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1. The field of statistics can be roughly subdivided into two

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1. The field of statistics can be roughly subdivided into two areas: descriptive statistics and probability. ______

2. A company employs 950 individuals. To ascertain how the employees feel regarding a pension plan, 105 of the employees were surveyed. The proportion of the 105 employees who favor the pension plan is a parameter. ________

3. Eye color would be an example of qualitative data. ______

4. A population is typically a very large collection of individuals or objects about which we desire information. _______

5. A qualitative variable that categorizes or describes or names an element of a population is referred to as a nominal variable. _______

6. Consider the following data: the height in centimeters of children in a fourth-grade class. Which of the following best describes these data?
A) Attribute data B) Qualitative data C) Quantitative data D) Sample Data

7. A company has developed a new battery, but the average lifetime is unknown. In order to estimate the average, a sample of 150 batteries is tested and the average lifetime of this sample is found to be 250 hours. The 250 hours is the value of a:
A) parameter B) statistic C) population D) variable

8. Temperature is an example of what level of measurement?
A) nominal B) ordinal C) interval D) ratio

9. A sample that does not represent a population correctly is called
A) random B) clustered C) biased D) unbiased

10. Sampling that subdivides the population into subgroups with like characteristics is called
A) cluster B) random C) stratified D) sequence

Part II Short Answers/Computational Questions

1. Describe the difference between inferential and descriptive statistics

2. In completing a survey, subjects used the following numbers to indicate marital status
I = Single (never married) 2 = Married 3 = Divorced 4 = Widowed
Is this data qualitative or quantitative? Explain

3. Discuss the difference between a variable and a parameter. Include an illustration.

4. Explain the difference between probability and statistics. Include an illustration

5. Label each of the following as an observational or experimental study. For the experimental study state the control group and the treatment group.

a. A researcher finds that children whose parents smoke have more respiratory illness than children whose parents do not smoke have.
b. A teacher divides her class into two groups. One group is taught a lesson on the computer and the other is taught by traditional lecture. The teacher then gives a test to see which group has better grades.

6. Construct a frequency distribution for the data given below:

7. Contrast the nominal scale of measurement to the ordinal scale. In your opinion what type of scale is used in a list of the order of finish in a horse race? What type of scale is used if we had to describe the sex of the jockeys?

8. Describe briefly how an experimental study is conducted. What is the difference between the two types of variables used?

3. True
4. True
5. True
6. C) Quantitative data
7. B) statistic
8. C) interval
9. C) biased
10. C) stratified

Part II Short Answers/Computational Questions

1. Describe the difference between inferential and descriptive statistics
Descriptive statistics summarizes data to tell you about the sample that was collected. Examples include the sample average, standard deviation, etc. Inferential Statistics seeks to infer from a sample, the characteristics of a population. Examples include hypothesis testing and confidence intervals.

2. In completing a survey, subjects used the following numbers to indicate marital status
I = Single (never married) 2 = Married 3 = Divorced 4 = Widowed
Is this data qualitative or quantitative? Explain

This data is qualitative. While each answer is coded with a number, they still represent the names of particular attributes of the respondent. Further evidence of this is that the average response would be non-sensical.

3. Discuss the difference between a variable and a parameter. Include an illustration.
A variable is a random quantity that summarizes some attribute of a sample. An example might be the sample average. This value changes with each sample that is drawn. It is random and has its own probability distribution. A parameter is a fixed, often unknown quantity that represents the true state of reality with regard to a population. While the sample average is the variable, the corresponding parameter is the population mean.

4. Explain the difference between probability and statistics. Include an illustration
Statistics is the collecting, organizing, summarizing and interpreting of data in order to make decisions. Probability has to do with the likelihood of a particular event occurring. Probability theory is one tool used in statistics to analyze data and make decisions.

5. Label each of the following as an observational or experimental study. For the experimental study state the control group and the treatment group.

a. Observational Study.
b. Experimental Study. The control group is the class getting that traditional lecture. The computer group is the treatment group.

6. Construct a frequency distribution for the data given below:

7. Contrast the nominal scale of measurement to the ordinal scale. In your opinion what type of scale is used in a list of the order of finish in a horse race? What type of scale is used if we had to describe the sex of the jockeys?

A nominal scale uses names to describe an attributes. For nominal data, there is no intrinsic order to nominal data. Ordinal data still uses names to describe attributes, but the names are XXXXX XXXXX some way. The finishing position of a horse race is ordinal data. Win (1st place), Place (2nd place), and Show (3rd place) are names, but they have an order. Gender is unordered, so it follows a nominal scale.

8. Describe briefly how an experimental study is conducted. What is the difference between the two types of variables used?

The two types of variables in an experimental study are independent and dependent variable. An experimental study involves determining which variables to manipulate (these are the independent variables) and which variables to observe (these are the dependent variables). For example, a quality control expert might be interested in the effect of a new manufacturing process on quality. He would then set the levels of his dependent variables (i.e., treatment A, treatment B, etc) and observe the quality of his product (perhaps the hardness of the steel or the number of defects. By analyzing the way the dependent variable is correlated with the independent variable, the researcher can determine what effect if any the treatments have on the process.

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