Business Statistics HW#1

Which of the following variables is not an example of a quantitative variable?

A person's social security number A person's social security number is a qualitative variable; it is used as a label to identify a specific person

In inferential statistics, we calculate statistics of sample data to

Both answers choices are correct. Inferential statistics is concerned with estimating unknown population parameters and testing hypotheses about them.

A discrete variable cannot assume an infinite number of values.

False A discrete variable may assume an infinite number of values, but these values are countable; that is, they can be presented as a sequence x1, x2, x3, and so on. The number of cars that cross the Golden Gate Bridge on a Saturday is a discrete variable. Theoretically, this variable assumes the values 0, 1, 2, 3, ...

A qualitative variable assumes meaningful numerical values.

False A quantitative variable assumes meaningful numerical values, while values of a qualitative variable are typically labels or names used to identify the distinguishing characteristics of each observation.

Cross-sectional data refers to data collected by recording a characteristic of one subject over several time periods.

False Cross-sectional data contain values of a characteristic of many subjects at the same point or approximately the same point in time, or without regards to differences in time.

The mathematical operations of addition and subtraction can be performed on nominal data.

False If we are presented with nominal data, all we can do is categorize or group the data.

A professor's marital status (married, single), as well as his/her rank (assistant, associate, full), represents ordinal data.

False Professor's marital status is nominal and rank is ordinal. The categories for nominal data do not have any natural ordering, while such an ordering exists for ordinal data.

In most statistical applications, we use population parameters to estimate the corresponding unknown sample statistics.

False Sample statistics are used to estimate the corresponding population parameter.

The recorded body temperature of 100 patients participating in a research study is an example of time series data.

False The recorded body temperature of 100 patients participating in a research study is an example of cross-sectional data.

The zero point of an interval scale reflects a complete absence of what is being measured.

False The zero point of an interval scale does not reflect a complete absence of what is being measured; the value of zero is arbitrarily chosen. For example, no specific meaning is attached to zero degrees Fahrenheit other than to say it is 15 degrees colder than 15 degrees Fahrenheit.

A population is defined as all members of a specified group.

True A large set of data—called a population—is defined as all members of a specific group (not necessarily people).

The Fahrenheit scale for temperatures is an example of an interval scale.

True The Fahrenheit scale for temperatures is an example of an interval scale, since not only can we categorize and rank the data, we are also assured the difference between temperature values are meaningful.

A knowledge of statistics provides the necessary tools to differentiate between sound statistical conclusions and questionable conclusions drawn from incomplete data points or just misinformation.

True To make intelligent decisions we must understand statistics—the language of data.

The branch of statistical studies called descriptive statistics refers to the summary of important aspects of a data set in the form of charts, tables, and numerical measures.

True Descriptive statistics refers to the summary of important aspects of a data set.

Structured data tends to include numbers, dates, and groups of words and numbers called strings.

True Structure data generally refers to data that has a well-defined length and format. This type of data is not open to interpretation.

Population parameters are difficult, if not impossible, to calculate due to the following main reasons

both cost prohibitions on data collection and the infeasibility of collecting data on the entire population. Gathering population data can be very expensive and difficult if not impossible to obtain.