isds chapter 1

The nominal scale

-LEAST sophisticated level of measurement. - all we can do is categorize or group the data. -differ merely by name or label.

the interval scale

-can categorize and rank the data and assured that the differences between scale values are meaningful. -3rd strongest level of measurement

cross-sectional data v. time series data

Cross-sectional data contain values of a characteristic of many subjects at the same point or approximately the same point in time. Time series data contain values of a characteristic of a subject over time.

discrete variable

assumes a countable number of values. EX: number of children in a family or the number of points scored in a basketball game. but we will not observe 1.3 children or 92.5 scored points.

time series data

refers to data collected by recording a characteristic of a subject over several time periods.

qualitative variable

we use labels or names to identify the distinguishing characteristic of each observation. (descriptive)

statistics

methodology of extracting useful info from data sets

the ordinal scale

-2 strongest -both categorize and rank the data with respect to some characteristic or trait.

the ratio scale

-MOST powerful level of measurement -can categorize and rank the data and assured that the differences between scale values are meaningful. as well as a true zero point, which allows us to interpret the ratios of values.

the need for sampling

1. Obtaining information on the entire population is expensive. 2. It is impossible to examine every member of the population.

population v. sample

A population consists of all items of interest in a statistical problem. A sample is a subset of the population.

population

A population is defined as all members of a specified group (not necessarily people), whereas a sample is a subset of that particular population.

quantitative variable

A variable that assumes meaningful numerical values

cross-sectional data

refers to data collected by recording a characteristic of many subjects at the same point in time, or without regard to differences in time.

inferential statistics

refers to drawing conclusions about a large set of data—called a population—based on a smaller set of sample data.

descriptive statistics

refers to the summary of important aspects of a data set. This includes collecting data, organizing the data, and then presenting the data in the form of charts and tables.

sample

smaller set of data

statistic v. parameter

We analyze sample data and calculate a sample statistic to make inferences about the unknown population parameter.

variable

When a characteristic of interest differs in kind or degree among various observations,

continuous variable

is characterized by uncountable values within an interval. Weight, height, time, and investment return EX: an unlimited number of values occur between the weights of 100 and 101 pounds, such as 100.3, 100.625, 100.8342, and so on.