables that have been entered into SPSS. Entering the name merely requires clicking a cell in the column labeled “Name” and typing in the variable name. In the column labeled “Type,” the default option is “Numeric,” which is used for data that are numbers. (Other options include “String” for text; dates; and time measurements.) The next two columns, “Width” and “Decimals” refer to the length of a variable (in terms of the number of digits). “Width” must be at least 1 digit, and “Decimals” must be a smaller number than the number entered into “Width.” In this example, the “Grade” variable has 5 digits, of which 3 are decimals and 1 (automatically) is the decimal point in the number. The “Label” column is a more detailed name that you can give a label. This is helpful if the variable name itself is too short or if the limits of SPSS’s “Name” column (e.g., no variables beginning with numbers, no spaces) are too constraining.

The “Values” column is very convenient for nominal and ordinal data. By clicking the cell, the user can tell SPSS what numbers correspond to the different category labels. An example of this appears in Figure 2.2. This window allows users to specify which numbers refer to the various groups within a variable. In Figure 2.2, Group 1 is for female subjects, and Group 2 is for male subjects. Clicking on “Missing” is similar, but it permits users to specify which numbers correspond to missing data. This tells SPSS to not include those numbers when performing statistical analyses so that the results are not distorted. The next two columns (labeled “Columns” and “Align”) are cosmetic; changing values in these columns will make the numbers in the data view appear differently, but will not change the data or how the computer uses them.

To change the level of data for a variable, you should use the column labeled “Measure.” Clicking a cell in this column generates a small drop-down menu with three options, “Nominal” (for nominal data), “Ordinal” (for ordinal data), and “Scale” (for interval and ratio data). Assigning a variable to the proper level of data requires selecting the appropriate option from the menu.

## 统计代写|Generalized linear model代考广义线性模型代写|Reflection Questions: Application

1. 将以下变量分类为正确的数据级别（名义、有序、区间或比率）
B. 高度，以厘米为单位
C. 反应时间
D. 开尔文温标
e. 种族/民族
f. 母语
g. 军衔
h. 摄氏温度标度
i。大学专业
j。电影内容分级（例如，G,磷G,磷G−13,R,ñC−17)
k. 性格类型
2. 每周看电视的时间
米. 运动员未受罚的比赛的百分比
n。婚姻状况（即单身、已婚、离婚、丧偶
）华氏温标。
3. 标记每个有问题的示例6(一种−这)作为连续或离散数据。
4. 凯文在他的研究中收集了有关人们体重的数据。他无法获得他们的确切体重，所以他只要求人们指出他们是“瘦”（标记为第 1 组）“平均”（第 2 组）还是“重”（第 3 组）。
一种。凯文收集了什么级别的数据？
湾。凯文能否将他的数据转换为名义水平？为什么或者为什么不？如果可以，他将如何进行这种转换？
C。凯文能否将他的数据转换为比率水平？为什么或者为什么不？如果可以，他将如何进行这种转换？
5. 在大多数大学中，教师是——按资历递增的——兼职（即兼职）教师、讲师、助理教授、副教授和正教授。
一种。这些信息将是什么级别的数据？
湾。如果研究人员收集的是一名教员在大学任教的年数，那将是什么级别的数据？
C。前两个问题的答案（9一种和9 b)，哪个级别的数据更详细？
6. 如果学生愿意，他们必须收集的最低数据水平是多少
？分类科目？
湾。把分数加起来？

## 统计代写|Generalized linear model代考广义线性模型代写|SPSS

SPSS 允许用户在变量视图中指定数据级别。（有关变量视图的信息，请参阅第 1 章的软件指南。） 图2.1显示已输入 SPSS 的五个变量。输入名称只需要单击标有“名称”的列中的单元格并输入变量名称。在标有“类型”的列中，默认选项是“数字”，用于数字数据。（其他选项包括文本的“字符串”；日期；和时间测量。）接下来的两列，“宽度”和“小数”是指变量的长度（以位数表示）。“宽度”必须至少为 1 位，“小数”必须小于“宽度”中输入的数字。在本例中，“Grade”变量有 5 位数字，其中 3 位是小数，1（自动）是数字中的小数点。“标签”列是一个更详细的名称，您可以给它一个标签。

“值”列对于名义和有序数据非常方便。通过单击单元格，用户可以告诉 SPSS 哪些数字对应于不同的类别标签。图 2.2 中显示了一个示例。此窗口允许用户指定哪些数字引用变量中的各个组。在图 2.2 中，第 1 组针对女性受试者，第 2 组针对男性受试者。单击“丢失”是类似的，但它允许用户指定哪些数字对应于丢失的数据。这告诉 SPSS 在执行统计分析时不要包含这些数字，以免结果失真。接下来的两列（标记为“列”和“对齐”）是装饰性的；更改这些列中的值将使数据视图中的数字显示不同，但不会更改数据或计算机使用它们的方式。

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## MATLAB代写

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